A Synergetic Pattern Matching Method Based-on DHT Structure for Intrusion Detection in Large-scale Network

AbstractResearch in network security, with the attacks becoming more frequent, increasing complexity means, for the large-scale network intrusion detection, this paper presents a warning by analyzing the behavior of the log, the contents of the relevant association, through the DHT(Distributed Hash Table) distributed architecture, the Collabarative matching, fusion, and ultimately determine the method of attack paths. First, by improving the classical Apriori algorithm, greatly improving the efficiency of the association. At the same time, through the behavior pattern matching algorithms to extract information about the behavior of the alert and the behavior sequence elements to match the template, and through the right path to finally determine the value of the threat of the network path. After the design of a DHT network, the distributed collaborative match the path used to find complex network attacks. Finally, the overall algorithm flow, proposed a complete threat detection system architecture

Joint Optimization of Active and Passive Beamforming in Multi-IRS Aided mmWave Communications

Intelligent reflecting surface (IRS) has been considered as a promising technology to alleviate the blockage effect and enhance coverage in millimeter wave (mmWave) communication. To explore the impact of IRS on the performance of mmWave communication, we investigate a multi-IRS assisted mmWave communication network and formulate a sum rate maximization problem by jointly optimizing the active and passive beamforming and the set of IRSs for assistance. The optimization problem is intractable due to the lack of convexity of the objective function and the binary nature of the IRS selection variables. To tackle the complex non-convex problem, an alternating iterative approach is proposed. In particular, utilizing the fractional programming method to optimize the active and passive beamforming and the optimization of IRS selection is solved by enumerating. Simulation results demonstrate the performance gain of our proposed approach.Comment: 6 pages, 4 figures, accepted by IEEE GLOBECOM 202

AI/ML for Beam Management in 5G-Advanced

In beamformed wireless cellular systems such as 5G New Radio (NR) networks, beam management (BM) is a crucial operation. In the second phase of 5G NR standardization, known as 5G-Advanced, which is being vigorously promoted, the key component is the use of artificial intelligence (AI) based on machine learning (ML) techniques. AI/ML for BM is selected as a representative use case. This article provides an overview of the AI/ML for BM in 5G-Advanced. The legacy non-AI and prime AI-enabled BM frameworks are first introduced and compared. Then, the main scope of AI/ML for BM is presented, including improving accuracy, reducing overhead and latency. Finally, the key challenges and open issues in the standardization of AI/ML for BM are discussed, especially the design of new protocols for AI-enabled BM. This article provides a guideline for the study of AI/ML-based BM standardization.Comment: 4 figure