Article: IoT botnet attack detection of ensemble classifier of customised POA optimisation Journal: International Journal of Bio-Inspired Computation (IJBIC) 2026 Vol.27 No.2 pp.103 - 116 Abstract: IoT-based attacks have steadily increased in quantity due to the growing use of IoT devices. The method is useful for resolving optimisation issues since it finds a balance between exploitation and exploration. However, the current intrusion detection systems may have trouble spotting intricate attack patterns if they are not familiar with IoT gadgets and their vulnerabilities to mitigate this challenge. This work introduces the IoT botnet attack detection of customised pelican optimisation algorithm (IoT-BADS-CPOA). Initially, the data normalisation is carried out. From the normalised data, higher-order features, improved correlation, statistical features, and improved technical features are derived as the features. The features are extracted; the presence of attacks is detected by a deep ensemble of classification models. Particularly, a new self-improved version of pelican algorithm is introduced, to tune DQN. In particular, the C-POA has an accuracy of 93.86%. Inderscience Publishers - linking academia, business and industry through research

Title: IoT botnet attack detection of ensemble classifier of customised POA optimisation

Authors: Rakesh Kumar Yadav; Sunil Kumar; Chandan Kumar Sonkar; Rajendra Prasad Mahapatra

Addresses: Amity School of Engineering and Technology, Amity University, Patna, Patna-801503, Bihar, India ' Computer Science and Engineering Department, Galgotias College of Engineering and Technology, Greater Noida-201310, India ' Department of Computer Science and Engineering, Government Polytechnic Premdhar Patti Raniganj, Pratapgarh-230001, Uttar Pradesh, India ' Computer Science and Engineering Department, SRM Institute of Science and Technology, Modinagar, Ghaziabad-201204, India

Abstract: IoT-based attacks have steadily increased in quantity due to the growing use of IoT devices. The method is useful for resolving optimisation issues since it finds a balance between exploitation and exploration. However, the current intrusion detection systems may have trouble spotting intricate attack patterns if they are not familiar with IoT gadgets and their vulnerabilities to mitigate this challenge. This work introduces the IoT botnet attack detection of customised pelican optimisation algorithm (IoT-BADS-CPOA). Initially, the data normalisation is carried out. From the normalised data, higher-order features, improved correlation, statistical features, and improved technical features are derived as the features. The features are extracted; the presence of attacks is detected by a deep ensemble of classification models. Particularly, a new self-improved version of pelican algorithm is introduced, to tune DQN. In particular, the C-POA has an accuracy of 93.86%.

Keywords: internet of things; IoT; attack detection; DQN; improved correlation; C-POA algorithm.

DOI: 10.1504/IJBIC.2026.152566

International Journal of Bio-Inspired Computation, 2026 Vol.27 No.2, pp.103 - 116

Received: 30 Oct 2023
Accepted: 25 Jan 2025
Published online: 27 Mar 2026 *

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Title: IoT botnet attack detection of ensemble classifier of customised POA optimisation

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