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JAIT 2025 Vol.16(5): 632-647
doi: 10.12720/jait.16.5.632-647

Optimized Self-Attention Pyramidal Convolutional Neural Network for Intrusion Detection Framework in IoT

Padma Yenuga 1, Satya Narayana Reddy Beeram 2, Sitanaboyina S. L. Parvathi 3, G. Mahesh Reddy 2, Venugopal Boppana 4, Sunitha Davuluri 4, Repudi Ramesh 2, Meda Srikanth 5, B. Vara Prasad Rao 5, Ravi Kumar Munaganuri 6, and Narasimha Rao 6,*
1. Department of Information Technology, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada, AP, India
2. Department of Computer Science and Engineering, KKR & KSR Institute of Technology and Sciences, Guntur, AP, India
3 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, AP, India
4. Department of Computer Science and Engineering, NRI Institute of Technology, Agiripalli, Krishna, AP, India
5. Department of Computer Science and Engineering, R.V.R. & J.C. College of Engineering and Technology, Vijayawada, AP, India
6. School of Computer Science and Engineering, VIT-AP University, Amaravati-52237, India
Email: Padma.yenuga@pvpsiddhartha.ac.in (P.Y.); snreddy.beeram@gmail.com (S.N.R.B.); srilakshmiparvathi@kluniversity.in (S.S.L.P.); Mahesh.gogula@gmail.com (G.M.R.); srees.boppana@gmail.com (V.B.); sunithadavuluri@gmail.com (S.D.); repudiramesh@gmail.com (R.R.); medasrikanth@gmail.com (M.S.); bvpr@rvrjc.ac.in (B.V.P.R.); ravi2kinus@gmail.com (R.K.M.); y.narasimharao@vitap.ac.in (Y.N.R.)
*Corresponding author

Manuscript received September 12, 2024; revised December 7, 2024; accepted February 19, 2025; published May 9, 2025.

Abstract—This research finds an important place in intrusion detection within the landscape of IoT when it puts forward the optimized Intrusion Detection System (IDS) solution. This is enabled by using the Self-Attention Pyramidal Convolutional Neural Network (SAPCNN) that is powered by Hybrid Ebola and Bald Eagle Search Optimization Algorithm, thereby enhancing classification accuracy. The methodology of this technique is basically a preprocessing tool called Dynamic Context-Sensitive Filtering (DCSF) aimed at removing data redundancy as well as filling missing values, followed by the mechanism of Pelican Optimization Algorithm (POA)-based feature selection. Performance evaluations on the Canadian Institute for Cybersecurity Intrusion Detection System 2017 Dataset (CICIDS2017) dataset reveal that the proposed IDS can accurately detect Distributed Denial of Service (DDoS) attacks with a precision of 98.9% and reduce the computational time by 31.7% as compared to baseline models. These results therefore indicate that the model can successfully handle complex Internet of Things (IoT) intrusion scenarios with great precision and efficiency.
 
Keywords—dynamic context-sensitive filtering, pelican optimization algorithm, self-attention pyramidal convolutional neural network, Ebola optimization search algorithm, bald eagle search optimization algorithm, intrusion detection, internet of things

Cite: Padma Yenuga, Satya Narayana Reddy Beeram, Sitanaboyina S. L. Parvathi, G. Mahesh Reddy, Venugopal Boppana, Sunitha Davuluri, Repudi Ramesh, Meda Srikanth, B. Vara Prasad Rao, Ravi Kumar Munaganuri, and Narasimha Rao, "Optimized Self-Attention Pyramidal Convolutional Neural Network for Intrusion Detection Framework in IoT," Journal of Advances in Information Technology, Vol. 16, No. 5, pp. 632-647, 2025. doi: 10.12720/jait.16.5.632-647

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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