A security mechanism known as the NIDS is developed to maintain surveillance on network traffic and identify any suspicious or illegal activity. NIDS are vital in detecting and countering network attacks by analyzing data packets and traffic patterns [1]. Abuse detection is also referred to as rule-based intrusion detection [2]. DRL has numerous applications in the real world [3]. NIDS are key security defense technologies that monitor computer networks or systems for network-based threats or malicious attacks that might compromise the system’s functionality. A misuse-based NIDS system relies on many attack patterns for intrusion detection. However, this system is vulnerable to zero-day attacks and has a lengthy processing time. An anomaly-based IDS detects concealed attacks on computer systems by identifying atypical traffic patterns. This approach can be used to identify zero-day attacks, and the discovery findings can be stored in a database for future detection using signature-based methods. The approach will be implemented and will begin to train the agent and the environment using the DRL algorithm [4]. In recent years, several authors have adapted classical methodologies to the issue of network information security. While dealing with smaller data sets of lower dimension, Machine Learning (ML) algorithms can achieve better classification results, which are all characterized as learning algorithms.

Recently, a new algorithm or approach combining the advancement of Deep Learning (DL), called DRL, has been developed. The generator is taught to produce fresh data, while the discriminator model attempts to distinguish between genuine and generated data. The RL approach is also used to train the generator. However, this DL-based system is extremely vulnerable to adversaries and also has an obvious limitation [5,6]. As a branch of ML, ARL studies the behavior of several agents in a hostile or competitive environment. Its main focus is on building algorithms and techniques that enable agents to acquire and modify knowledge in contexts with other agents whose goals are at odds with their own. As a branch of ML, ARL studies the behavior of several agents in a hostile or competitive environment. Thus, Fig. 1 illustrates the core targets of ARL. Thus, Fig. 2 represents the Artificial Intelligence (AI) taxonomy, where RL is a subclass of ML. Thus, this is the first comprehensive survey considering the studies employing ARL-IDS.

Robustness: ARL aims to develop agents that can survive and thrive in the face of hostile methods and forces. This requires techniques or rules that are immune to flaw-based attacks.

Counteracting Strategies: ARL enables agents to anticipate & counter opposing strategies. They may need to predict their opponents’ actions, adjust their policies, and capitalize on their weaknesses.

Exploration and Exploitation: ARL algorithms should enable agents to devise effective strategies and leverage successful approaches against opponents, striking a balance between exploration and exploitation.

Stability and Convergence: ARL aims to maintain learning.

Algorithm stability and convergence in competitive environments. Algorithms that can converge to Nash equilibria or other acceptable solutions are needed.

Transferability: ARL algorithms might adapt to hazardous environments under ideal conditions. Because of this, agents may apply what they’ve learned in one situation to perform better in another, unknown one.

Real-World Applications: ARL aims to develop algorithms and strategies for addressing adversarial or competitive real-world scenarios. Gaming, cybersecurity, negotiation systems, and multi-agent simulations are examples of these applications.

Scope of Survey: ARL has surpassed ML methods since incorporating RL and AT. In ARL, the generator plays the role of the adversary against the discriminator, attempting to deceive it. Tasks that require the generation of realistic data samples, such as Generative Adversarial Networks (GANs), benefit from ARL [7]. The underlying concept behind ARL is to demonstrate that a generator can replicate real data effectively, while training a discriminator to distinguish between the two types of data. Additionally, ARL aims to improve the data quality by using AT to generate more representative samples. ARL also combines AT regarding RL to enhance the efficacy and data quality of the generative model.

This section provides a detailed illustration of the background. Were this section categorized as follows: A.

Reinforcement Learning (RL)

RL is an ML model that depends on an agent connecting with their environment to accomplish a goal. Thus, RL and ARL require separate but linked algorithms and formulas. Throughout RL, the agent learns to make choices by optimizing accumulated reward through interactions with the environment. At the same time, the ARL provides enemies that test the agent’s learning process, which is sometimes depicted as a two-player game. The opponent aims to limit the payoff, while the agent seeks to maximize it.

MDP and Bellman Equations for RL: RL relies on rewards and punishments, rather than supervised and unsupervised learning, which utilize labeled data for model training and pattern discovery. Adapting to rewards and punishments from the environment, the agent maximizes cumulative rewards over time. RL algorithms aim to make the best judgments in complex and uncertain scenarios. RL requires agents, environments, states, actions, rewards, and regulations. RL is used in robotics, gaming, autonomous automobiles, recommendation systems, and finance. Thus, RL defines automation as an agent interacting with an environment and learning through trial and error [8]. RL elements are described in Fig. 4. The following core elements of the Markov Decision Process (MDP) concept are discussed for their importance.

RL: The training of ML models to make a sequence of decisions.

Agent Function: May choose an action, receive a reward from the environment, and transition to a new state.

Reward Function: The feedback the agent receives based on the action it performed.

If the feedback is positive, it receives a reward.

The environment provides the agent with a state.

Bellman optimality equation for an MDP’s state-value function V(s). (1)V(s)=maxa(R(s,a)+ΥV(s′))

Value Function: The value function V(s) represents the expected cumulative reward starting from state s under a policy π: (2)Vπ(s)=Eπ[∑i=0∞γtr(st,at)|s0=s]

Action-Value Function (Q-Function): The action-value function Q (s, a) represents the expected cumulative reward starting from state (s), acting (a), and following policy π: (3)Qπ(s,a)=Eπ[∑i=0∞γtr(st,at)|s0=s,a0=a]

Bellman Equation for Value Function: The Bellman equation expresses the value of a state as the immediate reward plus the discounted value of the next state: (4)Vπ(s)=∑aεAπ(a|s)∑s′P(s′|s,a)[R(s,a)+γVπ(s′)]

Bellman Equation for Q-Function: Similarly, the Bellman equation for the Q-function is: (5)Qπ(s,a)=R(s,a)+γ∑s′P(s′|s,a)∑a′π(a′|s′)Qπ(s′,a′)

Optimal Policy (Bellman Optimality Equation): RL aims to maximize the cumulative reward: (6)V∗=maxa[R(s,a)+γ∑s′P(s′|s,a)V∗(s′)] (7)Q∗(s,a)=R(s,a)+γ∑s′P(s′|s,a)maxa′Q∗(s′,a′)

Policy Gradient (PG) (for Policy-Based Methods): In policy gradient methods, we aim to optimize the policy by adjusting the parameters θ. The gradient of the expected cumulative reward with respect to θ is: (8)∇θJ(θ)=E π[∇θlogπθ⁡(a|s)Qπ(s,a)]

Baseline RL for Intrusion Detection: In the RL framework, the IDS agent interacts with the environment (network traffic). The agent observes states. s∈S, takes actions a∈A \in, and receives rewards r∈R to optimize its detection policy π π(a|s). (9)Q(s,a)←Q(s,a)+α[r+γmaxa′Q(s′,a′)−Q(s,a)]

Additionally, the notation for the mathematical formulas of the MDP and the Bellman approach for the RL algorithm (as shown in Algorithm 1: MPD-RL Algorithm) is provided in Table 1.

Consequently, fields like DRL have made great strides, combining RL algorithms with DNN to tackle more difficult problems and provide SOTA outcomes. Regarding AI, RL remains a hotspot for study, and it shows promise in tackling tough challenges. Thus, DRL appears to be subject to adversarial attacks, which restrict its applicability in crucial real-world applications or systems. Furthermore, DRL can perform well in various situations with limited manual intervention. A key component in classifying RL algorithms is the agent’s ability to learn its surroundings, including functions that predict state transitions and rewards. The following categories of RL approaches are shown in Fig. 5.

