Kernel-Space Intrusion Detection Using Software-Defined Networking

Abstract

Software-Defined Networking (SDN) has encountered serious Denial of Service (DoS) attacks. However, existing approaches cannot sufficiently address the serious attacks in the real world because they often present significant overhead and they require long detection and mitigation time. In this paper, we propose a lightweight kernel-level intrusion detection and prevention framework called KernelDetect, which leverages modular string searching and filtering mechanisms with SDN techniques. In KernelDetect, we sufficiently utilize the strengths of the Aho-Corasick and Bloom filter to design KernelDetect by using SDN. We further experimentally compare it with SNORT and BROS, two conventional and popular Intrusion Detection and Prevention System (IDPS) on the Global Environment for Networking Innovations (GENI), a real-world testbed. Our comprehensive studies through experimental data and analysis show that KernelDetect is more efficient and effective than SNORT and BROS. Received on 01 May 2018; accepted on 02 June 2018; published on 09 October 2018