Publication Type:

Conference Proceedings

Source:

2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Bangalore, India, p.2441-2446 (2018)

URL:

https://ieeexplore.ieee.org/document/8554710

Keywords:

Clustering algorithms, computer network, computer network security, Data privacy, Genetic algorithms, genetic k-means algorithm, genetics, IGKM, IGKM algorithm, Internet, Intrusion detection, Intrusion Detection System, k-means++ algorithm, KDD-99, KDD-99 dataset, Network Intrusion, Network intrusion detection, Partitioning algorithms, pattern clustering, personal privacy theft, Prediction algorithms, Time complexity, Training

Abstract:

Internet is a widely used platform nowadays by people across the globe. This has led to the advancement in science and technology. Many surveys show that network intrusion has registered a consistent increase and lead to personal privacy theft and has become a major platform for attack in the recent years. Network intrusion is any unauthorized activity on a computer network. Hence there is a need to develop an effective intrusion detection system. In this paper we acquaint an intrusion detection system that uses improved genetic k-means algorithm(IGKM) to detect the type of intrusion. This paper also shows a comparison between an intrusion detection system that uses the k-means++ algorithm and an intrusion detection system that uses IGKM algorithm while using smaller subset of kdd-99 dataset with thousand instances and the KDD-99 dataset. The experiment shows that the intrusion detection that uses IGKM algorithm is more accurate when compared to k-means++ algorithm.

Cite this Research Publication

J. V. Anand Sukumar, Pranav, I., Neetish, M., and Jayasree Narayanan, “Network Intrusion Detection Using Improved Genetic k-means Algorithm”, 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, Bangalore, India, pp. 2441-2446, 2018.