A Reputation-Based Coalition Game to Prevent Smart Insider Jamming Attacks in MANETs

Oyedare, Taiwo; Sharah, Ashraf Al; Shetty, Sachin

doi:10.1007/978-3-319-33936-8_19

Cited by 4 publications

(6 citation statements)

References 14 publications

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“…The main goal of their approach is to model the interactions between the attacker's attacking target selection and the source's relay selection. Furthermore, a reputation-based coalition game is proposed in [90] to detect the presence of smart jammer nodes when they are passively eavesdropping and collecting information about the network prior to launching the jamming attack. Another work [91] involves a distributed game theorybased multiagent anti-jamming (DMAA) algorithm as a solution against intelligent jammers.…”

Section: E Attacks Detectionmentioning

confidence: 99%

“…[84] SYN Flooding Attack NS-2 Packet delivery ratio, control overhead, delay, throughput, jitter. [90] Insider Jamming Attacks NS-2 Detection accuracy, detection delay, percentage of false positives, and the detection time of the insider jammer. [85] Not specified (Sybil attack, Selfish attack, RREQ flooding attack, and Black-hole attack) VPNPTool Certainty, uncertainty, hesitation.…”

Section: Ns-2mentioning

confidence: 99%

See 1 more Smart Citation

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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Section: E Attacks Detectionmentioning

confidence: 99%

Section: Ns-2mentioning

confidence: 99%

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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“…In [18], a survey of game-theoretic approaches for security requirements and threats mitigation in wireless sensor networks is discussed. In [19], a reputation-based coalition game was proposed to mitigate smart insider jamming attacks in MANET.…”

Section: ) Game Theory-based Analysis and Strategies For Jamming Attackmentioning

confidence: 99%

Federated Learning-Based Cognitive Detection of Jamming Attack in Flying Ad-Hoc Network

et al. 2020

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Flying Ad-hoc Network (FANET) is a decentralized communication system solely formed by Unmanned Aerial Vehicles (UAVs). In FANET, the UAV clients are vulnerable to various malicious attacks such as the jamming attack. The aerial adversaries in the jamming attack disrupt the communication of the victim network through interference on the receiver side. Jamming attack detection in FANET poses new challenges for its key differences from other ad-hoc networks. First, because of the varying communication range and power consumption constraints, any centralized detection system becomes trivial in FANET. Second, the existing decentralized solutions, disregarding the unbalanced sensory data from new spatial environments, are unsuitable for the highly mobile and spatially heterogeneous UAVs in FANET. Third, given a huge number of UAV clients, the global model may need to choose a sub-group of UAV clients for providing a timely global update. Recently, federated learning has gained attention, as it addresses unbalanced data properties besides providing communication efficiency, thus making it a suitable choice for FANET. Therefore, we propose a federated learning-based on-device jamming attack detection security architecture for FANET. We enhance the proposed federated learning model with a client group prioritization technique leveraging the Dempster-Shafer theory. The proposed client group prioritization mechanism allows the aggregator node to identify better client groups for calculating the global update. We evaluated our mechanism with datasets from publicly available standardized jamming attack scenarios by CRAWDAD and the ns-3 simulated FANET architecture and showed that, in terms of accuracy, our proposed solution (82.01% for the CRAWDAD dataset and 89.73% for the ns-3 simulated FANET dataset) outperforms the traditional distributed solution (49.11% for the CRAWDAD dataset and 65.62% for the ns-3 simulated FANET dataset). Moreover, the Dempster-Shafer-based client group prioritization mechanism identifies the best client groups out of 56 client group combinations for efficient federated averaging.INDEX TERMS Unmannad aerial vehicle, flying ad-hoc network, jamming attack, federated learning, ondevice AI, Dempster-Shafer theory.

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“…Each node will have a reputation table for all neighboring nodes. Our previous effort in [1] explains more about the legitimate grand coalition model.…”

Section: Legitimate Grand Coalition Modelmentioning

confidence: 99%

“…In a prior effort, we have proposed a reputation-based coalition game to detect insider threats in MANET [1]; in our approach, we proposed the formation of a grand coalition which will detect insider threats based on stored transmission rate and reputation for each node in the coalition. However, after an insider node is detected and removed from the coalition, it is possible for a group of insider nodes to collaborate and form an attack coalition and attempt to attack the original grand coalition.…”

Section: Introductionmentioning

confidence: 99%

Colluding Jamming Attack on a Grand Coalition by Aggrieved Nodes

Sharah¹,

Oyedare²,

Shetty³

2016

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Mobile Ad-Hoc Networks (MANETs) are highly vulnerable to insider jamming attacks. Several approaches to detect insider jammers in MANET have been proposed. However, once the insider jammer is detected and removed from the network, it is possible for the insider jammer to leverage the knowledge of insider information to launch a future attack. In this paper, we focus on collaborative smart jamming attacks, where the attackers who have been detected as insider jammers in a MANET, return to attack the MANET based on the knowledge learned. The MANET uses a reputation-based coalition game to detect insider jammers. In the collaborative smart jamming attack, two or more smart jammers will form a coalition to attack the coalitions in the MA-NET. The smart jammers were detected and then excluded from their initial coalition, they then regrouped to start their own coalition and share previously gained knowledge about legitimate nodes in their erstwhile coalition with the aim of achieving a highly coordinated successful jamming attack on the legitimate coalition. The success of the attack largely depends on the insider jammer's collective knowledge about the MANET. We present a technique to appropriately represent knowledge gathered by insider jammers which would lead to a successful attack. Simulation results in NS2 depict that coalition of jammers can leverage past knowledge to successfully attack MANET.

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A Reputation-Based Coalition Game to Prevent Smart Insider Jamming Attacks in MANETs

Cited by 4 publications

References 14 publications

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

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

Federated Learning-Based Cognitive Detection of Jamming Attack in Flying Ad-Hoc Network

Colluding Jamming Attack on a Grand Coalition by Aggrieved Nodes

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