Performance Enhancement of Improved Distinct Clustering Identification Algorithms for Multi-dimensional Datasets

The complexity of network environment increases day by day due to the increase of Internet usage. The network holds personal, official and sensitive medical banking data in a vast quantity and delivered to the receiver in a stimulated period of time. The detection of Intrusions in the network is most needed to reduce the cyberattacks. Now a days intrusion detection systems (IDSs) with Machine Learning(ML) plays a major role in the optimization of network issues. The major factor that affects the performance of IDS is outliers. In this proposed paper a Flawless feature selection algorithm combined with DBSCAN has been proposed to detect the noises and separate the given data into number of clusters. Then a Navie bayes classifier has been applied for empirical examination of different datasets. The results are analyzed with different classifiers and shows that the proposed method provides a better accuracy compared with other methods