Model-based RL: An agent learns a model of the environment and then utilizes it to anticipate and plan for the future. Agents change and restructure models periodically. How many data samples do agents require to maximize rewards and perform successfully in their environments? When model-based algorithms state sample efficiently, they imply this. This is because the agent does not need to see every result. Learning (world models), model-based value growth, model-based fine-tuning, or being given are methods to acquire a model.

Model-free RL: Instead of constructing or getting a model, an agent uses its knowledge to select optimal behaviors in a state. Model-free algorithms learn state values, not policies. The agent’s main goal is to behave optimally. Therefore, the agent learns by exploring rather than exploiting its environment. It covers policy optimization and Q-Learning algorithms. Policy-based and value-based model-free methods exist. Some refer to policy-based or PG methods as on-policy. In policy optimization, algorithms optimize parameters θ directly by gradient ascent on the performance object, such as the cost function, or indirectly by maximizing a local estimate. The primary objective is to select a PG that rewards good acts and penalizes negative ones. PG can learn an acceptable policy even when the Q-function is too difficult. It converges quickly and can learn stochastic rules. Furthermore, continuous spaces are simpler to model. Policy-based techniques aim to benefit from learning an acceptable policy even when the Q-function is too difficult to learn. It converges quickly and can learn stochastic rules. Furthermore, continuous spaces are simpler to model. Policy-based techniques aim to maximize a performance metric, such as the parametrized policy’s true value function, over all starting states [9]. B.

Adversarial Reinforcement Learning (ARL)

ARL combines AT and RL, both strong ML paradigms designed to solve problems in dynamic and hostile settings. AT, which stems from the discipline of adversarial ML, entails exposing models to adversarial cases during training to improve their resilience to possible assaults. In contrast, RL is concerned with agents engaging in an environment to learn optimal methods via trial and error, thereby maximizing cumulative rewards over time. ARL combines these two approaches, enabling agents to acquire resilient rules for making informed choices in uncertain and hostile environments. ARL combines AT’s combative resistance with RL’s decision-making skills and offers new pathways for handling difficult real-world challenges in various disciplines, including cybersecurity and ML [10]. A powerful DRL method, ARL incorporates the DQN model for IDS and is specially designed for Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Findings show that DRL significantly improves detection accuracy compared to traditional ML methods like K-Nearest Neighbors (KNN) and Q-learning. The ARL framework can dynamically identify threats in hostile situations surrounding vital public infrastructure by constantly learning and adapting. ARL enhances the total security posture during NIDS in complex and evolving threat environments, utilizing the actor as an IDS instrument and mood as packets from the network to improve classification judgments and accuracy [11]. ARL Taxonomy is provided via Fig. 6, where ARL is classified into model adjustment, application domain, technique, framework feature, and adaptability.

During ARL, adversarial attacks manipulate the incentive system to have the agent make poor choices. The agent may adopt rude habits or fail to fulfill its goals due to these assaults. Adversarial attacks in ARL are challenging to identify and counteract, necessitating robust defenses to ensure agent performance and reliability. These attacks can lead to improper agent behavior or prevent it from achieving its goals. Adversarial attacks in RL are often difficult to detect and require robust defense approaches to ensure agent performance and reliability. However, Fig. 7 categorizes the taxonomy of adversarial attacks. Furthermore, Table 2 outlines the differences between RL, DRL, and ARL.

C.

Intrusion Detection System (IDS)

IDS is a crucial component of today’s cybersecurity techniques. IDS is generally a software or hardware technology that observes network and system activity to detect vulnerabilities and respond to unauthorized use, criminal activity, and possible security breaches. An IDS is used similarly to a firewall, serving as a watchful guardian that provides real-time alerts or implements automated actions to secure systems containing sensitive data. It analyzes recurring activity patterns and compares them with known malicious activity signatures or unusual deviations, as shown in Fig. 8. IDS plays a crucial role in proactively identifying and removing cyber threats, thereby enhancing the robustness and security of technological systems, whether within an organization’s internal networks or beyond its perimeter. Thus, NIDS aim to accurately detect and classify complex attacks in real-time [12]. There are various varieties of IDS; consider Fig. 9 to demonstrate IDS taxonomy, detection approach, detection methodology, architecture, response method, analysis target, and analysis time.

NIDS are crucial for detecting and preventing network security threats. Furthermore, NIDS is the initial step in building security status awareness since it is the primary technology for detecting various network attacks and analyzing network data. NIDS can detect patterns or signatures already existing in the training dataset. To meet the categorization standards, it must be updated in real-time. The basic goal of NIDS is to monitor and control network connections while blocking unauthorized ones. Fig. 10 illustrates the NIDS architecture, which is intended to protect data and systems. As a result, it may be used to determine whether an NIDS occurred.

Hybrid-based IDS (Hybrid-IDS): Hybrid-IDS integrates various detection techniques, such as signature-based, anomaly-based, and behavior-based methods, to provide comprehensive security measures. By combining these approaches, Hybrid-IDS can effectively detect and mitigate a wide range of cyber threats, providing a more robust defense mechanism than a single-method IDS. Leveraging ML and DL algorithms, Hybrid-IDS can analyze complex patterns and behaviors in network traffic, enhancing threat detection accuracy while minimizing FP. Hybrid-IDS’s continuous evolution and refinement ensure their ability to adapt to the ever-changing landscape of cybersecurity threats, making them an asset in safeguarding networks and systems from malicious activities [13].

Anomaly Detection (AD): AD is a critical aspect of cybersecurity that involves identifying unusual or suspicious behavior in a system. It is vital for detecting security threats, such as network intrusions, fraud, or system malfunctions. By leveraging ML and DL techniques, AD systems can analyze vast amounts of data to establish patterns of normal behavior and flag deviations that may indicate anomalies. These systems are designed to continuously adapt and learn from new data, thereby enhancing their ability to detect. AD is essential for proactively identifying and mitigating security risks in various domains, including network security, financial transactions, and industrial processes [15]. Moreover, Table 3 illustrates the key differences between the various types of IDS. Finally, IDS seeks to categorize hostile network events in real-time, learn from past experiences, eliminate mistakes, and strengthen network defenses against attacks. It removes the need for humans to develop criteria and indicators to detect and prevent attacks. RL can educate IDS systems on how to respond properly to incentives and penalties [16].

The following section discusses previous ARL-based NIDS studies and how they have identified attacks and malicious behavior in various capacities, employing different methodologies and strategies. In 1999, ML was conceived and used for intrusion detection. “Wenke Lee” led his team in developing an AD model to assess the network flow of data via log auditing, setting the framework for future ML advancements in IDS. Since the main concept is addressed in 154 related studies that utilize the ARL technique to enhance current NIDS, which are developed using similar technology, Table 4 presents a comparison between relevant studies from the past few years, including contributions, datasets, models, and results. Furthermore, Fig. 11 illustrates the latest advancement of ARL over the last 11 years. Researchers have recently developed and enhanced ARL for use in various environments and applications.

