Detection of Eavesdropping Attack in UAV-Aided Wireless Systems: Unsupervised Learning With One-Class SVM and K-Means Clustering

Hoang, Tiep M.; Nguyen, Nghia M.; Duong, Trung Q.

doi:10.1109/lwc.2019.2945022

Cited by 88 publications

(36 citation statements)

References 12 publications

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“…Then, the potential of using unsupervised learning for the detection of active eavesdropping in UAV-aided relay-based networks was investigated in Reference [55], where the uplink was responsible for the authentication procedure. More importantly, one-class SVM and K-means clustering were exploited for the detection of possible attacks during the authentication.…”

Section: Security and Safety Issuesmentioning

confidence: 99%

A Survey on Machine-Learning Techniques for UAV-Based Communications

Bithas

Michailidis

Νομικός

et al. 2019

Sensors

260

144

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Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

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Section: Security and Safety Issuesmentioning

confidence: 99%

A Survey on Machine-Learning Techniques for UAV-Based Communications

Bithas

Michailidis

Νομικός

et al. 2019

Sensors

260

144

View full text Add to dashboard Cite

show abstract

“…In [65], an attack detection technique was proposed in which two different machine learning algorithms such as Support Vector Machine (SVM) and K-mean clustering are used. These algorithms learn from the data and make decisions for the upcoming samples.…”

Section: Learning-based Intrusion Detectionmentioning

confidence: 99%

“…We have analyzed some statistical results of machine learning based techniques for UAVs which either incorporate rule-based or learning-based techniques [65], [69]- [74]. The results analyzed are displayed in Table 4.…”

Section: Rules-based Intrusion Detectionmentioning

confidence: 99%

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

2021

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With the rapid growth in technology, the use of Unmanned Aerial Vehicles (UAVs) have increased in civil and military applications including rescue operations, disaster recovery, and military operations. Despite the utility and advantages of UAVs, they may lead to major security breaches in the context of hardware, software, and communication channel, due their ease of use and availability. UAVs are vulnerable to various types of attacks such as spoofing, false data injection, jamming, fuzzing, availability, confidentiality, and integrity attacks. To overcome these security threats, researchers have been investigating strong security protocols to keep UAVs safe from the attackers. Nevertheless, there are many flaws in the developed protocols which can be exploited by hackers. Therefore, it is becomes crucial to study and analyze the existing security protocols used in UAVs to discover and address their vulnerabilities and weaknesses. The purpose of this study is to explore the vulnerabilities in the security protocols and propose guidelines to improve the security and provide future research directions.

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“… Eavesdropping: Eavesdropping is a real-time attack in which an attacker intercepts private communications such as calls, texts, faxes, and video conferences. The main purpose here is to steal information sent over the network (Hoang et al, 2019).  Fake Node and Malicious: This type of attack is an attack where the attacker enters fake data through a node added to the system.…”

Section: Perception Layermentioning

confidence: 99%

An in-depth exam of IoT, IoT Core Components, IoT Layers, and Attack Types

Yıldırım¹,

Demiroğlu²,

Şenol³

2021

European Journal of Science and Technology

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The Internet of Things (IoT) is a global network of devices that can communicate with each other through different communication protocols and have the ability to detect and process data. Since the Internet of Things connects electronic devices to the Internet for efficient examination and execution of daily activities, it has a seriously positive effect on human life. These devices use some communication protocols to communicate with each other. The selection of these communication protocols is of great importance. The data obtained from these smart devices are stored with the help of cloud technology. These stored data reveal the concept of big data. The processing and analysis of these data are of great importance. Machine learning methods are of great importance in processing this large amount of data. In this study, IoT, the basic components of the IoT, layer structure, security attacks are examined.

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Detection of Eavesdropping Attack in UAV-Aided Wireless Systems: Unsupervised Learning With One-Class SVM and K-Means Clustering

Cited by 88 publications

References 12 publications

A Survey on Machine-Learning Techniques for UAV-Based Communications

A Survey on Machine-Learning Techniques for UAV-Based Communications

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

An in-depth exam of IoT, IoT Core Components, IoT Layers, and Attack Types

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