An Intrusion Detection Based on Bayesian Game Theory for UAV Network

Sun, Jianguo; Wang, Wenshan; Da, Qingan; Liang, Kun; Zhao, Guodong; Zhang, Liguo; Han, Qilong

doi:10.4108/eai.21-6-2018.2277054

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…When looking more specifically at Sybil attack detection for FANETs, to the best of our knowledge, there are currently no survey papers that discuss this topic. Nevertheless, we did find a limited number of existing research works in this area, including de Melo et al [29], Sun et al [10] and Walia et al [30], details of which are summarized in the following paragraphs. Note that none of these schemes operate on pure use of physical layer characteristics of the radio signals.…”

Section: B Sybil Attack Detection In Ioftmentioning

confidence: 99%

“…In Sun et al [10], a Bayesian Nash equilibrium game theorybased intrusion detection scheme is proposed, which can detect Sybil attacks among other attack types. The game is between the intrusion detection nodes and the attacking nodes, with each side strategizing to maximize their profits.…”

Section: B Sybil Attack Detection In Ioftmentioning

confidence: 99%

“…It refers to the situation when a malicious node falsely claims to have numerous identities [8] [9]. There are several incentives for a node to act in such a way; in the context of FANETs, examples are such as to allow it to illegitimately acquire more weight in a voting system and to create an illusion of traffic congestion in a particular area [10] [11]. Countermeasures for Sybil attack include prevention, detection and mitigation.…”

Section: Introductionmentioning

confidence: 99%

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Sybil Attack Detection in Internet of Flying Things-IoFT: A Machine Learning Approach

Chulerttiyawong

Jamalipour

2023

IEEE Internet Things J.

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Sybil attack refers to the situation when a malicious node falsely claims to have numerous identities and is known to be one of the security threats to the Internet of Things (IoT). Due to recent increase usage of unmanned aerial vehicles (UAVs) in various applications, Sybil attack has been identified as a threat to the flying ad hoc network (FANET) paradigm and its integration with the IoT to form the Internet of Flying Things (IoFT). In this paper, we propose an intelligent Sybil attack detection approach for FANETs-based IoFT using physical layer characteristics of the radio signals emitted from the UAVs as detected by two ground nodes. A supervised machine learning approach is employed and experimented with several different classifiers available in the Weka workbench platform. The experiment was carried out based on two features of the radio signals, namely, the received signal strength difference (RSSD) and the time difference of arrival (TDoA). Simulation results revealed that the proposed scheme can achieve a high correct classification accuracy of above 91% on average, even for smart malicious nodes with power control capability operating at power levels not directly trained. In addition to its high performance, the proposed scheme is also less susceptible to various attacks commonly carried out on the upper layers, such as data spoofing, due to the use of only intrinsically generated physical layer data. Furthermore, no additional communications overheads of the UAV nodes are required for the functionality of this scheme.

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Section: B Sybil Attack Detection In Ioftmentioning

confidence: 99%

Section: B Sybil Attack Detection In Ioftmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sybil Attack Detection in Internet of Flying Things-IoFT: A Machine Learning Approach

Chulerttiyawong

Jamalipour

2023

IEEE Internet Things J.

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“…The game can either be static or dynamic. Various MAS security can be naturally modeled through game theory [102], [103], [104], [105], [106], [107], [108], [109], [110].…”

Section: Game Theorymentioning

confidence: 99%

Security of Multi-Agent Cyber-Physical Systems: A Survey

Owoputi

Ray

2022

IEEE Access

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Multi-agent systems are becoming increasingly popular due to their successful implementation in several sectors. However, there are a variety of threats that might undermine the agent's security and imperil system security. As a result, security concerns should be addressed during the design of multi-agent systems. This survey reviews different models for securing multi-agent systems, which were developed based on the concepts regarding the agent's role and communications. This paper presents and categorizes the most common attacks on MASs. Then, we study and analyze numerous security strategies in the literature, classifying them as prevention, detection, and resiliency approaches based on reputation and trust. Finally, we recommend which security approach is the best countermeasure for specific types of attacks based on recent advances in the research field.

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“…For example, the authors of [5] developed a real-time resilient method based on the communication connectivity of multi-UAV systems. The authors of [6] proposed an intrusion detection scheme based on the data exchanges to improve the security resilience of the UAV network. Moreover, the authors of [7] developed the resilient algorithms for localization, gathering, and network configurations that highly depend on the communication connectivity of the USNET.…”

Section: Introductionmentioning

confidence: 99%

Resilient UAV Swarm Communications with Graph Convolutional Neural Network

Mou¹,

Gao²,

Liu³

et al. 2021

Preprint

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In this paper, we study the self-healing problem of unmanned aerial vehicle (UAV) swarm network (USNET) that is required to quickly rebuild the communication connectivity under unpredictable external disruptions (UEDs). Firstly, to cope with the one-off UEDs, we propose a graph convolutional neural network (GCN) and find the recovery topology of the USNET in an on-line manner. Secondly, to cope with general UEDs, we develop a GCN based trajectory planning algorithm that can make UAVs rebuild the communication connectivity during the self-healing process. We also design a meta learning scheme to facilitate the on-line executions of the GCN. Numerical results show that the proposed algorithms can rebuild the communication connectivity of the USNET more quickly than the existing algorithms under both one-off UEDs and general UEDs. The simulation results also show that the meta learning scheme can not only enhance the performance of the GCN but also reduce the time complexity of the on-line executions.

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An Intrusion Detection Based on Bayesian Game Theory for UAV Network

Cited by 7 publications

References 15 publications

Sybil Attack Detection in Internet of Flying Things-IoFT: A Machine Learning Approach

Sybil Attack Detection in Internet of Flying Things-IoFT: A Machine Learning Approach

Security of Multi-Agent Cyber-Physical Systems: A Survey

Resilient UAV Swarm Communications with Graph Convolutional Neural Network

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