To start with, the ARL-NIDS comparative method segment, ARL is a branch of RL that could address varied conditions in which the agent encounters adversaries or opponents actively seeking to obstruct its development or take advantage of weaknesses, in addition to boundaries in its decision-making framework. The aim is to research robust policies that can withstand antagonistic assaults or disturbances. The following section outlines the conceptual framework of ARL-NIDS for detecting malicious and unusual activities. Furthermore, ARL-NIDS may appoint RL models based on the detection of malicious and peculiar activities. Moreover, ARL-NIDS involves various key steps to ensure its effectiveness and robustness in detecting and mitigating cyber threats. Thus, Algorithm 2 illustrates the overall ARL-NIDS algorithm. Additionally, Algorithm 2 guides the construction of an ARL-NIDS framework that can enhance community defenses. ARL-NIDS, the generalized structure, is represented in Fig. 12.

Fig. 12 illustrates the process of selecting data for the ARL-NIDS model, which enhances NIDS by training it to identify and respond to attacks in network traffic. The NIDS agent finds unusual activities, and the reward function checks the agent’s actions to label them as either normal or an attack. This feedback trains and evaluates the NIDS agents to enhance security detection. We have twelve evaluation metrics to measure performance, including accuracy, F1-score, recall, FPR, True Negative Rate (TNR), log loss, precision, ROC-AUC, PR-AUC, confusion matrix, Matthews Correlation Coefficient (MCC), and Cohen’s Kappa. We optimize results through epochs of training rounds, during which the model learns from its experiences. ARL aims to achieve the best behavior in decision-making tasks.

To consider the hostile character of the environment via ARL, while an opponent influences the environment, it might be necessary to adapt the Bellman equation. However, the approaches for overtly or implicitly modeling the adversary’s activities, among other aspects associated with identifying ARL issues, may dictate how the Bellman equation remains adjusted. Fig. 13 illustrates ARL-NIDS, which involves unfavorable threats and defenses, where the system is trained on network traffic and evaluated against various adverse dangers. Attackers can try poisoning, theft, or extraction, which is countered through poison detection, negative training, and verification. This framework process remains flexible by maintaining a reliable classification performance during the input, RL process, and attacks of the IDs. Furthermore, Fig. 14 illustrates a conceptual framework for ARL-NIDS. It integrates adversarial components, namely the Adversarial Actor and Adversarial Critic, with an agent responsible for packet classification.

The opponent manipulates the agent’s input or reward signal to create ideal yet challenging circumstances, thereby enhancing the agent’s flexibility in responding to complex attacks. The agent interacts with the environment by classifying network packets and assessing its detection accuracy, which adversarial critics then evaluate. This cyclical engagement fosters adaptive learning and flexibility in intrusion detection.

Q-Learning Update with Adversarial Perturbation: The Formula (11) extends RL to adversarial IDS by modifying Q-learning updates under adversarial settings. This equation also updates the RL action-value function. It calculates the cumulative payoff for the activity at in state st, considering environmental adversaries. The agent critic and network environment loop in the ARL-NIDS Fig. 14 refines the agent’s policy after analyzing incentives and future states [28]. (10)Q(st,at)←Q(st,at)+α[rt+γmaxa′Q(st+1,a′)−Q(st,at)]

Adversarial Perturbation of States: Formula (12) illustrates how the attacker disrupts the state to deceive the IDS. Adversarial actors create δ, which alters observable traffic properties before the data reaches the agent. This is evident in the picture, where Adversarial Actors move into the Network Environment, injecting adversarial samples [34]. (11)St′=st+δ′δ∼A(st)

Policy Gradient with Advantage Function: Actor update rule in an actor-critic method. The agent adjusts its policy parameters to enhance action selection in the face of hostile influence. θ. The advantage function A(st,at) Helps the agent find better-than-average actions. Agent actors learn from agent critic comments, as seen in Fig. 14. (12)θ←θ+α∇θlog⁡πθ(at|st)A(st,at)

Adversarial Reward Shaping: The reward function is reshaped by adversarial influence, where C(st,at) Represents the cost induced by adversarial attacks (e.g., false negatives, evasion success). The parameter λ Balances standard detection rewards with adversarial penalties. Fig. 14 corresponds to the Adversarial Critic, which evaluates the success of the attack against the IDS [34]. (13)rt′=rt−λ.C(st,at)

Table 5 provides a summary of the key components of ARL-NIDS, mapping each component to its corresponding formula and components. To clarify the significance of each term used in the formulas, Table 6 explains each term mentioned.

Table 7 examines the most recent studies that utilize diverse AI models, including RL, ML, and DRL, in various environments. Thus, it summarizes the most common models, environments, and IDS. In addition, the table also explores the latest dataset employed and the novel techniques. Lastly, the attacks detected in previous studies are listed in the table.

In short, the methodology and recent advancements demonstrate the growing integration of reinforcement learning with AD in next-generation networked environments. Research authors [76] employ DDQN for AD in Cyber-Physical Systems (CPS), emphasizing RL’s importance in managing complex and dynamic conditions. Moreover, the authors in [77] apply DRL to business process automation, highlighting data efficiency and weak supervision as essential design factors. Similarly, a privacy-enhanced DRL-based IDS tailored for CPS, which balances detection accuracy with confidentiality, was developed in [78]. Thus, in [79], to balance an imbalanced dataset, the Synthetic Minority Over-sampling Technique (SMOTE) algorithm is employed to enhance detection fidelity. Regarding the RL framework that integrates oversampling and undersampling strategies to address dataset imbalance issues common in intrusion detection, a proposal is made. However, to balance an imbalanced dataset, the Synthetic Minority Over-sampling Technique (SMOTE) algorithm must be employed before training models. Collectively, these works reinforce that NIDS are an appropriate application area for ARL, as they must operate in adversarial settings, adapt to evolving threats, and ensure robust detection across diverse and imbalanced datasets. This positions ARL-NIDS as a promising methodology for achieving adaptive, resilient, and intelligent cybersecurity defenses.

To begin with, the comparative analysis and discussion section illustrates the results of this unique survey study. This research focused on the ARL-NIDS approach to detecting malicious and abnormal activities, which will be discussed within the current section. The objective is to synthesize earlier research, highlight methodological variations, and assess algorithms, datasets, and settings across the literature. Analysis of the latest applications, dataset selection, ARL within various algorithms, extensions, datasets, cybersecurity risks, security challenges, overall comparison, and analysis and discussion. This current section delves into the following: A.

