A novel random neural network based approach for intrusion detection systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Accepted/In press

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Original languageEnglish
Title of host publicationProceedings of 10th Computer Science and Electronic Engineering (CEEC'18)
StateAccepted/In press - 3 Sep 2018

Conference

Conference10th Computer Science and Electronic Engineering Conference
CountryUnited Kingdom
CityEssex
Dates19/09/1821/09/18

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%.

Keywords

  • intrusion detection, machine learning, neural networks, NSL-KDD, Internet of Things security