In recent years, the proliferation of mobile ad-hoc networks have gained considerable attention due to their flexible and dynamic nature. However, the inherent characteristics of MANETs, such as decentralized infrastructure, wireless communication, and the absence of a fixed base station, make them vulnerable to various security threats, including intrusion attempts. In response to these threats, intrusion detection systems IDSs, have emerged as a critical component of network security in MANET.
Challenges in MANETs
Mobile Ad Hoc Networks, MANETs present a dynamic and decentralized communication environment [22], making them vulnerable to a diverse array of security threats. These threats encompass not only well-known attacks such as Denial of Service (DoS), but also insidious intrusions like black hole, gray hole, and selfish node attacks. The distinctive feature of MANETs lies in their lack of a central authority or predefined infrastructure, which further amplifies the complexity of intrusion detection. Consequently, the development of robust and efficient Intrusion Detection Systems tailored to MANETs has become an intricate and continually evolving domain of research. Addressing the multifaceted challenges posed by these networks is pivotal to ensuring the security and reliability of mobile ad hoc communications in an increasingly interconnected world.
Traditional IDS Approaches
In the realm of MANETs, traditional intrusion detection methods, such as signature-based and anomaly-based detection, have been customized and applied. Signature-based systems hinge on the recognition of predefined attack patterns, while anomaly-based systems are geared towards spotting irregularities and deviations from the established norms of network behavior [23]. Nonetheless, it's imperative to acknowledge that these well-established methods exhibit certain limitations, particularly within the context of dynamic and resource constrained MANETs. The ever-changing nature of MANETs, coupled with their inherent resource constraints, imposes substantial challenges on the applicability and effectiveness of these conventional intrusion detection approaches. This necessitates the exploration of novel and context-aware techniques tailored to the unique intricacies of MANETs, thereby ensuring the security of these mobile and self-configuring networks [24].
Behavioral IDSs
Because of its exceptional capacity to adjust to the extremely dynamic and decentralized character of MANETs, Behavioral Intrusion Detection Systems have become well-known. These systems are mainly concerned with observing and evaluating the behavioral patterns that certain network nodes display. This allows for the detection of abnormalities that may indicate malicious activity or deviance from accepted network norms.
The strength of Behavioral IDSs lies in their advanced analytical capabilities, driven by the integration of machine learning algorithms, statistical analysis, and data mining techniques [25][26]. These methodologies empower behavioral IDSs to learn from historical data and dynamically adjust their intrusion detection mechanisms. Statistical analysis enhances the precision of anomaly detection, ensuring that even the most subtle and sophisticated intrusions are promptly detected.
In practice, behavioral IDSs play a critical role in scrutinizing the voluminous data traffic traversing MANETs. They leverage data mining techniques to meticulously examine network data, uncovering intricate patterns and correlations that may elude conventional intrusion detection methods [27].
Machine Learning-Based Approaches
Machine learning techniques play a pivotal role in the realm of behavioral IDSs within MANETs. These techniques, encompassing decision trees, random forests, and support vector machines, serve as instrumental tools in classifying network behavior, distinguishing between normal and malicious activities [28].
The core strength of employing machine learning methodologies in behavioral IDSs lies in their adaptability to the ever-changing dynamics of MANETs [29]. These techniques are designed to evolve in tandem with shifting network conditions, allowing for the seamless adjustment of intrusion detection mechanisms. By employing machine learning models, Behavioral IDSs gain the flexibility to remain effective even when faced with novel and previously unseen threats, ensuring the network's security.
Statistical Analysis
Statistical approaches have proven invaluable for the detection of outliers and anomalies within network traffic. These methods encompass sophisticated algorithms such as clustering and isolation forests [30][31], which have emerged as potent tools in the arsenal of intrusion detection.
Clustering algorithms play a key role in identifying patterns within the vast and often complex data generated in MANETs. By grouping similar data points into clusters, these algorithms can recognize when network behavior deviates from established norms. On the other hand, isolation forests excel in singling out anomalies by isolating them from the rest of the data. This isolation facilitates the swift and precise detection of intrusions [32].
One of the notable advantages of statistical approaches is their efficiency, which translates into lower false positive rates. This efficiency is particularly crucial in MANETs, where resource constraints necessitate a judicious allocation of computational power. By minimizing false positives, statistical intrusion detection methods help network administrators focus their attention on genuine threats [33][34], thereby enhancing the overall security of the network.
Data Mining and Pattern Recognition
The utilization of data mining and pattern recognition techniques holds profound significance in the quest to unearth intricate attack patterns and emerging trends within the network traffic [35][36]. These methods are instrumental in unraveling subtle deviations from normal behavior that might signify looming threats or vulnerabilities.
Data mining, an indispensable component of modern intrusion detection, involves the systematic analysis of voluminous data to uncover hidden patterns, correlations, and anomalies. This capability enables it to discern irregularities that may evade conventional detection methods. Furthermore, it empowers the detection system with the capacity to understand and adapt to the ever-evolving landscape of MANET traffic.
Pattern recognition techniques, on the other hand, are tailored to identify specific recurring patterns and behaviors. In MANETs, they play a pivotal role in recognizing known attack signatures, even when these signatures have been subtly altered to evade detection. This adaptability to evolving attack strategies enhances the system's resilience against novel threats.
The synergy between data mining and pattern recognition within MANET intrusion detection offers a formidable defense against the dynamic and complex nature of network behavior. By peering into the depths of network traffic, these techniques reveal the hidden adversaries and subtle anomalies, fortifying the network's security posture.
Security Challenges
As MANETs continue to evolve, so do the security challenges. Security and intrusion detection researchers need to address issues related to secure routing, trust establishment, and privacy concerns in MANETs. The security of mobile ad-hoc networks remains a critical concern, and intrusion detection systems are pivotal for safeguarding the integrity and availability of these networks. The diverse array of threats that MANETs face necessitates an ongoing exploration of innovative approaches in intrusion detection.
This literature review sets the stage for our comparative study of behavioral IDSs in MANETs, where we will investigate the strengths and weaknesses of different methods and propose potential enhancements.