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arXiv 2025年

作者： Naeem, Amna Khan, Muazzam A. Alasbali, Nada Ahinad, Jawad Khattak, Aizaz Ahmad Khan, Muhammad Shahbaz Department of Computer Science Quaid-i-azam University Islamabad Pakistan Department of Computer Sciences Quaid-e-Azam University Islamabad Pakistan Department ofInformatics and Computing Systems College of Computer Science King Khalid University Abha Saudi Arabia Cyber Security Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh United Kingdom

The ever-increasing security vulnerabilities in the Internet-of-Things (loT) systems require improved threat detection approaches. This paper presents a compact and efficient approach to detect botnet attacks by employing an integrated approach that consists of traffic pattern analysis, temporal support learning, and focused feature extraction. The proposed attention-based model benefits from a hybrid CNN-BiLSTM architecture and achieves 99% classification accuracy in detecting botnet attacks utilizing the N-BaloT dataset, while maintaining high precision and recall across various scenarios. The proposed model's performance is further validated by key parameters, such as Mathews Correlation Coefficient and Cohen's kappa Correlation Coefficient. The close-to-ideal results for these parameters demonstrate the proposed model's ability to detect botnet attacks accurately and efficiently in practical settings and on unseen data. The proposed model proved to be a powerful defense mechanism for IoT networks to face emerging security challenges. © 2025, CC BY-NC-ND.

关键词： Bot (Internet)

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