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Article

Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things

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Department of Information Technology, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia
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Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia
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Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
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Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
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Author to whom correspondence should be addressed.
Academic Editors: Constantinos Kolias, Georgios Kambourakis and Weizhi Meng
Electronics 2021, 10(11), 1341; https://doi.org/10.3390/electronics10111341
Received: 9 March 2021 / Revised: 27 March 2021 / Accepted: 29 March 2021 / Published: 3 June 2021
(This article belongs to the Special Issue Design of Intelligent Intrusion Detection Systems)
The need for timely identification of Distributed Denial-of-Service (DDoS) attacks in the Internet of Things (IoT) has become critical in minimizing security risks as the number of IoT devices deployed rapidly grows globally and the volume of such attacks rises to unprecedented levels. Instant detection facilitates network security by speeding up warning and disconnection from the network of infected IoT devices, thereby preventing the botnet from propagating and thereby stopping additional attacks. Several methods have been developed for detecting botnet attacks, such as Swarm Intelligence (SI) and Evolutionary Computing (EC)-based algorithms. In this study, we propose a Local-Global best Bat Algorithm for Neural Networks (LGBA-NN) to select both feature subsets and hyperparameters for efficient detection of botnet attacks, inferred from 9 commercial IoT devices infected by two botnets: Gafgyt and Mirai. The proposed Bat Algorithm (BA) adopted the local-global best-based inertia weight to update the bat’s velocity in the swarm. To tackle with swarm diversity of BA, we proposed Gaussian distribution used in the population initialization. Furthermore, the local search mechanism was followed by the Gaussian density function and local-global best function to achieve better exploration during each generation. Enhanced BA was further employed for neural network hyperparameter tuning and weight optimization to classify ten different botnet attacks with an additional one benign target class. The proposed LGBA-NN algorithm was tested on an N-BaIoT data set with extensive real traffic data with benign and malicious target classes. The performance of LGBA-NN was compared with several recent advanced approaches such as weight optimization using Particle Swarm Optimization (PSO-NN) and BA-NN. The experimental results revealed the superiority of LGBA-NN with 90% accuracy over other variants, i.e., BA-NN (85.5% accuracy) and PSO-NN (85.2% accuracy) in multi-class botnet attack detection. View Full-Text
Keywords: botnet attacks; intrusion detection; heuristic optimization; neural networks; bat algorithm; Internet-of-Things security botnet attacks; intrusion detection; heuristic optimization; neural networks; bat algorithm; Internet-of-Things security
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MDPI and ACS Style

Alharbi, A.; Alosaimi, W.; Alyami, H.; Rauf, H.T.; Damaševičius, R. Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things. Electronics 2021, 10, 1341. https://doi.org/10.3390/electronics10111341

AMA Style

Alharbi A, Alosaimi W, Alyami H, Rauf HT, Damaševičius R. Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things. Electronics. 2021; 10(11):1341. https://doi.org/10.3390/electronics10111341

Chicago/Turabian Style

Alharbi, Abdullah, Wael Alosaimi, Hashem Alyami, Hafiz T. Rauf, and Robertas Damaševičius. 2021. "Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things" Electronics 10, no. 11: 1341. https://doi.org/10.3390/electronics10111341

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