A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes

Basan, Elena; Basan, Alexander; Nekrasov, Alexey; Fidge, Colin; Gamec, Ján; Gamcová, Mária

doi:10.3390/s21020509

Cited by 22 publications

(9 citation statements)

References 21 publications

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“…Thus, for example, the speed of the engines and the flight altitude may not be related to the attack but may change due to gusts of wind. For an unambiguous definition of an attack, it is necessary to analyze several cyber-physical parameters at once and determine the degree of their deviation [42].…”

Section: Intrusion Detection Methods For Uavsmentioning

confidence: 99%

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

Basan

Nekrasov

et al. 2021

Drones

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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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Section: Intrusion Detection Methods For Uavsmentioning

confidence: 99%

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

Basan

Nekrasov

et al. 2021

Drones

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“…Our analysis of the normalized cyber-physical parameters showed that the probability distribution of the victim and the normal state is often close to each other, but the abnormal state is significantly different [ 27 ]. To measure the difference between function distributions, the Kullback-Leibler entropy measure is used.…”

Section: Methodsmentioning

confidence: 99%

“…Relative entropy is a measure of the distance between two distributions [28]. In statistics, it appears as the expected logarithm of the likelihood ratio [27]. The relative entropy D (p||q) is the measure of inefficiency if the distribution is assumed to be q when the true distribution is equal to p. For example, if you know the true distribution p of a random variable, you can construct a code with an average description length H (p).…”

Section: The Methods Of Detecting Anomalies Based On Entropy In the Sensor Systemmentioning

confidence: 99%

An Adaptive Protection System for Sensor Networks Based on Analysis of Neighboring Nodes

Gamec

Basan

et al. 2021

Sensors

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Creation and operation of sensor systems is a complex challenge not only for industrial and military purposes but also for consumer services (“smart city”, “smart home”) and other applications such as agriculture (“smart farm”, “smart greenhouse”). The use of such systems gives a positive economic effect and provides additional benefits from various points of view. At the same time, due to a large number of threats and challenges to cyber security, it is necessary to detect attacks on sensor systems in a timely manner. Here we present an anomaly detection method in which sensor nodes observe their neighbors and detect obvious deviations in their behavior. In this way, the community of neighboring nodes works collectively to protect one another. The nodes record only those parameters and attributes that are inherent in any node. Regardless of the node’s functionality, such parameters include the amount of traffic passing through the node, its Central Processing Unit (CPU) load, as well as the presence and number of packets dropped by the node. Our method’s main goal is to implement protection against the active influence of an internal attacker on the whole sensor network. We present the anomaly detection method, a dataset collection strategy, and experimental results that show how different types of attacks can be distinguished in the data produced by the nodes.

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“…The studies were also carried out on the required power, and requirements for the content of the generated signal. The characteristics of determining the fact of an ongoing GPS spoofing attack were also proposed [28].…”

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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A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes

Cited by 22 publications

References 21 publications

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

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

An Adaptive Protection System for Sensor Networks Based on Analysis of Neighboring Nodes

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

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