Improved Intrusion Detection Algorithm based on
TLBO and GA Algorithms
Mohammad Aljanabi1,2 and MohdArfian
Ismail2
1College of Education,
Aliraqia University, Iraq
2Faculty of Computing,
Universiti Malaysia Pahang, Malaysia
Abstract: Optimization
algorithms are widely used for the identification of intrusion. This is
attributable to the increasing number of audit data features and the decreasing
performance of human-based smart Intrusion Detection Systems (IDS) regarding
classification accuracy and training time. In this paper, an improved method
for intrusion detection for binary classification was presented and discussed
in detail. The proposed method combined the New Teaching-Learning-Based
Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning
Machine (ELM), and Logistic Regression (LR) (feature selection and weighting)
NTLBO algorithm with supervised machine learning techniques for Feature Subset
Selection (FSS). The process of selecting the least number of features without
any effect on the result accuracy in FSS was considered a multi-objective
optimization problem. The NTLBO was proposed in this paper as an FSS mechanism;
its algorithm-specific, parameter-less concept (which requires no parameter
tuning during an optimization) was explored. The experiments were performed on
the prominent intrusion machine-learning datasets (KDDCUP’99 and CICIDS 2017),
where significant enhancements were observed with the suggested NTLBO algorithm
as compared to the classical Teaching-Learning-Based Optimization algorithm
(TLBO), NTLBO presented better results than TLBO and many existing works. The
results showed that NTLBO reached 100% accuracy for KDDCUP’99 dataset and 97%
for CICIDS dataset.
Keywords: TLBO,
feature subset selection, NTLBO, IDS, FSS.
Received July 24, 2019; accepted May 9, 2020