Abstract
The rapid development of Internet of Things (IoT) technology, together with mobile network technology, has created a never-before-seen world of interconnection, evoking research on how to make it vaster, faster, and safer. To support the ongoing fight against the malicious misuse of networks, in this paper we propose a novel algorithm called AMDES (unmanned aerial system multifractal analysis intrusion detection system) for spoofing attack detection. This novel algorithm is based on both wavelet leader multifractal analysis (WLM) and machine learning (ML) principles. In earlier research on unmanned aerial systems (UAS), intrusion detection systems (IDS) based on multifractal (MF) spectral analysis have been used to provide accurate MF spectrum estimations of network traffic. Such an estimation is then used to detect and characterize flooding anomalies that can be observed in an unmanned aerial vehicle (UAV) network. However, the previous contributions have lacked the consideration of other types of network intrusions commonly observed in UAS networks, such as the man in the middle attack (MITM). In this work, this promising methodology has been accommodated to detect a spoofing attack within a UAS. This methodology highlights a robust approach in terms of false positive performance in detecting intrusions in a UAS location reporting system.
Highlights
The "information age" has provided infinite possibilities of interconnecting various devices
Motivated by the practical problems encountered with unmanned aerial systems (UAS) network security, we aimed to further develop the potential of the intrusion detection systems (IDS) methodology that we proposed in [1], a methodology that takes advantage of the randomly varying nature of network statistics to detect network intrusions, by accomplishing it with a machine learning (ML) classification algorithm
Instead of applying random walk mobility models that have been employed in other research, we considered the mission profiles and mobility patterns of modern aircraft to be more suitable representations of a complex UAS
Summary
The "information age" has provided infinite possibilities of interconnecting various devices. Among the different kinds of network attacks, the denial of service (DoS) flooding attacks and MITM/spoofing attacks are both commonly found in network systems, such as UAS and RADAR systems. These attacks have brought various challenges to the network developer and administrators. Studies on UAS IDS dedicated to flooding anomalies [1] have been conducted. This type of attack severely disrupts the normal operation of a network by injecting an enormous amount of traffic into the network, paralyzing the entire system. While it has been demonstrated that an accurate model-based IDS dedicated to DoS attacks can perform adequately, such attacks are detectable by MF analysis [2,3] with better performance
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