Wenbin Zhai

In recent years, Unmanned Aerial Vehicle (UAV) networks are widely used in both military and civilian scenarios. However, due to the distributed nature, they are also vulnerable to threats from adversaries. Time delay attack is a type of internal attack which maliciously delays the transmission of data packets and further causes great damage to UAV networks. Furthermore, it is easy to implement and difficult to detect due to the avoidance of packet modification and the unique characteristics of UAV networks. However, to the best of our knowledge, there is no research on time delay attack detection in UAV networks. In this paper, we propose an Efficient Time Delay Attack Detection Framework (ETD). First, we collect and select delay-related features from four different dimensions, namely delay, node, message and connection. Meanwhile, we utilize the pre-planned trajectory information to accurately calculate the real forwarding delay of nodes. Then, one-class classification is used to train the detection model, and the forwarding behaviors of all nodes can be evaluated, based on which their trust values can be obtained. Finally, the K-Means clustering method is used to distinguish malicious nodes from benign ones according to their trust values. Through extensive simulation, we demonstrate that ETD can achieve higher than 80% detection accuracy with less than 2.5% extra overhead in various settings of UAV networks and different routing protocols.

Recently, unmanned aerial vehicle (UAV) networks have been widely used in military and civilian scenarios; however, they suffer various attacks. Time-delay attacks maliciously delay the transmission of packets without tampering with the contents or significantly affecting the transmission pattern, making detection difficult. In this paper, a holistic cross-layer time-delay attack detection framework (HOTD) is proposed for UAV networks. A holistic selection of the delay-related features available at all layers is performed, before adopting supervised learning to build a consistency model between these features and the corresponding forwarding delay to calculate the degree of consistency of each node. Finally, the clustering method is used to distinguish malicious from benign nodes according to their degree of consistency. Experimental results show that the performance of HOTD is superior to that of state-of-the-art detection methods, and it achieves a detection accuracy higher than 85% with less than 2.5% additional overhead.

Unmanned Aerial Vehicles (UAVs) have been widely used in both military and civilian scenarios since they are low in cost and flexible in use. They can adapt to a wide variety of dangerous scenarios and complete many tasks that manned aerial vehicles (MAVs) cannot undertake. In order to establish connectivity and collect data in large areas, numerous UAVs often cooperate with each other and set up a UAV wireless network. Many multi-hop routing protocols have been proposed to efficiently deliver messages with high delivery ratio and low energy consumption. However, most of them do not consider that the power level of UAVs is adjustable. In this paper, we propose a Power-Aware Routing (PAR) algorithm for UAV networks. PAR utilizes the pre-planned trajectory information of UAVs to compute the encounters at different power levels, and then constructs a power-aware encounter tree to calculate the transmission path with minimum energy consumption from the source to the destination within the delay constraint. Through extensive simulations, we demonstrate that compared with three classic algorithms, PAR significantly reduces the energy consumption and improves the network performance on the basis of ensuring timely delivery of packets.