Analysis of Parallel Architectures for Network Intrusion Detection
Title | Analysis of Parallel Architectures for Network Intrusion Detection |
Publication Type | Conference Paper |
Year of Publication | 2016 |
Authors | Calix, Ricardo A., Cabrera, Armando, Iqbal, Irshad |
Conference Name | Proceedings of the 5th Annual Conference on Research in Information Technology |
Publisher | ACM |
Conference Location | New York, NY, USA |
ISBN Number | 978-1-4503-4453-1 |
Keywords | cognitive 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. |
URL | http://doi.acm.org/10.1145/2978178.2978182 |
DOI | 10.1145/2978178.2978182 |
Citation Key | calix_analysis_2016 |