Biblio
The time-varying properties of the wireless channel are a powerful source of information that can complement and enhance traditional security mechanisms. Therefore, we propose a cross-layer authentication mechanism that combines physical layer channel information and traditional authentication mechanism in LTE. To verify the feasibility of the proposed mechanism, we build a cross-layer authentication system that extracts the phase shift information of a typical UE and use the ensemble learning method to train the fingerprint map based on OAI LTE. Experimental results show that our cross-layer authentication mechanism can effectively prompt the security of LTE system.
In 5G networks, the Cloud Radio Access Network (C-RAN) is considered a promising future architecture in terms of minimizing energy consumption and allocating resources efficiently by providing real-time cloud infrastructures, cooperative radio, and centralized data processing. Recently, given their vulnerability to malicious attacks, the security of C-RAN networks has attracted significant attention. Among various anomaly-based intrusion detection techniques, the most promising one is the machine learning-based intrusion detection as it learns without human assistance and adjusts actions accordingly. In this direction, many solutions have been proposed, but they show either low accuracy in terms of attack classification or they offer just a single layer of attack detection. This research focuses on deploying a multi-stage machine learning-based intrusion detection (ML-IDS) in 5G C-RAN that can detect and classify four types of jamming attacks: constant jamming, random jamming, deceptive jamming, and reactive jamming. This deployment enhances security by minimizing the false negatives in C-RAN architectures. The experimental evaluation of the proposed solution is carried out using WSN-DS (Wireless Sensor Networks DataSet), which is a dedicated wireless dataset for intrusion detection. The final classification accuracy of attacks is 94.51% with a 7.84% false negative rate.
The security of wireless network devices has received widespread attention, but most existing schemes cannot achieve fine-grained device identification. In practice, the security vulnerabilities of a device are heavily depending on its model and firmware version. Motivated by this issue, we propose a universal, extensible and device-independent framework called SCAFFISD, which can provide fine-grained identification of wireless routers. It can generate access rules to extract effective information from the router admin page automatically and perform quick scans for known device vulnerabilities. Meanwhile, SCAFFISD can identify rogue access points (APs) in combination with existing detection methods, with the purpose of performing a comprehensive security assessment of wireless networks. We implement the prototype of SCAFFISD and verify its effectiveness through security scans of actual products.
Network security policies contain requirements - including system and software features as well as expected and desired actions of human actors. In this paper, we present a framework for evaluation of textual network security policies as requirements documents to identify areas for improvement. Specifically, our framework concentrates on completeness. We use topic modeling coupled with expert evaluation to learn the complete list of important topics that should be addressed in a network security policy. Using these topics as a checklist, we evaluate (students) a collection of network security policies for completeness, i.e., the level of presence of these topics in the text. We developed three methods for topic recognition to identify missing or poorly addressed topics. We examine network security policies and report the results of our analysis: preliminary success of our approach.