Open AccessAn Application and Performance Evaluation of Twin Extreme Learning Machine Classifier for Intrusion Detection
Author(s) -
D. Vivek,
K. Selvanayaki,
C. Anoor Selvi
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.h6821.078919
Subject(s) - hacker , intrusion detection system , computer science , extreme learning machine , artificial intelligence , machine learning , identification (biology) , intrusion , classifier (uml) , support vector machine , computer security , artificial neural network , botany , geochemistry , biology , geology
Network along with Security is most significant in the digitalized environment. It is necessary to secure data from hackers and intruders. A strategy involved in protection of information from hackers will be termed as Intrusion Detection System (IDS).By taking into nature of attack or the usual conduct of user, investigation along with forecasting activities of the clients will be performed by mentioned system.Variousstrategies are utilized for the intrusion detection system. For the purpose of identification of hacking activity, utilization of machine learning based approach might be considered as novel strategy.In this paper, for identification of the hacking activity will be carried out by Twin Extreme Learning Machines (TELM).Employing the concept of Twin Support Vector Machine with the fundamental structure of Extreme Learning Machine is considered in the establishment of Twin Extreme Learning Machine (TELM).Also, its performance and accuracy are compared with the other intrusion detection techniques