Deep Learning Approach for Intelligent Intrusion Detection System

Article


Vinayakumar, R., Alazab, Mamoun, Soman, K. P., Poornachandran, Prabaharan, Al-Nemrat, A. and Venkatraman, Sitalakshmi 2019. Deep Learning Approach for Intelligent Intrusion Detection System. IEEE Access. 7, pp. 41525-41550.
AuthorsVinayakumar, R., Alazab, Mamoun, Soman, K. P., Poornachandran, Prabaharan, Al-Nemrat, A. and Venkatraman, Sitalakshmi
Abstract

Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are occurring in very large volumes requiring a scalable solution. There are different malware datasets available publicly for further research by cyber security community. However, no existing study has shown the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware datasets available publicly are to be updated systematically and benchmarked. In this paper, a deep neural network (DNN), a type of deep learning model, is explored to develop a flexible and effective IDS to detect and classify unforeseen and unpredictable cyberattacks. The continuous change in network behavior and rapid evolution of attacks makes it necessary to evaluate various datasets which are generated over the years through static and dynamic approaches. This type of study facilitates to identify the best algorithm which can effectively work in detecting future cyberattacks. A comprehensive evaluation of experiments of DNNs and other classical machine learning classifiers are shown on various publicly available benchmark malware datasets. The optimal network parameters and network topologies for DNNs are chosen through the following hyperparameter selection methods with KDDCup 99 dataset. All the experiments of DNNs are run till 1,000 epochs with the learning rate varying in the range [0.01–0.5]. The DNN model which performed well on KDDCup 99 is applied on other datasets, such as NSL-KDD, UNSW-NB15, Kyoto, WSN-DS, and CICIDS 2017, to conduct the benchmark. Our DNN model learns the abstract and high-dimensional feature representation of the IDS data by passing them into many hidden layers. Through a rigorous experimental testing, it is confirmed that DNNs perform well in comparison with the classical machine learning classifiers. Finally, we propose a highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks.

JournalIEEE Access
Journal citation7, pp. 41525-41550
ISSN2169-3536
Year2019
PublisherIEEE
Publisher's version
Digital Object Identifier (DOI)doi:10.1109/ACCESS.2019.2895334
Web address (URL)https://doi.org/10.1109/ACCESS.2019.2895334
Publication dates
Online11 Apr 2019
Publication process dates
Deposited25 Apr 2019
Accepted03 Jan 2019
Accepted03 Jan 2019
FunderNvidia
Nvidia
Copyright information© 2019 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.
LicenseAll rights reserved
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