Internet of Things (IoT) transforms network communication to Machine-to-Machine (M2M) basis and provides open access and new services to citizens and companies. It extends the border of Internet and will be developed as one part of the future 5G networks. However, as the resources of IoT’s front devices are constrained, many security mechanisms are hard to be implemented to protect the IoT networks. Intrusion detection system (IDS) is an efficient technique that can be used to detect the attackers when cryptography is broken, and it can be used to enforce the security of IoT networks. In this article, we analyzed the intrusion detection requirements of IoT networks and then proposed a uniform intrusion detection method for the vast heterogeneous IoT networks based on an automata model. The proposed method can detect and report the possible IoT attacks with three types: jam-attack, false-attack, and reply-attack automatically. We also design an experiment to verify the proposed IDS method and examine the attack of RADIUS application.

Rapid progress of networking technologies leads to an exponential growth in the number of unauthorized or malicious network actions. As a component of defense-in-depth, Network Intrusion Detection System (NIDS) has been expected to detect malicious behaviors. Currently, NIDSs are implemented by various classification techniques, but these techniques are not advanced enough to accurately detect complex or synthetic attacks, especially in the situation of facing massive high-dimensional data. Besides, the inherent defects of NIDSs, namely, high false alarm rate and low detection rate, have not been effectively solved. In order to solve these problems, data fusion (DF) has been applied into network intrusion detection and has achieved good results. However, the literature still lacks thorough analysis and evaluation on data fusion techniques in the field of intrusion detection. Therefore, it is necessary to conduct a comprehensive review on them. In this article, we focus on DF techniques for network intrusion detection and propose a specific definition to describe it. We review the recent advances of DF techniques and propose a series of criteria to compare their performance. Finally, based on the results of the literature review, a number of open issues and future research directions are proposed at the end of this work.

Network slicing empowers 5G with enhanced network performance and efficiency, cost saving, and better QoS and customer satisfaction, and expands the commercial application scenarios of 5G networks. However, the introduction of new techniques usually raises new security threats. Most of the existing works on 5G security only focus on 5G itself and do not analyze 5G network slicing security in detail. We consider network slices as a virtual logical network that can unite the subnetwork parts of 5G. If a 5G network slice has security problems or has been attacked, the entire 5G network will have security risks. In this paper, after synthesizing the existing literature, we analyze the security threats step by step through the lifecycle of 5G network slices, analyzing and summarizing more than 70 security threats in three major categories. Based on the security issues investigated, from a viewpoint of building a secure 5G network slicing system, we compiled 24 security requirements and proposed the corresponding recommendations for different scenarios of 5G network slicing. Finally, we collated the future research trends of 5G network slicing security.

Over the past decade, Open Source Software (OSS) has experienced rapid growth and widespread adoption, attributed to its openness and editability. However, this expansion has also brought significant security challenges, particularly introducing and propagating software vulnerabilities. Despite the use of machine learning and formal methods to tackle these issues, there remains a notable gap in comprehensive surveys that summarize and analyze both Vulnerability Detection (VD) and Security Patch Detection (SPD) in OSS. This article seeks to bridge this gap through an extensive survey that evaluates 127 technical studies published between 2014 and 2023, structured around the Vulnerability-Patch lifecycle. We begin by delineating the six critical events that constitute the Vulnerability-Patch lifecycle, leading to an in-depth exploration of the Vulnerability-Patch ecosystem. We then systematically review the databases commonly used in VD and SPD, and analyze their characteristics. Subsequently, we examine existing VD methods, focusing on traditional and deep learning based approaches. Additionally, we organize current security patch identification methods by kernel type and discuss techniques for detecting the presence of security patches. Based on our comprehensive review, we identify open research questions and propose future research directions that merit further exploration.

Authenticated group key agreement (AGKA) protocols protect the security of communications among a group of users. Dutta and Barua proposed a dynamic AGKA protocol, but it fails to the leaving user attacks and the attacks by two malicious users. In this article, we propose a dynamic AGKA protocol based on the computational Diffie-Hellman problem. The proposed AGKA protocol achieves sound dynamicity and efficiency. With 10 users in a group, the improvements in computation and communication overheads achieve 82.5% and 72.6%, respectively. Then the proposed AGKA protocol is applied to vehicular ad hoc network (VANET). SC-AGKA, a self-certified authentication and key agreement protocol for VANET is presented. Pseudonyms are employed with care in SC-AGKA to provide conditional privacy. It is suitable for SC-AGKA to protect the vehicular group communications in VANET considering its security strength and efficiency.