This paper is published in Volume-4, Issue-4, 2018
Area
Machine Learning, Meta-heuristic Algorithms, Network Security
Author
Tahira Khorram, Nurdan Akhan Baykan
Org/Univ
Selçuk University, Konya, Turkey, Turkey
Pub. Date
04 August, 2018
Paper ID
V4I4-1414
Publisher
Keywords
Feature selection, Intrusion detection, Network security, Machine learning, Metaheuristic algorithms

Citationsacebook

IEEE
Tahira Khorram, Nurdan Akhan Baykan. Feature selection in network intrusion detection using metaheuristic algorithms, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Tahira Khorram, Nurdan Akhan Baykan (2018). Feature selection in network intrusion detection using metaheuristic algorithms. International Journal of Advance Research, Ideas and Innovations in Technology, 4(4) www.IJARIIT.com.

MLA
Tahira Khorram, Nurdan Akhan Baykan. "Feature selection in network intrusion detection using metaheuristic algorithms." International Journal of Advance Research, Ideas and Innovations in Technology 4.4 (2018). www.IJARIIT.com.

Abstract

Network Intrusion Detection (IDS) mechanism is a primary requirement in the current fast growing network systems. Data Mining and Machine Learning (DM-ML) approaches are widely used for network anomaly detection during the past few years. Machine learning based intrusive activity detector is getting popular. However, they produce a high volume of false alarms. One of the main reasons for generating false signals is redundancy in the datasets. To resolve this problem, an efficient feature selection is necessary to improve the intrusion detection system performance. For this purpose, here we use Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). The three abovementioned algorithms are used to select the most relevant feature set for identifying network attacks, KNN and SVM algorithms are used as classifiers to evaluate the performance of these feature selection algorithms. The standard NSL-KDD dataset is used for training and testing in this study. We used different metrics to determine which of these algorithms provide a better overall performance when they are used for feature selection in intrusion detection. Our experiments show that PSO, ACO and ABC algorithms perform better than other approaches in feature selection. Feature selection based on ABC provides 98.9% of accuracy rate and 0.78% false alarm with KNN algorithm as the classifier, which is the best result among the examined algorithms.