Visible to the public Compressed Sensing based Intrusion Detection System for Hybrid Wireless Mesh Networks

TitleCompressed Sensing based Intrusion Detection System for Hybrid Wireless Mesh Networks
Publication TypeConference Paper
Year of Publication2018
AuthorsShi, T., Shi, W., Wang, C., Wang, Z.
Conference Name2018 International Conference on Computing, Networking and Communications (ICNC)
Date Publishedmar
Keywordsactive state metric, composability, compressed sensing, compressed sensing theory, compressive sampled data, compressive sampling, computer network security, detecting intrusions, energy consumption, high detection overhead, hybrid wireless mesh networks, hybrid WMN, Intrusion detection, intrusion detection system, Logic gates, Measurement, Metrics, Overhead, Peer-to-peer computing, PHY layer, power consumption, pubcrawl, resilience, signal reconstruction, sparse original signals, sparse signal reconstruction, wireless mesh networks, wireless networks
AbstractAs wireless mesh networks (WMNs) develop rapidly, security issue becomes increasingly important. Intrusion Detection System (IDS) is one of the crucial ways to detect attacks. However, IDS in wireless networks including WMNs brings high detection overhead, which degrades network performance. In this paper, we apply compressed sensing (CS) theory to IDS and propose a CS based IDS for hybrid WMNs. Since CS can reconstruct a sparse signal with compressive sampling, we process the detected data and construct sparse original signals. Through reconstruction algorithm, the compressive sampled data can be reconstructed and used for detecting intrusions, which reduces the detection overhead. We also propose Active State Metric (ASM) as an attack metric for recognizing attacks, which measures the activity in PHY layer and energy consumption of each node. Through intensive simulations, the results show that under 50% attack density, our proposed IDS can ensure 95% detection rate while reducing about 40% detection overhead on average.
Citation Keyshi_compressed_2018