Detecting Signal Spoofing and Jamming Attacks in UAV Networks using a Lightweight IDS
Detecting Signal Spoofing and Jamming Attacks in UAV Networks using a Lightweight IDS
复制标题
DOI：
10.1109/cits.2019.8862148
复制
发表时间：
2019-08
期刊：
影响因子：
--
通讯作者：
Menaka Pushpa Arthur
中科院分区：
文献类型：
--
作者：
Menaka Pushpa Arthur
近来，鉴于他们在平民和防御域中的应用，与无人机有关的安全问题受到了网络和通信电路的广泛关注。无人驾驶飞机的自动驾驶系统（无人机）无法保护无人机免受信号的攻击和黑客攻击。通过攻击者控制飞行操作或操纵无人机的自动驾驶仪系统，从地面或飞行的车辆进行了飞行。 ，无人机需要自适应侵入检测系统，以识别其入侵者并确保其安全回家（RTH）。具有多类SVM的自学成才（STL）也用于维持ID的高真实率，即使在未知领域中，IDS恢复阶段也是如此。对于动态路线学习以促进无人机的安全返回结果。特异性。
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.