A Hybrid Approach of Fuzzy C-mean Clustering and Genetic Algorithm (GA) to Improve Intrusion Detection Rate
Abstract: This paper describes a hybrid approach of Fuzzy C-means clustering and Genetic Algorithm (GA) is proposed that provides better accuracy & increases the intrusion detection rate. This approach provides better accuracy of detection as compared to K-means and FCM Clustering. With this proposed approach intrusion detection rate is improved considerably. A brief overview of a hybrid approach of genetic algorithm and fuzzy c-means clustering to improve anomaly or intrusion is presented. This paper proposes genetic algorithm and fuzzy c-means clustering to generate to detect intrusions. The goal of intrusion detection is to monitor network activities automatically, detect malicious attacks and to establish a proper architecture of the computer network security. We have been using fuzzy data mining techniques to extract patterns that represent normal behavior for intrusion detection. We describe a variety of modifications that we have made to the data mining algorithms in order to improve accuracy and efficiency.
Keywords: intrusion detection, clustering, fuzzy c-means clustering, genetic algorithm, Kddcup 99 Dataset
How to Cite?: Kamaldeep Kaur, Navjot Kaur, "A Hybrid Approach of Fuzzy C-mean Clustering and Genetic Algorithm (GA) to Improve Intrusion Detection Rate", Volume 5 Issue 5, May 2016, International Journal of Science and Research (IJSR), Pages: 955-959, https://www.ijsr.net/getabstract.php?paperid=NOV163546, DOI: https://dx.doi.org/10.21275/NOV163546