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Jamal Ghasemi

Jamal Ghasemi

Academic rank: Associate Professor
ORCID:
Education: PhD.
ScopusId:
Faculty: Faculty of Technology and Engineering
Address:
Phone: 01135302902

Research

Title
Intrusion detection system using an optimized kernel extreme learning machine and efficient features
Type
JournalPaper
Keywords
Intrusion detection system (IDS) genetic algorithms (GA) feature selection kernel extreme learning machine (KELM)
Year
2020
Journal Sadhana - Academy Proceedings in Engineering Sciences
DOI
Researchers Jamal Ghasemi ، Jamal Esmaily ، Reza Moradinezhad

Abstract

In the study of Intrusion Detection System (IDS) choosing proper combination of features is of great importance. Many researchers seek to obtain appropriate features with optimization algorithms. There are several optimization algorithms that can properly select a near-optimal combination of features to reach an improved IDS. Genetic Algorithms (GA) as one of the most powerful methods have been used in this research for feature selection. In this paper, voted outputs of built models on the GA suggested features of a more recent version of KDD CUP 99 dataset, NSL KDD, based on five different labels, have been gathered as a new dataset. Kernel Extreme Learning Machine (KELM), whose parameters have been optimally set by GA, is executed on the obtained dataset and results are collected. Based on IDS criteria, our proposed method can easily outperform general classification algorithms which use all the features of the employed dataset, especially in R2L and U2R with the accuracy of 98.73% and 98.22% respectively which is the highest among the current literature.