Toward Design of an Intelligent Cyber Attack Detection System using Hybrid Feature Reduced Approach for IoT Networks

With simple connectivity and fast-growing demand of smart devices and networks, IoT has become more prone to cyber attacks. In order to detect and prevent cyber attacks in IoT networks, intrusion detection system (IDS) plays a crucial role. However, most of the existing IDS have dimensionality curse...

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Vydáno v:Arabian journal for science and engineering (2011) Ročník 46; číslo 4; s. 3749 - 3778
Hlavní autoři: Kumar, Prabhat, Gupta, Govind P., Tripathi, Rakesh
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2021
Springer Nature B.V
Témata:
Accuracy
Algorithms
Correlation coefficients
Cybersecurity
Datasets
Direct reduction
Electronic devices
Engineering
Humanities and Social Sciences
Hybrid systems
Machine learning
multidisciplinary
Networks
Research Article-Computer Engineering and Computer Science
Science
Cyber attacks
Internet of Things (IoT)
Feature selection
Intrusion detection system (IDS)
Feature ranking
ISSN:2193-567X, 1319-8025, 2191-4281
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Popis
Shrnutí:With simple connectivity and fast-growing demand of smart devices and networks, IoT has become more prone to cyber attacks. In order to detect and prevent cyber attacks in IoT networks, intrusion detection system (IDS) plays a crucial role. However, most of the existing IDS have dimensionality curse that reduces overall IoT systems efficiency. Hence, it is important to remove repetitive and irrelevant features while designing effective IDS. Motivated from aforementioned challenges, this paper presents an intelligent cyber attack detection system for IoT network using a novel hybrid feature reduced approach. This technique first performs feature ranking using correlation coefficient, random forest mean decrease accuracy and gain ratio to obtain three different feature sets. Then, features are combined using a suitably designed mechanism (AND operation), to obtain single optimized feature set. Finally, the obtained reduced feature set is fed to three well-known machine learning algorithms such as random forest, K-nearest neighbor and XGBoost for detection of cyber attacks. The efficiency of the proposed cyber attack detection framework is evaluated using NSL-KDD and two latest IoT-based datasets namely, BoT-IoT and DS2OS. Performance of the proposed framework is evaluated and compared with some recent state-of-the-art techniques found in literature, in terms of accuracy, detection rate (DR), precision and F1 score. Performance analysis using these three datasets shows that the proposed model has achieved DR up to 90%–100%, for most of the attack vectors that has close similarity to normal behaviors and accuracy above 99%.
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ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-020-05181-3