Transforming voluminous data flow into continuous connection vectors for IDS. (1st January 2014)

• Record Type: Journal Article
• Authors: Salem, Maher; Buehler, Ulrich
• Is Part Of: International journal of internet technology and secured transactions. Volume 5:Number 4 (2014)
• Page Start: 307
• Page End: 326
• Publication Date: 2014-01-01
• Subjects: data flow aggregation -- network security -- intrusion detection systems -- real–time systems -- data mining -- performance monitoring -- continuous connection vectors -- IDS -- massive data flows -- intelligent aggregators -- queuing -- dynamic window size -- network packets -- host events; Internet -- Security measures -- Periodicals; Computer security -- Periodicals; Information systems management -- Security measures -- Periodicals; Computer networks -- Security measures -- Periodicals; Information technology -- Security measures -- Periodicals; 005.805
• DOI: 10.1504/IJITST.2014.068710
• Languages: English
• ISSNs: 1748-569X

Abstract

Handling massive data flows in the online intrusion detection systems is considered one of the major challenges in computer network security. This paper presents a novel method that overcomes this challenge using data mining and statistical techniques. The main contributions of the proposed method are: 1) capturing network traffic and hosts' activities using an intelligent aggregator; 2) introducing a queuing concept of dynamic window size; 3) an improved correlation method between network packets and hosts' events; 4) exporting continuous connection vectors based on certain features set. The proposed method has been evaluated using offline, synthetic, and realistic data flow. Moreover, it is compared to other competent methods. We have examined the plausibility and scalability of the constructed connection vectors by using them in evaluating intrusion detection models.