Adaptive and Online Network Intrusion Detection System using Clustering and Extreme Learning Machines
Abstract
Despite the Large Volume of Research Conducted in the Field of Intrusion Detection, Finding a Perfect Solution of Intrusion Detection Systems for Critical Applications is Still a Major Challenge. This is Mainly Due to the Continuous Emergence of Security Threats Which Can Bypass the Outdated Intrusion Detection Systems. the Main Objective of This Paper is to Propose an Adaptive Design of Intrusion Detection Systems on the Basis of Extreme Learning Machines. the Proposed System Offers the Capability of Detecting Known and Novel Attacks and Being Updated According to New Trends of Data Patterns Provided by Security Experts in a Cost-Effective Manner.
Recommended Citation
S. Roshan et al., "Adaptive and Online Network Intrusion Detection System using Clustering and Extreme Learning Machines," Journal of the Franklin Institute, vol. 355, no. 4, pp. 1752 - 1779, Elsevier, Mar 2018.
The definitive version is available at https://doi.org/10.1016/j.jfranklin.2017.06.006
Department(s)
Engineering Management and Systems Engineering
International Standard Serial Number (ISSN)
0016-0032
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Mar 2018
