Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/28598
Title: Performance Analysis-Based Parameter Tuning of Clonal Selection Algorithm for Anomaly Detection
Authors: Sule, Aishat Aladi
Oyefolahan, Ishaq Olabisi
Abdullahi, Muhammad Bashir
Keywords: Intrusion Detection
Artificial Immune System
Clonal Selection Algorithm
KDDcup’99 Dataset
Issue Date: Oct-2018
Publisher: Department of Computer Sciences, University of Lagos, Nigeria
Citation: Sule Aishat Aladi, Oyefolahan Ishaq Oyebisi, and Muhammed Bashir Abdullahi. Performance Analysis-Based Parameter Tuning of Clonal Selection Algorithm for Anomaly Detection. Proceedings of the 1st CoNIMS International Conference and Workshop on Digital Security Considerations for Development (DiSec2018), pp. 47-56. Arthur Mbanefo Digital Research Centre, University of Lagos, Nigeria. 23rd – 25th October, 2018.
Abstract: Intrusion detection has become paramount in the field of network security owing to the fact that network data are being compromised on a daily basis. To this effect, several algorithms have been made available to detect intrusion in the network environment. The Clonal Selection Algorithm (CSA) is one of such algorithms for intrusion detection. Often times, the detection capability of this algorithm are limited by incorrect settings of the parameters involved. Thus, tuning some of the parameters involved in CSA is sacrosanct in determining the performance analysis of the algorithm. Hence, this paper is aimed at tuning the parameters of CSA and analysing its performance for anomaly-based intrusion detection using KDDcup’99 dataset as benchmark for the evaluation. The findings showed that CSA is a good intrusion detection algorithm.
URI: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/28598
Appears in Collections:Computer Science



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