Artificial Intelligence and Communication Technologies
A Novel Approach for Classification of DDoS Attacks using Naive Bayes
Authors: Usham Sanjota Chanu, Khundrakpam Johnson Singh and Yambem Jina Chanu
Publishing Date: 16-09-2022
ISBN: 978-81-955020-5-9
Abstract
One of the prevailing Internet attacks that cause havoc in society is the Distributed Denial of Service (DDoS) attack. It is human bad intention that triggers it and is one of the most discussed intrusion detection in the field of Information security and control access. Detection of DDoS attack is quite challenging and required effective classification models. Moreover, before any dataset is fed into the classification algorithm, it requires certain pre-processing to decrease the dimensions of the dataset. The original attack datasets contain features that have no or very less significance in classification. Information gain which is a feature selection algorithm is applied to decrease the dimension of the dataset which in turn helps in selection of important features. The Naïve Bayes classifier which works on the principle of bayes theorem is deployed as a classifier to the selected features to classify the class categories within a short duration with improve performance parameters.
Keywords
Naïve Bayes, Information Gain, DDoS attack, Information Security, Ranking Algorithm.
Cite as
Usham Sanjota Chanu, Khundrakpam Johnson Singh and Yambem Jina Chanu, "A Novel Approach for Classification of DDoS Attacks using Naive Bayes", In: Saroj Hiranwal and Garima Mathur (eds), Artificial Intelligence and Communication Technologies, SCRS, India, 2022, pp. 429-436. https://doi.org/10.52458/978-81-955020-5-9-40