IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Hybridization of Machine Learning Algorithm in Intrusion Detection System

Hybridization of Machine Learning Algorithm in Intrusion Detection System
View Sample PDF
Author(s): Amudha P. (Avinashilingam Institute for Home Science and Higher Education for Women, India)and Sivakumari S. (Avinashilingam Institute for Home Science and Higher Education for Women, India)
Copyright: 2020
Pages: 26
Source title: Handbook of Research on Machine and Deep Learning Applications for Cyber Security
Source Author(s)/Editor(s): Padmavathi Ganapathi (Avinashilingam Institute for Home Science and Higher Education for Women, India)and D. Shanmugapriya (Avinashilingam Institute for Home Science and Higher Education for Women, India)
DOI: 10.4018/978-1-5225-9611-0.ch008

Purchase

View Hybridization of Machine Learning Algorithm in Intrusion Detection System on the publisher's website for pricing and purchasing information.

Abstract

In recent years, the field of machine learning grows very fast both on the development of techniques and its application in intrusion detection. The computational complexity of the machine learning algorithms increases rapidly as the number of features in the datasets increases. By choosing the significant features, the number of features in the dataset can be reduced, which is critical to progress the classification accuracy and speed of algorithms. Also, achieving high accuracy and detection rate and lowering false alarm rates are the major challenges in designing an intrusion detection system. The major motivation of this work is to address these issues by hybridizing machine learning and swarm intelligence algorithms for enhancing the performance of intrusion detection system. It also emphasizes applying principal component analysis as feature selection technique on intrusion detection dataset for identifying the most suitable feature subsets which may provide high-quality results in a fast and efficient manner.

Related Content

G. Boopathy, Balaji Ganesan, P. Sivaprakasam, T. Kumaran. © 2026. 42 pages.
G. Prasad. © 2026. 14 pages.
Kishorebabu Dasari, Sujana Parry, Srinivas Mekala. © 2026. 30 pages.
Chikesh Ranjan, Jonnalagadda Srinivas, P. S. Balaji, Kaushik Kumar. © 2026. 24 pages.
G. Ananthi, S. Mehala Shevani, P. Priyadharshini Devi. © 2026. 24 pages.
G. Prasad, Snehal Malik, Aadya Gupta, Yash Nigam. © 2026. 26 pages.
Dhirendra Patel, M. L. Azad. © 2026. 36 pages.
Body Bottom