Mobile Edge computing (MEC) in Cyber-Physical Systems (CPS) with massive resource-constrained Edge Computing Node (ECN) faces new challenges in security provisioning. The traditional centralized security authentication schemes with low performance are no longer applied for MEC in CPS.Due to the mobility of ECN, it is extraordinarily practical for ECN to establish a security association with another AP once leaving the service area of its current AP. In this paper, we represent the related research and propose a novel and efficient Software Defined Networking (SDN)-based Handover Authentication Scheme for MEC in CPS (SHAS). An authentication Handover Module (AHM) in the SDN controller is applied for key distribution and authentication management. Before ECN handovers, the AHM distributes a key to the current serving AP for ECN further handover. Whenever a handover happens, target AP requests the AHM for the one-time session key to authenticate the ECN. The target AP and ECN can proceed with the 3-way handshake protocol by the one-time session key to achieve mutual authentication and secret key confidentiality. Using the logical derivation of Burrows, Abadi, and Needham and formal verification by Automated Validation of Internet Security Protocols and Applications (AVISPA), proposed SHAS scheme can get mutual authentication and secret key confidentiality with a strong anti-attack ability. The simulation results show that the SHAS scheme has the characteristics of lower computational delay and less communication resources.Finally, the practical demonstration of our scheme is done using the widely-accepted NS-3 simulation.

Fuzz testing is a vulnerability discovery technique that tests the robustness of target programs by providing them with unconventional data. With the rapid increase in software quantity, scale and complexity, traditional fuzzing has revealed issues such as incomplete logic coverage, low automation level and insufficient test cases. Machine learning, with its exceptional capabilities in data analysis and classification prediction, presents a promising approach for improve fuzzing. This paper investigates the latest research results in fuzzing and provides a systematic review of machine learning-based fuzzing techniques. Firstly, by outlining the workflow of fuzzing, it summarizes the optimization of different stages of fuzzing using machine learning. Specifically, it focuses on the application of machine learning in the preprocessing phase, test case generation phase, input selection phase and result analysis phase. Secondly, it mentally focuses on the optimization methods of machine learning in the process of mutation, generation and filtering of test cases and compares and analyzes its technical principles. Furthermore, it analyzes the performance gains brought by applying machine learning techniques to fuzzing, mainly including coverage, vulnerability detection capability, efficiency and effectiveness of test cases. Lastly, it concludes by summarizing the challenges and difficulties in combining machine learning with fuzzing and presents prospects for future trends in this field.

As a core enabler of the emerging low-altitude economy, UAV networks face significant security risks during operation, including malicious node infiltration and data tampering. Existing trust management schemes suffer from deficiencies such as strong reliance on infrastructure, insufficient capability for multi-dimensional trust evaluation, and vulnerability to collusion attacks. To address these issues, this paper proposes a group decision-making (GDM)-enabled dynamic trust management method, termed GDM-DTM, for low-altitude UAV networks. GDM-DTM comprises four core parts: Subjective Consistency Evaluation, Objective Consistency Evaluation, Global Consistency Evaluation, and Self-Proof Consistency Evaluation. Furthermore, the method integrates a Dynamic Trust Adjustment Mechanism with multi-attribute trust computation, enabling efficient trust evaluation independent of ground infrastructure and thereby facilitating effective malicious UAV detection. The experimental results demonstrate that under identical conditions with a malicious node ratio of 30%, GDM-DTM achieves an accuracy of 85.04% and an F-score of 91.66%. Compared to the current state-of-the-art methods, this represents an improvement of 6.04 percentage points in accuracy and 3.71 percentage points in F-score.