Indian Journal of Science and Technology
Year: 2018, Volume: 11, Issue: 4, Pages: 1-9
Rashidah Funke Olanrewaju, Burhan Ul Islam Khan*, Athaur Rahman Najeeb, Ku Nor Afiza Ku Zahir and Sabahat Hussain
Department of Electrical and Computer Engineering, Kulliyyah of Engineering, International Islamic University Malaysia, Jalan Gombak, 53100 Kuala Lumpur, Selangor, Malaysia; [email protected], [email protected], [email protected], [email protected], [email protected]
*Author for correspondence
Burhan Ul Islam Khan,
Department of Electrical and Computer Engineering, Kulliyyah of Engineering, International Islamic University Malaysia, Jalan Gombak, 53100 Kuala Lumpur, Selangor, Malaysia; [email protected]
Objectives: In this paper, a smart Intrusion Detection System (IDS) has been proposed that detects network attacks in less time after monitoring incoming traffic thus maintaining better performance. Methods/Statistical Analysis: The features are extracted using back-propagation algorithm. Then, only these relevant features are trained with the help of multi-layer perceptron supervised neural network. The simulation is performed using MATLAB. Findings: The proposed system has been verified to have high accuracy rate, high sensitivity as well as a reduction in false positive rate. Besides, the intrusions have been classified into four categories as Denial-of-Service (DoS), User-to-root (U2R), Remote-to-Local (R2L) and Probe attacks; and the alerts are stored and shared via a central log. Thus, the unknown attacks detected by other Intrusion Detection Systems can be sensed by any IDS in the network thereby reducing computational cost as well as enhancing the overall detection rate. Applications/Improvements: The proposed system does not waste time by considering and analysing all the features but takes into consideration only relevant ones for the specific attack and supervised learning neural network is used for intrusion detection. By the application of Snort before backpropagation algorithm, the latter has only one function to perform – detection of unknown attacks. In this way, the time for attack detection is reduced.
Keywords: Back-Propagation, Intrusion Detection System, Multi-Layer Feed-Forward Neural Network, Snort
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