ARL-NIDS Applications

The increasing complexity of cyber threats in modern digital ecosystems has catalyzed the development of intelligent ID mechanisms. ARL has emerged as a promising paradigm that enables adaptive and flexible NIDS. By integrating side effects that mimic malicious behavior, the ARL framework enhances the model’s detection capabilities in response to evolving attacks. Recently, literature reflected a rejection in various domains, including CPS, IIoT, smart health services, and web safety. These systems benefit from the dynamic learning of optimal defense strategies for DRL architecture, improve the accuracy of nonconformities, and treat adverse disorders. Recent progress in ARL-NIDS has shown its versatility and efficiency in addressing real-world challenges. For instance, ref. [76] proposed a DDQN framework for ED in the next generation of CPS, demonstrating increased adaptability in dynamic environments. Business process AD has also benefited from ARL, as demonstrated by [77], who employed weakly supervised learning to improve data efficiency. Similarly, an ARL model introduced privacy protection within [78] ID, addressing data privacy, a significant concern in sensitive infrastructure, while Ref. [79] focused on convenience choice using DRL to adapt performance for detection. Healthcare and vehicular systems are emerging frontiers for ARL-NIDS. The RL for NIDS utilizes the CSE-CIC-IDS2018 and NSL-KDD datasets, as discussed in [80]. Whereas ref. [81] addressed the coexistence of anomalies in intelligent connected vehicles using RL-based data validity analysis, regarding the oversampling and undersampling discussed to balance unbalanced datasets via RL and DRL. Regarding UAVs and vehicles, for data validity in RL and DRL, as mentioned in [82], the authors define a driving style that’s appropriate for a quantitative model. In another context, both studies [83,84] employ RL and DRL in cybersecurity and network security to counter adversarial attack simulations. For IoT environments, it is vital to implement robust cybersecurity frameworks that can help mitigate cyberattacks within IoT layers. However, scientists in [85,86] explore IDS and botnets for RL within the IoT environment. In industrial contexts, the study used [87] the dynamic reward mechanism and the main component analysis to detect IIOT attacks. Thus, in [88], an adaptive RL model for secure routing in smart healthcare is developed. These applications underscore the growing significance of ARL in safeguarding heterogeneous and mission-critical systems. The study [89] seeks to leverage DRL in conjunction with IDS within an IoT context to detect evolving threats and enhance the complexity of detection. Wherein [90], authors introduce a novel approach for computing a policy for prioritizing alerts using ARL. Due to multi-agent RL, authors in [91,92] employ minor and major RL for IDS to enhance both centralized and decentralized approaches based on the NSL-KDD dataset. Regarding the hybrid approach and multi-class network, DRL is employed within NIDS in [93]. Also, authors train GANs with the NSL-KDD dataset. Furthermore, the multi-agent adversarial detection is employed via TL in [94]. Also, the NIDS was employed to detect anomalies against evasion attacks. However, Table 8 provides an overview of the latest applications of ARL-NIDS, including brief details.

B.

ARL-NIDS within Various Models

Depending on the ARL algorithm, the sub-section compares ARL with other algorithms and approaches. Table 9 explores the ARL comparison with existing algorithms/models. RL and side effects are used to identify complex and dynamic cyber threats, such as ARL-NIDS and advanced network security approaches. It contradicts traditional IDS models, signature-based IDS, and traditional ML approaches such as Decision Tree (DT), SVM, and Random Forests (RF). However, ARL-NIDS dynamically adjusts the optimal defense strategies through interactions with the network environment, utilizes multi-agent DRL for cooperative advertising, and optimizes the defense strategies in response to unfortunate attacks. Latest research has advanced ARL-NIDS by incorporating hybrid models that integrate methodologies such as GANs for data augmentation and TL for multi-adversarial detection. The mentioned methodologies enable ARL-NIDS to proactively detect abnormalities in complex network contexts, including IoT, IIoT, and CPS, even in the presence of advanced evasion or stealth attacks [95]. ARL-NIDS offers a substantial advantage in terms of flexibility, robustness, and predictive performance compared to traditional IDS frameworks [96]. Traditional monitored models rely on labeled datasets and often struggle against new attacks. ARL-NIDs continuously refine their guidelines through RL, enabling them to predict and counteract the dangers that are already unacceptable [97]. However, the Hybrid ARL-NIDS architecture intersects with GAN or multi-agent collaboration traditional DL-based NIDs in detecting stolen attacks [93,98]. However, sophisticated systems often require multiple processing resources and careful adjustments to reward features, ensuring stability and preventing overheating. Despite these obstacles, ARL-NIDs demonstrate increased flexibility in enemy contexts compared to traditional ML models, providing an active security solution rather than a reactive one. Furthermore, the training time and policy teaching for adversarial attacks against RL algorithms, as presented in [99]. In [100], authors employ RL for an autonomous defense approach for software-defined networks.

C.

ARL-NIDS Extensions

ARL-NIDS has demonstrated significant dynamic detection capabilities, reducing the incidence of cyberattacks in network environments. However, as with sophisticated AI systems, they are receptive to new attack strategies and operational challenges. Recent research has focused on expanding ARL-NIDS to enhance its strength, adaptability, and overall efficiency in real-world applications. These extensions benefit from insights into unfavorable attacks, AD, social robotics, and moving goal defense to improve the ARL system’s identity ability and flexibility [101]. Fig. 15 identifies the ARL extensions used to acquire knowledge & improve efficiency. Various approaches have been proposed to extend the classical ARL-NIDS framework. Malicious attack characterization and optimal adversarial approaches allow ARL models to anticipate and defend against sophisticated threats, improving the stability and convergence of RL policies [102]. Recent extensions of ARL have significantly strengthened its theoretical foundations and expanded its applicability in security-critical domains. During [103], the authors formalized optimal attack strategies against RL policies, exposing vulnerabilities in well-trained agents by leveraging policy structures. However, in [104], snooping attacks are introduced, in which adversaries deduce the internal states or decision patterns of DRL agents, highlighting the dangers of information leakage during deployment. Thus, robust ARL’s stability and convergence characteristics in linear quadratic systems provide the mathematical assurances necessary for secure incorporation into control systems, as examined in the study [105].

DRL overviews outline strategies for enhancing training effectiveness and making policies more generalizable in dynamic network settings [106]. Beyond that, various technical insights offer a detailed look at AI-driven cybersecurity policies, demonstrating how ARL can be employed to develop initiative-taking defense systems in dynamic threat environments [107]. All these works extend ARL beyond simply reacting to obstacles. They also transfer toward strategic foresight, system-level resilience, and formal assurance. Moreover, research has investigated the integration of ARL with social robotics methodologies, enabling systems to collectively acquire knowledge from various agents and environmental feedback, thereby enhancing AD in complex network architectures [108]. Deep Sarsa and hybrid approaches are indicative of DRL algorithms that can be applied in the real world to detect network problems, even when attackers attempt to conceal them [109,110].

When used in conjunction with ARL, Moving Target Defense (MTD) aims to make it challenging for intruders to access a network, thereby complicating their ability to navigate it [111]. Research has identified the open security weaknesses and potential challenges associated with RL training, emphasizing the requirement for flexible side effects [110,112]. ARL enhances conventional RL by incorporating adversarial dynamics that simulate hostile or uncertain environments. This makes the system more robust and adaptable. In reference [113], the authors analyze cascaded fuzzy reward methodologies for robotic path planning in textiles. ARL could improve frameworks by training agents to operate under adversarial perturbations or dynamic constraints, thereby augmenting resilience in practical applications. Likewise, the research in [114] demonstrates how RL can mitigate DoS attacks in smart grids. By treating attackers as adversarial agents, the ARL approach may enhance this phenomenon. This informs defenders on how to respond when threats evolve. However, a DRL is employed through various functions of IDS in [115]. Furthermore, the train DRL utilizing the NSL-KDD dataset further evaluates the performance with several adversarial attacks. These add-ons make ARL a valuable tool for creating intelligent systems that function effectively, are secure, and can effectively manage issues even in the presence of threats. The ARL-NIDS extensions aim to enhance flexibility, resilience, and operational effectiveness in response to advanced cyber threats. By integrating unfavorable training, approaches for detecting collaboration with various agents, robust target rescue, and refined deviations, ARL-NIDS can achieve a height of active danger restriction. These extensions strengthen system performance and provide a framework for future research on AI-driven cybersecurity solutions.

D.

