With the development of airborne equipment and integrated avionics technology, the unmanned aerial vehicle (UAV) network replaces human beings in many works. However, the limited energy capacity of UAVs has great restrictions on long-time missions. To prolong the lifetime of the UAV network, we introduce the wireless static chargers (WSCs) into the UAV network, and a nondisruptive wireless rechargeable UAV network (WRUN) model is proposed, in which UAVs can be wirelessly charged without returning back to the charging platform and WSCs are scheduled to turn on and release energy only in the charging time periods. The goal of this article is to minimize the energy waste of WSCs under the premise that UAVs will not run out of energy. To calculate the efficient charging time periods of WSCs, we first discretize flight paths of UAVs so that the nondisruptive charging time schedule problem (nCTSP) that has an infinite solution space both in the spatial and time dimensions can be formalized as an optimization problem. Then, we propose the baseline algorithm exhaust candidate solutions (ECSs) to calculate the charging time periods and propose an improved algorithm using the idea of pruning (PECS) to reduce the computational complexity of ECS. Finally, experiments are conducted and PECS achieves better performance in terms of saving energy consumption and maximizing energy utilization.

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.

Internet of Things (IoT) network is vulnerable to various cyberattacks, especially insider attacks. Most existing studies mainly detect nontargeted insider attackers, who manipulate all packets forwarded by them with a probability. Compared with nontargeted attackers, targeted attackers only manipulate specific packets, which makes them more efficient and covert. In this article, we propose a targeted insider attack model called conditional packets manipulation attack (CPMA), in which attackers maliciously manipulate the packets whose attribute values meet specific conditions with a probability. When resisting the CPMA attack, most existing detection algorithms are inefficient to find such malicious behavior. Also, they detect malicious nodes by collecting and analyzing the overall behavior of nodes, which are not appropriate for energy-constrained nodes in the IoT network. To solve these problems, we present CPMAED, a malicious nodes detection framework against CPMA attack. CPMAED maintains some partial trust metrics for each relay node, which indicate the probability of launch attacks when forwarding the packets with different attribute values. Also, our scheme leverages regression and clustering algorithms to evaluate the trust values of nodes and classify them into benign or malicious. In order to obtain higher detection accuracy, we optimize the routing of transmitted packets and inject the packets to collect more information about nodes to enhance detection. The experimental results show that our proposed scheme utilizing support vector machine and $K$ -means can achieve good detection performance and identify malicious nodes’ attack modes with high accuracy.

With the widespread of cyber attacks, network intrusion detection system (NIDS) is becoming an important and essential tool to protect Internet of Things (IoT) environments. However, it is well known that the NIDS performance depends heavily on the effectiveness of the detection model, which can be influenced significantly by the learning mechanism and the available training data. Many existing studies try to mitigate the above challenges, but few of them consider the adaptability and the cost of deploying an NIDS, the integrity of the learning process, the capacity of model based on concrete traffic samples at the same time. To fill this gap and improve the detection performance, we propose a collaborative learning-based detection framework called ADCL, which can mitigate the limitations on the knowledge of a single model by leveraging multiple models trained in similar environments and detecting intrusions in a collaborative manner. Our evaluation results indicate that ADCL can provide better performance compared with a single model on detecting various attacks in IoT networks. Specifically, ADCL improves F-score by up to 80% for adaptability, 42% in mitigating the reliance on learning integrity, 85% for model capacity. Furthermore, the detection results of ADCL guide those single models to update and increase the F-score by 15%.

Data aggregation is a fundamental and efficient algorithm to reduce the communication overhead and energy consumption in wireless sensor networks (WSNs). However, WSNs need both energy-efficient and privacy-preserving when being deployed in an unprotected area. In this paper, we propose an energy-efficient and privacy-preserving data aggregation algorithm CBDA (the chain-based data aggregation). In the CBDA, sensor nodes will be organized as a tree topology. The leaf nodes of the tree sequentially reconnect with each other to form many chain topologies. For assuring data privacy, after data gathering, (1) the tail nodes (the nodes which on the tail of chain) need to slice their sensing data into J fragments. One fragment is kept by themselves, and they distribute the J-1 data fragments to their neighbor nodes. (2) Each tail node will inject some fake fragments into its J-1 fragments to interfere with adversaries. The CBDA can achieve less energy consumption and higher aggregation accuracy during data aggregation. We perform a comprehensive simulation to make a comparison among the CBDA with existing algorithms. The experimental results demonstrate that the CBDA outperforms the existing algorithms.