Unmanned Aerial Vehicle (UAV) plays a paramount role in various fields, such as military, aerospace, reconnaissance, agriculture, and many more. The development and implementation of these devices have become vital in terms of usability and reachability. Unfortunately, as they become widespread and their demand grows, they are becoming more and more vulnerable to several security attacks, including, but not limited to, jamming, information leakage, and spoofing. In order to cope with such attacks and security threats, a proper design of robust security protocols is indispensable. Although several pieces of research have been carried out with this regard, there are still research gaps, particularly concerning UAV-to-UAV secure communication, support for perfect forward secrecy, and provision of non-repudiation. Especially in a military scenario, it is essential to solve these gaps. In this paper, we studied the security prerequisites of the UAV communication protocol, specifically in the military setting. More importantly, a security protocol (with two sub-protocols), that serves in securing the communication between UAVs, and between a UAV and a Ground Control Station, is proposed. This protocol, apart from the common security requirements, achieves perfect forward secrecy and non-repudiation, which are essential to a secure military communication. The proposed protocol is formally and thoroughly verified by using the BAN-logic (Burrow-Abadi-Needham logic) and Scyther tool, followed by performance evaluation and implementation of the protocol on a real UAV. From the security and performance evaluation, it is indicated that the proposed protocol is superior compared to other related protocols while meeting confidentiality, integrity, mutual authentication, non-repudiation, perfect forward secrecy, perfect backward secrecy, response to DoS (Denial of Service) attacks, man-in-the-middle protection, and D2D (Drone-to-Drone) security.

The communication in the Smart Home Internet of Things (SH-IoT) comprising various electronic devices and sensors is very sensitive and crucial. In addition, the key requirements of the SH-IoT include channel security, handover support, mobility management, and consistent data rates. Proxy mobile IPv6 (PMIPv6) is considered as one of the core solutions to handle extreme mobility; however, the default PMIPv6 cannot ensure performance enhancement in SH-IoT scenarios, i.e., Route Optimization (RO). The existing security protocols for PMIPv6 cannot support secure RO for smart home IoT services, where mobile nodes (MNs) communicate with home IoT devices not belonging to their domain. Motivated by this, a secure protocol is proposed, which uses trust between PMIPv6 domain and smart home to ensure security as well as performance over the path between MNs and home IoT devices. The proposed protocol includes steps for secure RO and handover management, where mutual authentication, key exchange, perfect forward secrecy, and privacy are supported. The correctness of the proposed protocol is formally analyzed using BAN-logic and Automated Validation of Internet Security Protocols and Applications (AVISPA). Furthermore, network simulations are conducted to evaluate the performance efficiency of the proposed protocol. The results show that the proposed approach is capable of providing secure transmission by resolving the RO problem in PMIPv6 along with the reduction in handover latency, end to end delay and packet loss, and enhancement in throughput and transmission rate even during the handover phase.

Smart farming is rapidly revolutionizing the agricultural sector where embedded Internet of Things (IoT) devices are integrated into the field to maintain or improve the quality of products as well as increase food production. Despite the tremendous benefits, various cybersecurity threats of IoT can also be inherited by the sector. In this paper, we propose a lightweight specification-based distributed detection to identify the misbehavior of heterogeneous embedded IoT nodes efficiently and effectively in a closed-loop smart greenhouse farming system. To expand the monitoring space of a node, we exploited the Kalman-filter algorithm and simple statistical operations to obtain estimates of data. Accordingly, this enables a monitoring node to assess a target node that has distinct physical characteristics and access to natural phenomena. Along with this, we derive the behavior-rules that are specific to the target system and carefully translate these rules into a state machine diagram. Besides, we formally verify the functional correctness of the monitoring processes as well as ensure that the behavior specifications are completely covered by using the model checker tool UPPAAL. Through extensive experimental simulation using Proteus, we verify its applicability to resource-constrained embedded devices, e.g., Arduino-Uno, as well as show high accuracy in detecting misbehaving nodes while having low false alarms.

“Internet of Things” (IoT) bridges the communication barrier between the computing entities by forming a network between them. With a common solution for control and management of IoT devices, these networks are prone to all types of computing threats. Such networks may experience threats which are launched by exploitation of vulnerabilities that are left unhandled during the testing phases. These are often termed as “zero-day” vulnerabilities, and their conversion into a network attack is named as “zero-day” attack. These attacks can affect the IoT devices by exploiting the defense perimeter of the network. The existing solutions are capable of detecting such attacks but do not facilitate communication, which affects the performance of the network. In this paper, a consensus framework is proposed for mitigation of zero-day attacks in IoT networks. The proposed approach uses context behavior of IoT devices as a detection mechanism followed by alert message protocol and critical data sharing protocol for reliable communication during attack mitigation. The numerical analysis suggests that the proposed approach can serve the purpose of detection and elimination of zero-day attacks in IoT network without compromising its performance.

The CAN (Controller Area Network) protocol is widely adopted for in-vehicle networks due to its cost efficiency and reliable transmission. However, despite its popularity, the protocol lacks built-in security mechanisms, making it vulnerable to attacks such as flooding, fuzzing, and DoS. These attacks can exploit vulnerabilities and disrupt the expected behavior of the in-vehicle network. One of the main reasons for these security concerns is that the protocol relies on broadcast frames for communication between ECUs (Electronic Control Units) within the network. To tackle this issue, we present an intrusion detection system that leverages multiple LSTM-Autoencoders. The proposed system utilizes diverse features, including transmission interval and payload value changes, to capture various characteristics of normal network behavior. The system effectively detects anomalies by analyzing different types of features separately using the LSTM-Autoencoder model. In our evaluation, we conducted experiments using real vehicle network traffic, and the results demonstrated the system’s high precision with a 99% detection rate in identifying anomalies.