Visible to the public Analysis of Parallel Architectures for Network Intrusion Detection

TitleAnalysis of Parallel Architectures for Network Intrusion Detection
Publication TypeConference Paper
Year of Publication2016
AuthorsCalix, Ricardo A., Cabrera, Armando, Iqbal, Irshad
Conference NameProceedings of the 5th Annual Conference on Research in Information Technology
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4453-1
Keywordscognitive processors, composability, GPU, IDS, Intrusion Detection System (IDS), Intrusion Detection Systems, intrusion detetion, KNN, machine learning, network intrusion detection, pubcrawl, Resiliency
Abstract

Intrusion detection systems need to be both accurate and fast. Speed is important especially when operating at the network level. Additionally, many intrusion detection systems rely on signature based detection approaches. However, machine learning can also be helpful for intrusion detection. One key challenge when using machine learning, aside from the detection accuracy, is using machine learning algorithms that are fast. In this paper, several processing architectures are considered for use in machine learning based intrusion detection systems. These architectures include standard CPUs, GPUs, and cognitive processors. Results of their processing speeds are compared and discussed.

URLhttp://doi.acm.org/10.1145/2978178.2978182
DOI10.1145/2978178.2978182
Citation Keycalix_analysis_2016