Exploring Manet Security Aspects: Analysis of Attacks and Node Misbehaviour Issues

Khan, Burhan Ul Islam; Anwar, Farhat; Rahman, Farah Diyana Bt. Abdul; Olanrewaju, Rashidah Funke; Kiah, Miss Laiha B. Mat; Rahman, Arafatur; Janin, Zuriati

doi:10.22452/mjcs.vol35no4.2

Cited by 3 publications

(5 citation statements)

References 63 publications

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“…Detecting collaborative attacks is difficult. Researchers are exploring security mechanisms [13][14] like intrusion detection systems, reputation-based models, and secure routing protocols to address this. Cooperation among network nodes in sharing information can also help detect and mitigate these threats.…”

Section: Problem Statementmentioning

confidence: 99%

Collaborative Attackers Detection and Route Optimization by Swarm Intelligent-based Q-learning in MANETs

2024

IJIES

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Mobile Adhoc Networks (MANETs) are vulnerable to various attacks such as Black Hole Attack (BHA), Gray Hole Attack (GHA), and Wormhole Attacks (WHA). While researchers have focused on detecting and mitigating individual attacks, protection against collaborative attacks is limited. Therefore, this article introduces a new Tunicate Swarm Optimization Q-learning-based Collaborative Attacker Detection Algorithm (TSOQCADA) to identify and prevent collaborative attackers like BHA, GHA, and WHA, thereby improving routing efficiency. This algorithm utilizes feedback about node properties such as energy, reputation, buffer space, transmission delay, and packet transfer rate from all nodes to determine efficient packet routing. In the TSOQ-learning algorithm, the TSO is adopted to set the Q-table values, resulting in faster convergence of Q-learning. First, a Q-table with prior knowledge is trained to enhance searchability. Additionally, a novel selective search mechanism is adopted to improve exploration efficiency and reduce unwanted explorations by considering the correlation between current and target locations. Furthermore, a nonlinear function is designed to achieve a tradeoff between search and use abilities in Q-learning, dynamically changing ε value in the ε-greedy method according to the number of iterations. Thus, the TSOQ-learning can efficiently obtain a routing path by isolating collaborative attackers with low reputation values. Simulation results show that the TSOQCADA achieves a Packet Delivery Ratio (PDR) of 94.8%, Packet Loss Rate (PLR) of 5.2%, energy consumption of 2.53J energy/packet, throughput of 355Kbps, and End-to-End (E2E) delay of 35ms for a network of 100 nodes with 20 malicious nodes in MANET, outperforming the Efficient Trust-based Routing Scheme (ETRS), Hybrid Trust-based Reputation Mechanism (HTRM) and Deep Neural Learned Projective Pursuit Regression-based Watchdog Malicious Node Detection and Isolation (DNLPPR-WMNDI) algorithms.

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Section: Problem Statementmentioning

confidence: 99%

Collaborative Attackers Detection and Route Optimization by Swarm Intelligent-based Q-learning in MANETs

2024

IJIES

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“…This class entails the presentation of various articles pertaining to the identification of malicious nodes in diverse scenarios. MANETs comprise four distinct node types, namely the malicious node, selfish node, erroneous node, and regular node, as documented in [6] and [15]. However, the authors categorized them using varying methods.…”

Section: A Malicious Nodes Mitigationmentioning

confidence: 99%

“…Among the complex security issues in MANETs, profiling the behaviour of nodes [15], and differentiating between "good" and "bad" nodes, stands out due to the heterogeneity of devices integrating a MANET (e.g. laptops, tablet PCs, smartphones).…”

Section: Introductionmentioning

confidence: 99%

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An Overview of Game Theory Approaches for Mobile Ad-Hoc Network’s Security

Messabih,

Kerrache,

Cheriguene

et al. 2023

IEEE Access

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The issue of cybersecurity has gained significant prominence in the context of safeguarding the privacy and integrity of information, especially with the increasing prevalence of interconnected devices such as Mobile Ad-hoc networks (MANETs). Due to their nature, particularly in light of the unavoidable security flaws that arise from the complexity of current and future systems and services, it is crucial to explore how to prevent cyberattacks efficiently. On the other hand, game theory is one of the most significant methods used in this field. Yet, survey articles that consider game theory for security in MANETs are less common. Therefore, in this paper, we survey the articles related to game theory for security in MANETs, presenting an overview of the basic MANETs concepts and their main vulnerabilities. Also, we will propose a taxonomy for recent works, highlighting the limits of the existing works, and outlining some potential future directions for cybersecurity in Ad-hoc networks.INDEX TERMS Cybersecurity, game theory, intrusion, malicious, Mobile Ad-hoc networks (MANETs).• The first type is an honest node which is interested in establishing communication with other nodes with the

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“…The routing process in MANET is responsible for discovering, establishing, and maintaining a route between two mobile nodes. Routing of packets can be performed using either a single-hop or a multi-hop paradigm [1]. In the singlehop paradigm, the destination node is assumed to be inside the communication range of the source node.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Performance Evaluation of Routing Protocols for Mobile Ad-hoc Networks

Khudayer¹,

Al-Zabin²,

Anbar³

et al. 2023

IJACSA

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Mobile Ad Hoc Network (MANET) is a group of wireless mobile nodes that can connect with each other over a number of hops without the need for centralized management or pre-existing infrastructure. MANET has been used in several commercial areas such as intelligent shipping systems, ad hoc gaming, and clever agriculture, and non-commercial areas such as army applications, disaster rescue, and wildlife observing domains. One of the main challenges in MANET is routing mobility management which affects the performance of MANET seriously. The routing protocols have been functionally classified into proactive routing protocols, reactive routing protocols, and hybrid routing protocols. The objective of this paper is to create observations about the advantages and disadvantages of these protocols. Thus, the aim of this paper is to conduct a comparative analysis of the three groups of MANET routing protocols by comparing their features and methods in terms of routing overhead, scalability, delay, and other factors. It was shown that the proactive protocols guarantee the availability of the routes. However, it suffers from scalability and overhead. Whereas, reactive protocols initiate route discovery only when data needs to be sent. However, reactive protocols introduce an undesirable delay due to route establishment, which affects the network performance. Hybrid protocols, attempt to utilize the beneficial features of both reactive and proactive protocols, hybrid protocols are suitable for large networks and keep up-to-date information, but they increase operational complexity. It was concluded that MANET needs enhancement with regard to routing in order to meet the required performance.

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Exploring Manet Security Aspects: Analysis of Attacks and Node Misbehaviour Issues

Cited by 3 publications

References 63 publications

Collaborative Attackers Detection and Route Optimization by Swarm Intelligent-based Q-learning in MANETs

Collaborative Attackers Detection and Route Optimization by Swarm Intelligent-based Q-learning in MANETs

An Overview of Game Theory Approaches for Mobile Ad-Hoc Network’s Security

A Comparative Performance Evaluation of Routing Protocols for Mobile Ad-hoc Networks

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