Intelligent Intrusion Detection System for a Group of UAVs

Basan, Elena; Lapina, Maria; Mudruk, Nikita N.; Abramov, Evgeny

doi:10.1007/978-3-030-78811-7_22

Cited by 16 publications

(10 citation statements)

References 22 publications

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“…The study and analysis of the scenarios of active, passive, and multi-stage attacks for an intelligent group control system for UAVs were carried out, and based on them a template for describing attacks and general scenarios for influencing the information system of a UAV was proposed, and probable attacks through communication channels (deauthentication and connection, eavesdropping, Blueprinting, Replay, Interception of "handshake", brute-force and others) were described [27].…”

Section: Methodsmentioning

confidence: 99%

Analysis of Communication Channels for the Organization of Control and Interaction of UAVs from the Security Viewpoint

2021

Short Paper Proceedings of the 2nd Siberian Scientific Workshop on Data Analysis Technologies With Applications 2021

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This article considers the issues of increasing the level of security of wireless communication channels between UAVs. The topic is quite relevant, since UAVs can be used to solve critical tasks, such as search operations, reconnaissance, and relaying communications. At the same time, wireless communication channels are not physically protected, and their security can easily be compromised. The article also discusses the main vulnerabilities of communication channels for UAVs, attacks, and threats to information security. Possibilities of increasing the level of security of UAV communication channels are considered. The methods and developments suggested on this topic by the authors are briefly presented.

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Section: Methodsmentioning

confidence: 99%

Analysis of Communication Channels for the Organization of Control and Interaction of UAVs from the Security Viewpoint

2021

Short Paper Proceedings of the 2nd Siberian Scientific Workshop on Data Analysis Technologies With Applications 2021

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“…The intrusion detection approach developed [17], called Attention-based Spatio-Temporal Graph Convolutional Network (ATGCN), integrates a graph convolutional network and a gated recursive unit to form a spatio-temporal graph convolutional network to address the temporal and spatial dynamic properties of UAV networks. The work carried out in [18] aims to identify suspicious behavior in UAV groups and classify the attack accordingly. To achieve this goal, the authors built an experimental setup by imitating traffic transmission among a fleet of UAVs and focused on analyzing how the transmission of traffic changes both when it is operating normally and when it encounters an attack.…”

Section: Related Workmentioning

confidence: 99%

A Novel Intrusion Detection System Based on Artificial Neural Network and Genetic Algorithm With a New Dimensionality Reduction Technique for UAV Communication

Cengiz,

Lipsa,

Dash

et al. 2024

IEEE Access

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Unmanned aerial vehicles (UAVs) are increasingly being deployed in crucial missions for the armed forces, law enforcement, industrial control monitoring, and other sectors. However, these hostile operating circumstances, along with the UAVs' dependence on wireless protocols, pose substantial security threats, limiting their mainstream application. With network security being such a major issue for UAV networks, the machine learning-based intrusion detection system (IDS) has been determined to be an effective strategy for protecting them. Additionally, though the existing methods offer effective strategies for detecting and categorizing abnormalities in the system, they are limited by their inability to adjust to various attack patterns. The dataset used as well as the memory and computational requirement of existing models, poses new challenges. One of the main concerns pertains to the reduced computational and memory demands of these models. So, the work carried out in this paper addresses this challenge. The work carried out in this paper addresses this challenge. A new dimensional reduction technique based on correlation coefficient, information gain, and principal component analysis (PCA) is introduced to reduce the dimensionality of the UAV Attack Dataset. A novel intrusion detection system based on an artificial neural network (ANN) and genetic algorithm (GA) is then proposed. The genetic algorithm is used to generate the optimal weights of the artificial neural network. A comparison is made between the proposed model and the backpropagation network and its variant in terms of its convergence and prediction accuracy. Furthermore, the performance of the proposed model is compared with that of other classifiers. This comparison reveals that the proposed model is time efficient with an increased prediction accuracy of at least 6% more than other classifiers.

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“…In our study, we need to detect the rare occurrence of cyber-physical parameter peak values that do not occur during normal operation of a UAV. Therefore, we also used the Poisson distribution [16,41]. The Poisson model usually describes a scheme of rare events [40,41].…”

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confidence: 99%

“…Therefore, we also used the Poisson distribution [16,41]. The Poisson model usually describes a scheme of rare events [40,41]. Under certain assumptions about the nature of random events, the number of events that occur over a fixed period or in a fixed region of space can obey the following Poisson distributions,…”

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confidence: 99%

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GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence

Basan

Nekrasov

et al. 2021

Drones

Self Cite

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Here, we developed a method for detecting cyber security attacks aimed at spoofing the Global Positioning System (GPS) signal of an Unmanned Aerial Vehicle (UAV). Most methods for detecting UAV anomalies indicative of an attack use machine learning or other such methods that compare normal behavior with abnormal behavior. Such approaches require large amounts of data and significant “training” time to prepare and implement the system. Instead, we consider a new approach based on other mathematical methods for detecting UAV anomalies without the need to first collect a large amount of data and describe normal behavior patterns. Doing so can simplify the process of creating an anomaly detection system, which can further facilitate easier implementation of intrusion detection systems in UAVs. This article presents issues related to ensuring the information security of UAVs. Development of the GPS spoofing detection method for UAVs is then described, based on a preliminary study that made it possible to form a mathematical apparatus for solving the problem. We then explain the necessary analysis of parameters and methods of data normalization, and the analysis of the Kullback—Leibler divergence measure needed to detect anomalies in UAV systems.

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Intelligent Intrusion Detection System for a Group of UAVs

Cited by 16 publications

References 22 publications

Analysis of Communication Channels for the Organization of Control and Interaction of UAVs from the Security Viewpoint

Analysis of Communication Channels for the Organization of Control and Interaction of UAVs from the Security Viewpoint

A Novel Intrusion Detection System Based on Artificial Neural Network and Genetic Algorithm With a New Dimensionality Reduction Technique for UAV Communication

GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence

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