Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization

Abstract

Wireless networks are vulnerable to jamming attacks. Jamming in wireless communication becomes a major research problem due to ease in Unmanned Aerial Vehicle (UAV) launching and blocking of communication channels. Jamming is a subset of Denial of Service Attack (DoS) and an intentional interference where the malicious node disrupts the wireless communication by increasing the noise at the receiver node through transmission interference signal towards the target channel. In this work, the considered jammer is a UAV hovering around the target area to block the communication channel between two transceivers. We proposed a three-dimensional (3-D) UAV jamming localization scheme to track and detect the jammer position at each time step by employing a single boundary node observer. For this purpose, we developed two distributed Extended Kalman Filter (EKF) based schemes: (1) the Distributed EKF (DEKF) scheme using the information of the received power from the jammer at a single nearby boundary node only and (2) Distance Ratio aided Distributed EKF (DEKF-DR) based scheme utilizing an edge node in addition to a single boundary node. Extensive simulations are conducted in order to evaluate the performance of the proposed distributed algorithms for a 3-D trajectory and compared with that of the conventional Centralized EKF (EKF-Centr) based method (which is also modified for the 3-D scenario). The results show the clear supremacy of the proposed distributed algorithms with much lesser complexity in contrast to the conventional EKF-Centr technique.

Deep Learning-Based Location Spoofing Attack Detection and Time-of-Arrival Estimation through Power Received in IoT Networks

“…where the P r (N, AP) is the total power received by the AP and S is the spoofing signal. To estimate the (t prop ) between two nodes in presence of a spoofer, we extended the concept of the distance ratio β, which was described in [38], to estimate the ToA at different points.…”

Adaptive Leader-Following Consensus Tracking Control of Multiple UAVs Subject to Deception Attacks

“…Due to the increasing dependence on the network, UAV systems are more vulnerable to malicious cyberattacks. At the same time, the issue of secure consensus control has also attracted extensive attention of scholars [30][31][32][33][34][35][36][37][38][39][40]. Under known and unknown acceleration of targets, in order to address the issue of collaborative siege and simultaneous assault, two collaborative guidance laws based on multiple attackers encircling the target region were proposed in [30].…”