| Title | Network Intrusion Detection Using Improved Genetic k-means Algorithm |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Anand Sukumar, J V, Pranav, I, Neetish, MM, Narayanan, Jayasree |
| Conference Name | 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) |
| Keywords | Clustering algorithms, composability, 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, Metrics, network intrusion, network intrusion detection, Partitioning algorithms, pattern clustering, personal privacy theft, Prediction algorithms, pubcrawl, Resiliency, 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. |
| DOI | 10.1109/ICACCI.2018.8554710 |
| Citation Key | anand_sukumar_network_2018 |
Network Intrusion Detection Using Improved Genetic k-means Algorithm