Volume 8 , Issue 2 , PP: 67-80, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Abedallah Z. Abualkishik 1 * , Rasha Almajed 2
Doi: https://doi.org/10.54216/IJWAC.080207
The Internet of Things (IoT) represents important security vulnerabilities, increasing difficulties in cyberattacks. Attackers employ these vulnerabilities to establish distributed denial-of-service (DDoS) attacks, compromising availability and causing financial losses to digital platforms. Newly, numerous Machine Learning (ML) and Deep Learning (DL) approaches have been presented for the identification of botnet attacks in IoT networks. By analyzing the patterns of communication and behavior of IoT devices, DL algorithms will be differentiated between malicious and normal activity, therefore supporting the earlier detection and avoidance of botnet attacks. This is essential to protect the integrity and security of IoT systems that can be increasingly vulnerable to botnet-driven attacks because of their limited security measures and often large-scale applications. In this aspect, this study designs an innovative tunicate swarm algorithm with stacked deep learning for botnet detection (TSASDL-BD) technique for IoT platforms. The purpose of the TSASDL-BD technique is to recognize the botnets and achieve maximum network security. In the TSASDL-BD technique, the TSA is applied for the effectual feature selection process, which aids in reducing the dimensionality problem. For botnet detection, the TSASDL-BD technique makes use of the stacked long short-term memory gated recurrent unit (SLSTM-GRU) model. Finally, the artificial humming algorithm (AHA) can be used for the optimal selection of the hyperparameter values of the SLSTM+GRU system. The outcome analysis of the TSASDL-BD method on the benchmark database takes place. The extensive outcomes stated that the TSASDL-BD approach gains maximum detection results over other algorithms with respect of different measures
Internet of Things , Intrusion Detection System , Denial-of-Service , Artificial Humming Algorithm , Feature selection
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