A comparative study of different fuzzy classifiers for cloud intrusion detection systems' alerts

Alqahtani, Saeed M. and John, Robert (2016) A comparative study of different fuzzy classifiers for cloud intrusion detection systems' alerts. In: IEEE SSCI 2016, 6-9 Dec 2016, Athens, Greece.

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Abstract

The use of Internet has been increasing day by day and the internet traffic is exponentially increasing. The services providers such as web services providers, email services providers, and cloud service providers have to deal with millions of users per second; and thus, the level of threats to their growing networks is also very high. To deal with this much number of users is a big challenge but detection and prevention of such kinds of threats is even more challenging and vital. This is due to the fact that those threats might cause a severe loss to the service providers in terms of privacy leakage or unavailability of the services to the users. To incorporate this issue, several Intrusion Detections Systems (IDS) have been developed that differ in their detection capabilities, performance and accuracy. In this study, we have used SNORT and SURICATA as well-known IDS systems that are used worldwide. The aim of this paper is to analytically compare the functionality, working and the capability of these two IDS systems in order to detect the intrusions and different kinds of cyber-attacks within MyCloud network. Furthermore, this study also proposes a Fuzzy-Logic engine based on these two IDSs in order to enhances the performance and accuracy of these two systems in terms of increased accuracy, specificity, sensitivity and reduced false alarms. Several experiments in this compatrative study have been conducted by using and testing ISCX dataset, which results that fuzzy logic based IDS outperforms IDS alone whereas FL-SnortIDS system outperforms FL-SuricataIDS.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Cloud Computing; IDS; Fuzzy Logic; Snort; Suricata; ISCX dataset
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Related URLs:
URLURL Type
http://ssci2016.cs.surrey.ac.uk/UNSPECIFIED
Depositing User: Eprints, Support
Date Deposited: 28 Oct 2016 13:06
Last Modified: 07 Feb 2017 17:27
URI: http://eprints.nottingham.ac.uk/id/eprint/38030

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