A Novel Random Neural Network Based Approach for Intrusion Detection Systems

Qureshi, Ayyaz-Ul-Haq; Larijani, Hadi; Ahmad, Jawad; Mtetwa, Nhamoinesu

Abstract

Computer security and privacy of user specific data is a prime concern in day to day communication. The mass use of internet connected systems has given rise to many vulnerabilities which includes attacks on smart devices. Regular occurrence of such events has made the availability of scalable Intrusion Detection System (IDS) a perilous challenge. An intelligent IDS should be able to stop the malicious activity before it destabilizes the core network and to achieve this goal we propose a novel Random Neural Network based Intrusion Detection System (RNN-IDS) in this paper. The performance is evaluated by training different numbers of input and hidden layer neurons with learning rates on benchmark NSL-KDD dataset for binary classification. To validate the feasibility of proposed scheme, results were compared with existing systems and its performance was evaluated by the detection of novel attacks while obtaining an accuracy of 94.50%.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2018 10th Computer Science and Electronic Engineering (CEEC)
• Start Date: Sep 19, 2018
• End Date: Sep 21, 2018
• Acceptance Date: Aug 1, 2018
• Online Publication Date: Mar 28, 2019
• Publication Date: 2018-09
• Deposit Date: Sep 13, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• ISBN: 9781538672754
• DOI: https://doi.org/10.1109/ceec.2018.8674228
• Keywords: Intrusion Detection, Machine Learning, Neural Networks, NSL-KDD, Internet of Things Security
• Public URL: http://researchrepository.napier.ac.uk/Output/2133644