Title: Anomaly-based network intrusion detection through assessing feature association impact scale

Authors: Jyothsna Veeramreddy; Rama Prasad V. Vaddella

Addresses: Sree Vidyanikethan Engineering College, Near Tirupati (A.P.), India ' Sree Vidyanikethan Engineering College, Near Tirupati (A.P.), India

Abstract: Phenomenal growth in the volume of computer network users leads to the drastic divergence of anomaly activities. Henceforth, it is quite obvious to consider the associability between network transactions and the feature involved to form those transactions. In this regard, the majority of current research is involved to devise signature-based intrusion detection using softcomputing techniques. Most of these soft computing approaches are delivering the computational complexity as O(n2), which is due to magnification of number evolutions. Here in this paper, a meta-heuristic statistical scaling process is derived to estimate if a network transaction is safe, suspicious or intrusion. The proposed model is using duplex graph strategy to estimate the strong associability of the features towards network transactions. The results explored from the empirical study are successfully delivering the accuracy towards identifying the intrusive state of a network transaction. The proposed strategy is able to stabilise the computational complexity to O(n*log(n)) towards assessing the network intrusion using the scale derived.

Keywords: network security; security attacks; intrusion detection systems; IDS; statistical-based intrusion detection; feature set optimisation; feature association impact scale; metaheuristics; duplex graphs; network intrusion.

DOI: 10.1504/IJICS.2016.079185

International Journal of Information and Computer Security, 2016 Vol.8 No.3, pp.241 - 257

Received: 02 Jan 2015
Accepted: 23 Jan 2016

Published online: 21 Sep 2016 *

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