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A Data Mining Analysis and Approach with Intrusion Detection / Prevention with Real Traffic

IJCA Proceedings on EGovernance and Cloud Computing Services - 2012
© 2012 by IJCA Journal
EGOV - Number 4
Year of Publication: 2012
Meenakshi R M
E. Saravanan

Meenakshi R M and E Saravanan. Article: A Data Mining Analysis and Approach with Intrusion Detection / Prevention with Real Traffic. IJCA Proceedings on EGovernance and Cloud Computing Services - 2012 EGOV(4):13-17, December 2012. Full text available. BibTeX

	author = {Meenakshi R M and E. Saravanan},
	title = {Article: A Data Mining Analysis and Approach with Intrusion Detection / Prevention with Real Traffic},
	journal = {IJCA Proceedings on EGovernance and Cloud Computing Services - 2012},
	year = {2012},
	volume = {EGOV},
	number = {4},
	pages = {13-17},
	month = {December},
	note = {Full text available}


An intrusion detection system (IDS) is a device or software application that monitors network or system activities for malicious activities or policy violations and produces reports to a Management Station. Some systems may attempt to stop an intrusion attempt but this is neither required nor expected of a monitoring system. Intrusion detection and prevention systems (IDPS) are primarily focused on identifying possible incidents, logging information about them, and reporting attempts. In addition, organizations use IDPS for other purposes, such as identifying problems with security policies, documenting existing threats, and deterring individuals from violating security policies. IDPS have become a necessary addition to the security infrastructure of nearly every organization. False positives and false negatives happen toevery intrusion detection and intrusion preventionsystem. This work proposes a mechanismfor false positive/negative assessment with multipleIDSs/IPSs to collect FP and FN cases fromreal-world traffic and statistically analyze thesecases. Over a period of 16 months, more than2000 FPs and FNs have been collected and analyzed. From the statistical analysis results, weobtain three interesting findings. First, morethan 92. 85 percent of false cases are FPs even ifthe numbers of attack types for FP and FN aresimilar. That is mainly because the behavior ofapplications or the format of the applicationcontent is self-defined; that is, there is not completeconformance to the specifications of RFCs. accordingly, when this application meets anIDS/IPS with strict detection rules, its traffic willbe regarded as malicious traffic, resulting in a lotof FPs. Second, about 91 percent of FP alerts,equal to about 85 percent of false cases, are notrelated to security issues, but to management policy. For example, some companies and campuseslimit or forbid their employees and studentsfrom using peer-to-peer applications; therefore,in order to easily detect P2P traffic, an IDS/IPSis configured to be sensitive to it. Hence, thiscauses alerts to be triggered easily regardless ofwhether the P2P application has malicious trafficor not. The last finding shows that buffer overflow,SQL server attacks, and worm slammerattacks account for 93 percent of FNs, eventhough they are aged attacks. This indicates thatthese attacks always have new variations toevade IDS/IPS detection.


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