Detecting Signal Spoofing and Jamming Attacks in UAV Networks using a Lightweight IDS
Autor: | Menaka Pushpa Arthur |
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Rok vydání: | 2019 |
Předmět: |
Spoofing attack
Computer science business.industry ComputerApplications_COMPUTERSINOTHERSYSTEMS 020302 automobile design & engineering 020206 networking & telecommunications Jamming 02 engineering and technology Intrusion detection system Computer security computer.software_genre Drone law.invention 0203 mechanical engineering Transmission (telecommunications) law Autopilot 0202 electrical engineering electronic engineering information engineering Global Positioning System business computer Collision avoidance |
Zdroj: | CITS |
DOI: | 10.1109/cits.2019.8862148 |
Popis: | In recent times, security issues pertaining to drones have received a great deal of attention from researchers in networking and communication circles, given their applications in the civilian and defence domains. Object collision avoidance over a trajectory is the only built-in security mechanism in the autopilot system of an unmanned aerial vehicle (UAV). This mechanism, however, cannot protect drones from signal spoofing and hacking attacks. Attacks on UAVs can be triggered from either the ground or flying vehicles in the transmission vicinity medium, by means of which attackers get to control flight operations or manipulate the UAV’s autopilot system. An intermittent network connection that disrupts communication in UAVs exacerbates the problem. Hence, a deep learning-based, adaptive Intrusion Detection System is needed for a drone to identify its intruders and ensure its safe return-to-home (RTH). In the proposed IDS, Self-Taught Learning (STL) with a multiclass SVM is used to maintain the high true positive rate of the IDS, even in uncharted territory. A self-healing method in IDS recovery phase uses the Deep-Q Network, a deep reinforcement learning algorithm for dynamic route learning to facilitate the drone’s safe return home. Simulation results show the efficiency of the proposed IDS against cyber security attacks on UAVs in terms of accuracy, sensitivity and specificity. |
Databáze: | OpenAIRE |
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