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.

Multi-drone swarm has been widely used in disaster monitoring, mapping and remote sensing, national defense military and other fields, and has become a research hotspot in recent years. Due to the openness of its operating environment, attackers can invade the control system to capture drone, and then carry out data attacks such as tamper attack, drop attack and replay attack in drone ad-hoc network, which causes a great threat to the security of drone network. Existing malicious nodes detection algorithms are not efficient when applied to drone ad-hoc network, for the following reasons: (1) The malicious node detection algorithms based on reputation usually adopt a static threshold to determine whether a node is malicious, which is inefficient in dynamic drone network. (2) Mutual cooperation based malicious node detection algorithms rely on the high meeting probability of nodes. In order to solve the above problems, we propose a Malicious Drones Detection Algorithm(MDA) based on supervised learning and clustering algorithms. The ground station calculates the reputation value of each routing path according to the received packets from different source nodes, and then evaluates the reputation value of drones with linear regression algorithm. Finally, gaussian clustering algorithm is used to cluster drones and find out malicious drones. Experiments were conducted in indoor and outdoor drone network. The experimental results indicate that the accuracy of MDA outperforms the existing methods by 10% 20%. And in the case of fewer malicious nodes, the accuracy can reach more than 90%, and the error rate is less than 10%.

Unmanned aerial vehicles (UAVs) can gather data in the air and transmit the data to the ground station. Multi-UAV systems have been used in an increasing number of mission scenarios and routing protocols play a critical role in UAV network communications. It is now well established that unstable link quality and frequently changing network topology pose significant challenges for messages forwarding in UAV networks. Hence, traditional mobile ad-hoc network routing protocols do not fit well in UAV networks. In many UAV applications, the flight paths of UAVs are planned in advance before performing missions. The positions and motion information of UAVs are available through Global Positioning System (GPS) and inertial sensors, which can be utilized to calculate the future positions of UAVs. Therefore, the future topology of the UAV network is also available. However, existing work does not take advantage of this information. Based on the trajectory, location and motion information of the UAVs, this paper proposes a future network topology-aware routing (FNTAR) protocol, which uses future location information to make superior routing decisions. Moreover, to mitigate data loss problems caused by unstable links and highly dynamic topology, FNTAR can forward messages to multiple excellent next-hop UAVs based on future network topology, and these UAVs can deliver messages to destinations faster. We implement FNTAR in the simulation experiment, the simulation results demonstrate that FNTAR can achieve lower latency and higher delivery ratio than DTNgeoprotocol.