2025
doi: 10.12720/jait.16.5.632-647
Optimized Self-Attention Pyramidal Convolutional Neural Network for Intrusion Detection Framework in IoT
Padma Yenuga1
,
Satya Narayana Reddy Beeram2 ,
Sitanaboyina S. L. Parvathi3 ,
G. Mahesh Reddy2 ,
Venugopal Boppana4 ,
Sunitha Davuluri4 ,
Repudi Ramesh2 ,
Meda Srikanth5 ,
B. Vara Prasad Rao5 ,
Ravi Kumar Munaganuri6 ,
Narasimha Rao6,*
- 1Department of Information Technology, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada, AP, India
- 2Department of Computer Science and Engineering, KKR & KSR Institute of Technology and Sciences, Guntur, AP, India
- 3Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, AP, India
- 4Department of Computer Science and Engineering, NRI Institute of Technology, Agiripalli, Krishna, AP, India
- 5Department of Computer Science and Engineering, R.V.R. & J.C. College of Engineering and Technology, Vijayawada, AP, India
- 6School of Computer Science and Engineering, VIT-AP University, Amaravati-52237, India
- Manuscript receivedSeptember 12, 2024
- revisedDecember 7, 2024
- acceptedFebruary 19, 2025
- publishedMay 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
How to 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. https://doi.org/10.12720/jait.16.5.632-647
Copyright & License
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).