ARL-NIDS Datasets

Regarding the widespread improvements of IDS, which potentially impact the growth of RL algorithms in the cybersecurity sector. Traditional IDS strategies often struggle to manage the evolving and adaptive characteristics of contemporary cyberattacks. DRL is a promising approach to enhancing the IDS because it can continually learn and adapt. In [116], the author examines how side effects can compromise DRL-based IDS agents by introducing micro-disorders in the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) network traffic. Their findings highlight the fragility of the DRL model when exposed to unfortunate examples and emphasize the need for a dataset that includes such clear patterns to evaluate the strength of ID. In contrast, the study [117] proposes an active defense mechanism that utilizes the Arl-promoted honeypot system integrated into the industrial control network. Their model simulates unfavorable conditions within an MDP structure, enabling the agent to learn the behavior of complex attacks and enhance accuracy against DDO’s variants, such as NetBIOS and LDAP. The system performs well on unbalanced data sets, suggesting that synthetic and unfavorably rich data is required to train and validate such models. However, DRL-based IDS is highly efficient and heavily depends on the quality and properties of the dataset used for training and evaluation. Side effects for AD and NIDS, as well as recent studies in DRL, have emphasized the significant role of diverse datasets in evaluating the strength and adaptability of the algorithm.

Studies, such as the one by [118], stressed the importance of a large-scale, diverse dataset for benchmarking ARL models in real-world situations. Both the data and the traffic that need to be analyzed are crucial to the significance of this subsection. Table 10 lists and compares the most recent datasets used, along with various basic parameters, such as the type of traffic, the nature of the data, the tasks associated with them, and additional information, to enhance the utilization of datasets.

Wherein [119], authors develop a DRL-NIDS system based on MDP to enhance the robustness of DRL-IDS for stochastic games via employing the NSL-KDD dataset. Furthermore, the study [120] explored FPGA-accelerated decentralized RL in UAV networks, where customized UAV traffic datasets were crucial for validating AD. Additionally, contributors in [121] utilize multi-attribute monitoring datasets to evaluate unsupervised, reward-based AD.

Moreover, the study [122] explores the DRL model for the edge computing paradigm, based on IDS packet sampling within network traffic, utilizing two datasets: UNSW-NB15 and CIC-IDS-2017. Furthermore, a lightweight IDS for UAV simulation in [123] based on DRL has been developed to create a robust system for preventing and protecting UAVs and IoT devices from malicious activities, as well as cyberattacks. In another reference, authors via [124] examine adversarial ML for cybersecurity resilience and network security enhancement to identify security and network challenges, gaps, and limitations. However, the research findings suggest that future adversarial ML processes should utilize various robust algorithms to enhance the accuracy and effectiveness of the model.

Regarding Software-Defined Networks (SDNs), the study [125] demonstrated the importance of traffic sampling datasets for scaling ARL models. However, broader perspectives on hierarchical RL [126,127] have further revealed dataset-driven exploration of intrinsic options in multi-layered environments. Beyond networking, ARL has been applied in financial systems [128] and robotics in [129], which relied heavily on simulated and real-world datasets to ensure adversarial robustness. Recent studies, such as [130], that examine backdoor detection using DRL, emphasize the ease with which datasets can be poisoned. On the other hand, classic studies, such as those in [131,132], emphasize the importance of utilizing cybersecurity-specific simulation datasets for the validation of adversarial RL. In summary, these studies suggest that the choice and variety of datasets remain crucial for the development of ARL-based NIDS, facilitating a smoother transition from controlled settings to real-world use. Thus, the study [133] explores the GAN user model based on RL and a recommendation system. At the same time, biometric authentication is proposed on [134] for a mobile environment. Thus, the study [135] discusses the significant role of biometric verification systems, strengths, limitations, weaknesses, and mitigation strategies. Although a domain ARL is discussed based on MDP and Zero-Shot RL to train across different visual backgrounds. The primary goal of the study is to improve the generalization of DRL agents.

In short, the datasets referenced within this survey study, including NSL-KDD, CICIDS2017, UNSW-NB15, and IoTID20, are among the most extensively utilized benchmarks in the NIDS and RL literature [117,121,136]. Their adoption across various studies underscores the representativeness of ARL-NIDS models in evaluating them under controlled experimental conditions. However, these datasets also represent that multiple limitations influence ARL performance. For instance, NSL-KDD and UNSW-NB15 often exhibit data imbalance and outdated attack profiles, leading to biased learning in adversarial environments. Similarly, CICIDS2017 and IoTID20 offer more modern and diversified network traffic, but their limited real-time adaptability constrains the transferability of ARL models to dynamic IoT or edge computing scenarios. Recognizing these limitations is critical to ensure the robustness and generalization of ARL-NIDS frameworks. Hence, future research should focus on developing hybrid datasets that integrate synthetic adversarial samples with real-world network traffic to enhance the robustness and adaptability of evaluation models across heterogeneous cybersecurity environments.

E.

ARL-NIDS Risks

ARL has recently emerged as a powerful technique to improve the adaptability and intelligence of the NIDs. ARL enhances AD by enabling agents from the dynamic environment, improving traffic analysis, and adaptive defense strategies. To demonstrate how adversarial learning might compromise cybersecurity resilience by corrupting policies and manipulating environments in [124]. This research’s findings highlight the need for employing AT, reward shaping, and uncertainty modeling as robust defensive mechanisms in ARL. This will help ensure that agents behave securely and reliably in real-world deployment situations. However, the integration also exposes NIDs to develop novel cybersecurity risks such as unfavorable attacks, system weaknesses, and cyber threats. Table 11 summarizes the latest cybersecurity risks in ARL-NIDS. These risks challenge identity efficiency and question the strength, scalability, and reliability of ARL-NIDs in the deployment of the real world [134]. The integration of ARL into NIDS introduces significant risks that stem from negative mobility, system complexity, and a mature cyber environment. An important anxiety domain is the vulnerability of the ARL model for optimization risk, where unstable reinforcement learning can lead to unstable guidelines and domain transfer errors [135]. Additionally, the study [136] highlights the vulnerability of IoT networks to sophisticated cyber threats due to their inherent limitations and limited computational resources. Using ELM, the author shows increased ID accuracy in high-dimensional and unbalanced datasets. Research on IoT emphasizes the need for lightweight, scalable detection models to mitigate the risk of unseen infiltration in the IoT environment and to minimize real-time hazards. In addition, ARL-based systems face challenges from adaptive cyber-attacks that exploit the weaknesses of RL-operated reactions, making them susceptible to reward manipulation and misleading political updates [137]. However, Fig. 16 shows the cybersecurity triad (CIA), which identifies three main aspects (confidentiality, integrity, and availability).

Vulnerabilities are flaws or weaknesses in a system’s design, implementation, or configuration that could be exploited. It is passive; it does not cause harm on its own, but it opens the door. ARL-NIDS systems are vulnerable to issues related to model interpretability, scalability, and resource consumption. Limitations in explainability hinder the justification of judgments; however, decentralized or distributed ARL presents synchronization issues and increased processing costs [135].

Threats: A threat is any potential cause of harm that can exploit a vulnerability. It can occur due to human intervention (such as hacking), natural causes (like earthquakes), or random events (like a misunderstanding). ARL-NIDS systems face external dangers such as zero-day attacks, botnets, and insider threats. The dynamic training process can also be manipulated to induce biased learning or introduce poisoned data streams, rendering the agent unable to normalize [121].

Attacks: An attack refers to the actual action taken by a malicious actor to exploit a vulnerability. It is conscious and active, aligning with the principles of privacy, integrity, and availability. ARL agents are unsafe for learning attacks for unfavorable reinforcement, including: (1) Examination: such as misleading the agent during training, (2) Exploration: that misleads the agent during training, (3) Evasion: where adversaries craft packets or flows to bypass detection, (4) Poisoning: corrupt training datasets or reward signals, and (5) Backdoor: that embeds hidden malicious policies [138].

