چکیده (انگلیسی):

In day to day information security infrastructure, intrusion detection is indispensible. Signature based
intrusion detection system mechanisms are often available in detecting many types of attacks. But this
mechanism alone is not sufficient in many cases. Another intrusion detection method viz K-means is
employed for clustering and classifying the unlabelled data. IDS is a special embedded device or relied
software package which process of monitoring the events occurring in a computer system or network
(WLAN (Wi-Fi, Wimax)) and LAN ((Ethernet, FDDI, ADSL, Token ring) based) and analysing them for
sign of possible incident which are violations or forthcoming threats of violations of computer security
policies or standard security policies (i.e., DMA acts). We proposed a new methodology for detecting
intrusions by means of clustering and classification algorithms. There we used correlation clustering and Kmeans
clustering algorithm for clustering and random forest algorithm for classification. This type of
extension establishes a layer which refines the escalated alerts using signature-based correlation. In this
study, signature based intrusion detection system with optimised algorithm for better prediction of
intrusions has been addressed. Results are presented and discussed.