38 research outputs found
Jamming Detection and Classification in OFDM-based UAVs via Feature- and Spectrogram-tailored Machine Learning
In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks against orthogonal frequency division multiplexing (OFDM) receivers with applications to unmanned aerial vehicles (UAVs). Using software-defined radio (SDR), four types of jamming attacks; namely, barrage, protocol-aware, single-tone, and successive-pulse are launched and investigated. Each type is qualitatively evaluated considering jamming range, launch complexity, and attack severity. Then, a systematic testing procedure is established by placing an SDR in the vicinity of a UAV (i.e., drone) to extract radiometric features before and after a jamming attack is launched. Numeric features that include signal-to-noise ratio (SNR), energy threshold, and key OFDM parameters are used to develop a feature-based classification model via conventional ML algorithms. Furthermore, spectrogram images collected following the same testing procedure are exploited to build a spectrogram-based classification model via state-of-the-art deep learning algorithms (i.e., convolutional neural networks). The performance of both types of algorithms is analyzed quantitatively with metrics including detection and false alarm rates. Results show that the spectrogram-based model classifies jamming with an accuracy of 99.79% and a false-alarm of 0.03%, in comparison to 92.20% and 1.35%, respectively, with the feature-based counterpart
A Survey of Security in UAVs and FANETs: Issues, Threats, Analysis of Attacks, and Solutions
Thanks to the rapidly developing technology, unmanned aerial vehicles (UAVs)
are able to complete a number of tasks in cooperation with each other without
need for human intervention. In recent years, UAVs, which are widely utilized
in military missions, have begun to be deployed in civilian applications and
mostly for commercial purposes. With their growing numbers and range of
applications, UAVs are becoming more and more popular; on the other hand, they
are also the target of various threats which can exploit various
vulnerabilities of UAV systems in order to cause destructive effects. It is
therefore critical that security is ensured for UAVs and the networks that
provide communication between UAVs. In this survey, we aimed to present a
comprehensive detailed approach to security by classifying possible attacks
against UAVs and flying ad hoc networks (FANETs). We classified the security
threats into four major categories that make up the basic structure of UAVs;
hardware attacks, software attacks, sensor attacks, and communication attacks.
In addition, countermeasures against these attacks are presented in separate
groups as prevention and detection. In particular, we focus on the security of
FANETs, which face significant security challenges due to their characteristics
and are also vulnerable to insider attacks. Therefore, this survey presents a
review of the security fundamentals for FANETs, and also four different routing
attacks against FANETs are simulated with realistic parameters and then
analyzed. Finally, limitations and open issues are also discussed to direct
future wor