This survey study contributes to the understanding of ARL in cybersecurity by examining ARL-NIDS within the IoT environment, as highlighted by various researchers in related past studies. Assorted studies attempted to mitigate adversarial reward manipulation by introducing adaptive or hierarchical reward functions to stabilize learning dynamics under adversarial perturbations [111,129]. However, others ignored the issue, assuming static environments that do not reflect dynamic attack surfaces. The transferability challenge of how ARL models trained on one dataset generalize to another was only partially explored, primarily in simulation-based research with limited real-world validation [135]. Likewise, few studies have analyzed the robust trade-offs between model adaptability and computational complexity, despite their importance in large-scale or resource-constrained systems, such as IoT and UAV networks [123,139]. This survey identifies these gaps as critical future research directions, encouraging empirical benchmarking of ARL models across heterogeneous datasets and environments.

However, ref. [139] proposed an AI-competent adaptive cybersecurity system that suggested provoking RL to respond to new threats in real-time. Research indicates the limitations of static rule-based systems in managing zero-day attacks and describes agents that learn through reinforcement to achieve effective security methods. The most significant threat addressed is that the old systems are slow and inflexible, which means they may not quickly adapt to new attack vectors. In addition, ref. [140] intensive examination of ML methods to protect the blockchain network, which postpones the dangers associated with manipulation in consensus, weaknesses in smart contracts, and data integrity breaches. Research examines DL, a Simbel approach, and AD to enhance risk and resistance, particularly in relation to the side effects and internal threats of decentralized systems.

In addition, investigations in [141] examine the surface of ARL systems, which explains how unfavorable ML can compromise model integrity through theft, poisoning, and invasion attacks. Authors emphasize the risk of higher dependence on opaque ML models in critical training infrastructure and for strong counterparts, as AI can secretly reduce exploitation. In another context of reviewing RL applications in cybersecurity [142], the author identifies significant risks associated with penetration tests, IDS, and malware reactions. They also insist on the challenge of trial efficiency and strength of the model in an adverse environment, suggesting the TRL-based system should be carefully set to avoid utilizing adaptive threats. In addition, the study [143] DRL uses an unfavorable cyber-attack simulation and explains how RL agents can learn optimal attack strategies against network defense. The research exposes the risk of intelligent adversaries exploiting system weaknesses through learned behaviors and proposes RL-based defensive agents to counteract these evolving threats.

During [144], a framework was introduced for adaptive RL for automated incident response, targeting the risk of delayed or ineffective reactions to cyber incidents, by modeling the response strategies such as RL problems, the system detection and scalability accuracy in the dynamic danger scenario improves, while addressing the important requirement for the autonomous defense mechanisms, which reduces FP. Another article [145] discovered DRLs to detect advanced risk, focusing on the risk of negative theft in harmful software and infiltration scenarios. The author demonstrates how DRL can enhance the accuracy and flexibility of detection, while also mitigating overfitting and unfavorable manipulation, which can compromise the system’s reliability under real-world conditions. However, they suggest [146] a hybrid security structure to ensure web applications for blockchain are adaptable and sufficient for learning. The study addresses risks such as SQL injection attacks, model poisoning, and IoT falsification. It demonstrates how decentralized consensus and unwanted training systems can jointly mitigate these risks while maintaining transparency and accountability.

Although ARL remains effective in enhancing decision-making and flexibility, it is nevertheless quite susceptible to a variety of complex attacks that target the operational and training phases. Within the most common types of attacks is poisoning, in which the adversary attempts to damage or harm the policy optimization process by manipulating the learning environment or the reward function. In secure systems, such as autonomous vehicles and cybersecurity frameworks, these manipulations have the potential to alter agent learning trajectories and weaken the robustness of policies. Another risk to ARL models is an evasion attack, which tricks the trained agent into generating bad, poor decisions without altering the environment by subtly modifying input states or observations during testing. The agent may also be overfit to hostile conditions or rewards by an exploration manipulation attack, which exploits the equilibrium between both. Another study has shown the seriousness of these risks in the smart transportation and cybersecurity industries. While in [147], the risks of adversarial interactions in multi-agent driving environments are highlighted. Where manipulated agents may modify, the results would be an insecure lane change. Wherein [148] authors examined a comprehensive survey about implementing ML-IDS to detect adversarial attacks. Furthermore, implement robust security controls to enhance the defense systems against adversarial attacks. The Deep PackGen introduced a DRL framework to generate unfavorable network packages, revealing how attackers can create an ethnic payload that bypasses IDS within [149]. The study identifies effective defense strategies and customized approaches to combat sophisticated stolen techniques, and assesses the risks posed by unfavorable samples and lawyers to inform adaptive defense strategies.

In addition, a deep dive into AI and ML in cybersecurity, as outlined in [150], highlights the transformative potential of intelligent systems while cautioning against risks such as data imbalance, adversarial manipulation, and a lack of explainability. The study calls for scalable, transparent, and privacy-preserving models to address the evolving threat landscape and ensure the trustworthy deployment of AI. The early work in [151] investigates how AI integrates ML models to maximize the behavior of agent simulation in the cybersecurity domain. Furthermore, the development strategies that enhance the models and approaches, as well as compare RL and DRL, are discussed. Even though the work in [152] reviews adversarial ML in biometric recognition, exposing vulnerabilities in face and fingerprint systems to spoofing and evasion attacks. The paper underscores the risk of data stationarity assumptions in ML models and advocates for adversarial-aware design to secure biometric authentication systems. Thus, the dissertation in [153] utilizes a chemical plant cybersecurity; the red team’s outputs simulate dynamics. The study suggests how the attackers can motivate the plant’s shutdown through sensor manipulation, while the protector uses RL and digital twins to predict and reduce the risk. This task highlights the risk of physical cyber-attack disruption and the need for flexible control systems.

F.

RL-NIDS Security Challenges

Since ARL intelligently revolutionized cybersecurity systems, integrating them into dynamic environments, such as smart networks, IoT networks, and CPS, novel security challenges have been highlighted. The adaptive nature of RL agents makes them robust defense approaches; however, they are vulnerable to sophisticated attacks that exploit their learning mechanisms. Recent literature highlighted increasing concern about conflicting threats, data integrity, and system strengthening, and requires a deep understanding of novel security challenges. This section synthesizes the latest and most significant security challenges facing ARL-NIDS-based security structures, which provide a basis for developing flexible and reliable intelligent systems. Table 12 provides a brief overview of the latest ARL-NIDS security challenges. Recent advancements in RL have introduced both opportunities and risks in cybersecurity applications. The most oppressive are unfavorable attacks that manipulate agents’ behavior, data poisoning that corrupts learning inputs, and rewards that facilitate manipulation, thereby deforming political adaptation. These threats are compounded by the lack of interpretability in RL decisions, making it an obstacle to detecting or explaining anomalous behavior. In addition, RL’s distribution introduces resource-composed environments, such as Those Found in Edge and IoT systems, which pose scalability and real-time adaptation problems. As the attackers evolve, the defense mechanism requires RL models with robust, clear, and privacy-preserving features that can withstand unfavorable conditions and maintain operational integrity. Furthermore, Fig. 17 represents the key ARL-NIDS security issues. As shown in the figure, the security issues are classified into four main categories: system-level threats to integrity, adversarial manipulation, and privacy concerns. RL’s developed landscapes in cybersecurity require an active approach to recognize and reduce innovative dangers. The key challenges in Fig. 17, which emphasize unfavorable manipulation and privacy risks, highlight the complexity of ensuring the integrity of intelligent systems. These issues require interdisciplinary strategies that combine technical hardness with moral and legal foresight. Future research should focus on developing a flexible RL architecture that is robust, scalable, transparent, and privacy-preserving, thereby ensuring the safe distribution of critical infrastructure.

G.

Overall Comparison

This section compares the most significant approaches in ARL-NIDS over the past years. The comparison is summarized in Table 13, which outlines the model employed, the dataset used, the strengths, weaknesses, limitations, results, and future directions. Thus, these comparisons are important because they provide a bird’s-eye perspective of the models utilized in previous studies, their primary goals, and the purpose of their research, all of which are relevant to our intended strategy, ARL-NIDS. Researchers can utilize the document’s strengths and weaknesses, limitations, and future directions to develop innovative studies that leverage ARL via other algorithms and classification, or other associated tasks.

In this study, 154 past studies related to ARL-NIDS are reviewed across various applications and approaches, highlighting the need for a generic ARL-NIDS to detect and identify attacks, unbalanced data, and malicious activities. For the importance of this sub-section, we classified the sub-section into: A.

Publication Analysis

This section examines the publication analysis, where Fig. 18 illustrates the yearly publication distribution over the most recent years. As shown in Fig. 18, 2023 had the highest distribution, while 2015 and 2016 had the lowest distribution. Moreover, Table 14 provides a detailed breakdown of the number of publications.

Regional contributions to ARL-NIDS research vary. Fig. 19 illustrates the regional distribution of ARL-NIDS worldwide. Also, the focus area of ARL-NIDS is identified in Table 15. ML models for NIDS, traffic management, cybersecurity, and networking security have been prominent in North America, especially in the US. The UK, Germany, France, and Italy are actively involved in comparative IDS probes, IoT AD, and botnets. Brazil’s ARL-based IDS and IoT lead South America by learning traffic classification and employing strong DRL. China leads in RL-based NIDS, traffic security, intelligent connected devices, and adaptive RL in the healthcare system, while India, Japan, and South Korea are increasing IoT and smart environmental applications. Middle East countries, including Saudi Arabia, the UAE, Türkiye, Iran, and Jordan, are showing interest in IoT-focused infiltration and securing smart infrastructure. Nigeria, South Africa, and Egypt are researching IoT, smart grid cybersecurity, and theft attacks on IDS.

Finally, Oceania, led by Australia, has upgraded its IoT infrastructure and industrial cybersecurity structure. These regional contributions show that ARL-NIDS are crucial to protecting modern digital infrastructure worldwide.

B.

Analysis of Publication Type

For a deeper understanding of the spread and maturity of ARL-NIDS research, it is necessary to analyze the types of publications, magazines, conferences, and other educational resources that have contributed to shaping the field. Fig. 20 shows the percentage distribution of publication types, while Table 16 provides a numerical breakdown. The Magazine Dominates the Scenario with 107 Publications, Reflecting the Maturity of ARL-NIDS Research and Colleague-Review. Conferences 44 stands for tasks, which indicate a rapid spread of conclusions within a living pipeline and research society of innovative ideas. Other contributions include a book chapter and a dissertation reflecting initial investigative efforts. This distribution suggests that the area has exceeded the first use for stricter, colleague-human production. Journals provide extensive visibility and high citation capacity, highlighting impressive features that shape the track into ARL-NIDS. Meanwhile, the stable flow of conference articles emphasizes innovation and current use, while the limited presence of research and book chapters indicates unused opportunities for deep academic studies.

Overall, the dominance of journal publications emphasizes the comparative weight of applied research over purely theoretical studies, reflecting the field’s strong orientation toward practical deployment in real-world cybersecurity systems.

C.

ARL-NIDS Datasets Analysis

Dataset ARL-based to detect infiltration, create a basis for exercise, validation, and benchmarking. Fig. 21 shows the most commonly used datasets NSL-KD, Cicids2017, CICIS-DOS-2019, UNSW-NB15, and IoT-specific datasets such as AWID, IoT-AID-20, and Kitsune. NSL-KD and Cicids2017 dominate the literature due to their structured labeling and extensive acceptance, while UNSW-NB15 provides more realistic traffic flows and a modern attack scenario.

Beyond the traditional dataset, interest in new domain-specific datasets, particularly in CPS, Industrial IoT (IIoT), and healthcare systems, is increasing, as unwanted threats directly impact safety and operational reliability. However, various data sets exhibit a recurring border imbalance, with significantly fewer malicious samples than benign traffic, and the outdated nature of the traffic patterns, which cannot accurately reflect the landscape of modern attacks. Researchers quickly utilize synthetic data paper techniques, including GANs, learning reinforcement, and simulation environments, to eliminate these intervals and produce side effects, thereby balancing data sets. This trend enhances the strength of model training and provides an opportunity to evaluate ARL-NIDS in realistic, dynamic scenarios. Overall, the development of the data set postpones the projection of the area from the Cultural Heritage Market against domain-specific and unwanted rich datasets, which are better suited to future cybersecurity challenges. D.

Comparative Model Performance

Important inspirations are detected compared to traditional ML and other RL variants, such as DRL and MARL. Traditional ML models (DT, Support Vector Machines, and Random Forests) perform well in a static environment but struggle with dynamic and adaptive attack strategies. In contrast, DRL approaches, such as DQN, DDQN, and PPO, are better suited to detect complex and developed threats, particularly in large datasets. The Marl Framework expands these opportunities, which are especially valuable for IoT and IIoT systems, by enabling distributed and cooperative identity in a multi-agent environment. However, these performances come at the expense of lost profits. Accuracy compared to traditional ML is usually improved with ARL and DRL. Still, training time and calculation costs also increase significantly due to the complexity of the neurological network architecture and negative training. Similarly, the scalability of Marl is expanded in distributed systems, but the coordination between agents introduces synchronization challenges. While the ARL-based models push restrictions on detection functions, their distribution requires balanced performance with practical ideas on costs, speed, and scalability. E.

Industrial & Societal Impact

There are far-reaching implications in integrating ARL-based infiltration systems in industrial and social domains. In critical infrastructure such as energy networks, ARL-NIDS can detect potential cyberattacks and adapt to those that threaten large-scale disruptions. In the healthcare system, learning from dynamic side effect behavior protects against manipulation of IoT units and data breaches, thereby safeguarding patient security and privacy. Similarly, ARL-NIDS supports smart cities’ transportation, tools, and communication networks, such as the system’s flexibility, which can have widespread consequences for safety phenomena. Beyond technical benefits, ARL-NIDS also increases the need for politics and standardization. The lack of standard benchmarks and evaluation standards makes it difficult to compare solutions and ensure industry interoperability. Also, regulatory bodies will require addressing problems such as compliance with privacy frameworks when distributed in the real world. Establishing such policies will increase confidence in ARL-based solutions and accelerate the adoption of industrial & social domains. F.

ARL Summary

The rapid development of ARL for NIDS underscores its transformative capabilities in modern cybersecurity. While technology reflects adaptability, strength, and scalability, the journey towards applying it in the real world requires a balanced understanding of its powers and challenges. This section summarizes the ARL-NIDS research by checking practical distribution challenges, intervals, and boundaries, as well as the benefits of the future, giving a general approach to the current position and the region’s future direction.

Practical Deployment Challenges

Although ARL-NIDS makes important promises, the deployment is not without obstacles. The primary challenge is calculating the overhead and training costs associated with the more complex and multi-agent reinforcement learning structure. Training side effects require extensive resources, often making them impractical for light IoT or edge equipment. Another challenge is the realism of the dataset compared to the real-world traffic. In most available data sets, the attack pattern lacks variation or fails to capture dynamic traffic behavior, which limits the model’s efficiency when implemented in the Live network. In addition, the integration of ARL-NIDs into the existing SOC is composed. SOC workflows depend on detecting interoperability, explanation, and low oppression, as ARL-based models can often meet these requirements without significant fine-tuning. These challenges underscore the need to bridge the gap between educational research and the operational cybersecurity environment.

ARL within Real World Scenarios Deployments

ARL-NIDS real-world deployments can integrate into modern network infrastructures to support adaptive defense mechanisms. For instance, in industrial IoT, ARL agents can continuously optimize anomaly thresholds to counter zero-day attacks. Similarly, in cloud-based NIDS, they can benefit from ARL-driven sampling and reward mechanisms to manage large-scale, distributed traffic. However, practical adoption requires addressing computational costs, data labeling limitations, and the interpretability of decision policies. ARL-NIDS recently commenced transferring from theoretical designs to practical implementations across various domains. However, IoT networks and UAVs, as well as ARL fashions, have been efficiently leveraged to enhance distributed AD and decentralized decision-making, presenting adaptive security against advancing cyber threats [120,123]. Industrial and critical infrastructure systems, including chemical plants and smart grids, are utilizing ARL to autonomously counter denial-of-service (DoS) and command injection attacks in real-time, thereby enhancing operational resilience and security. However, in edge and cloud-based contexts, ARL has proven useful for improving packet sampling, traffic prioritization, and secure routing within dynamic, large-scale infrastructure [122,125]. Furthermore, in cybersecurity simulation frameworks, ARL has been used for adversary scenario modeling and policy evaluation, thus facilitating more realistic and adaptive defense assessments [132]. In addition to traditional networking, ARL-NIDS ideas are applied to biometric systems and autonomous industrial control, where resilience is essential for privacy-preserving authentication and risk mitigation [152]. These real-world implementations demonstrate that ARL-NIDS offers a viable approach to a self-learning, flexible, and adaptive cybersecurity framework that aligns with the evolving AI paradigms in cybersecurity [150].

Benefits & Enhancements

Regardless of obstacles and challenges, ARL-NIDS’ compelling benefits and routes provide growth opportunities. Fig. 22 outlines the key benefits and potential future improvements associated with employing the ARL approach. One of the most remarkable benefits is its adaptive learning ability, which enables real-time adjustment to new or unseen attack strategies. This adaptability improves flexibility against developing cyber hazards compared to stable ML or rule-based IDS. In addition, the ARL framework provides an opportunity to simulate unfavorable dangers during training, leading to robust and active defense systems. Looking ahead, the campaign is expected to address today’s shortcomings. These include the development of light and energy-efficient ARL models for the IoT environment, as well as the integration of clarification tools to increase transparency and confidence. The inclusion of blockchain and federated learning can further enhance decentralization, privacy, and data integrity, while the healthcare system, encompassing research in smart cities, extends the purpose of ARL. Together, these benefits and improvements strengthen ARL-NIDS as a foundation stone for the next generation of cybersecurity systems.

Benefits: —

Adaptive Learning: Continuously evolves to detect new and sophisticated cyberattacks without manual retraining.

Future Enhancements: —

Adaptive Multi-Agent Systems: Expansion of MARL frameworks to coordinate defense across distributed networks and IoT ecosystems.

This survey provides a comprehensive overview of ARL as a transformative approach to enhance NIDS. By synthesizing 159 studies from various domains, including IoT, CPS, smart networks, and autonomous systems, this analysis highlights the growing importance of ARL in addressing dynamic and unfavorable environments. The integration of negative training with RL enables NIDS agents to learn beneficially from hostile behavior, thereby improving detection accuracy, flexibility, and scalability. The review emphasizes that the ARL-NIDS framework is reactive and uses unfavorable conditions to strengthen the defense mechanism. This work lays the foundation for future innovation in intelligent cybersecurity systems through comparative analysis, architectural insights, and the development of a comprehensive taxonomy.

Novelty: This survey study represents one of the first comprehensive investigations into the integration of ARL-NIDS within an IoT environment. This study uniquely combines RL and AL to form the novel discipline of ARL-NIDS. Previous studies have concentrated on each paradigm independently. However, ARL algorithms, their applications, and cybersecurity datasets are categorized in the article, offering an integrative approach absent in the literature. In addition, it discusses real-world implementation scenarios, highlights critical cyber threats, and identifies security challenges associated with the ARL-based NIDS framework. By systematically reviewing existing studies and models, this survey provides both theoretical and practical guidance for researchers and practitioners to understand the ARL-NIDS design principles, benchmark datasets, and evaluation metrics. Overall, this survey contributes a fundamental context that supports future research, model development, and risk-aware applications of ARL in intelligent cybersecurity defense systems.

Key Findings: The current survey study identifies that ARL-NIDS increases the framework’s adaptability and strength. By mimicking side effects during training, ARL-equipped agents can more effectively detect and counter theft, toxicity, and manipulation attacks. Hybrid models that combine ARL with technologies such as GANs, TL, and federated learning promise improvement in accuracy and scalability. In addition, Arl has demonstrated applications across domains, with successful distribution in IoT, industrial control systems, smart health services, and autonomous vehicles. Despite the dependence on benchmark datasets like NSL-KD and CICIDS2017, interest in synthetic and domain-specific datasets is increasing, reflecting the adverse dynamics of the real world.

Limitations of the Research: Although ARL offers convincing benefits, several limitations persist. Training of ARL models is calculation-intensive, presenting challenges for distributing resources such as Edge and IoT units. The interpretation of learning policy for DRL is limited and complicates forensic analysis and regulatory compliance. Various existing data sets have harmful diversity and a lack of real-time traffic properties, which reduces the generality of trained models. Multi-agent ARL systems encounter challenges and scalability issues, particularly in distributed settings. In addition, the reward highlights the reward forming and the policy manipulation ARL framework for the new attack vectors.

Recommendations for Future Studies: Future research should prefer developing clear ARL models to increase openness and confidence in important applications. Adapted light architecture, age, and IoT networks are required to detect real-time infiltration in the IoT network. Establishing standardized evaluation frameworks will provide continuous benchmarking facilities during the implementation of ARL-NIDS. Domain-specific ARL models can enhance the relevant threat of matching areas, such as healthcare, finance, and industrial automation. Integrating ARL with the event reaction mechanisms can enable autonomous and adaptive cyber defense strategies. Furthermore, future investigations on ARL-NIDS should address varied pressing challenges that currently hinder real-world deployment. These include ensuring online learning stability in the face of continuous and evolving network traffic, enhancing explainability to render ARL decision-making transparent and trustworthy, and improving scalability for IoT and edge environments with limited computational resources. Additionally, integrating privacy-preserving mechanisms and cross-domain adaptability might be essential to enable ARL models to generalize security across diverse cybersecurity contexts. These challenges highlight promising directions for advancing ARL-NIDS from theoretical exploration to practical, resilient defense systems. Ultimately, creating unfavorable, rich, balanced, and realistic data sets is possible through the use of GANs or simulation environments, which will be crucial for maintaining the strength and reliability of ARL-based NIDs.