2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 16 , Issue 1 , PP: 199-210, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
P. Jagdish Kumar 1 * , S. Neduncheliyan 2
Doi: https://doi.org/10.54216/JISIoT.160117
Internet of things (IoT) is an intelligent combination of embedded systems, cloud computing and wireless communications. However, the data privacy and leakage problems are considered as the major deadlocks for deploying the IoT devices in the real time fields. Nevertheless, the complication of Distributed Denial of Service (DDoS) hazard on the IoT devices recent surge has seen an uptick, making it prone to numerous threat complications. For this reason, prompt detection of these attacks plays a pivotal role to safeguard the user’s data. The AI methodology of Machine and Deep Learning Models engaged for the designing the intelligent systems to provide the secured environment to safeguard the network against the various attacks. However, the computational overhead of deep learning model handicaps to deploy it in the IoT-Cloud environment. To tackle this issue, the present article suggests the novel hybrid learning based detection system called CAT-FEED-NETS that incorporates the Deep feed forward neural networks (DFFNN) where the hyper parameters are tuned by the Cat Swarm Intelligence Algorithms. Comprehensive trials and analysis are performed using NSL-KDD and UNSW datasets and criteria to assess the efficacy of quality measurements such as accuracy, precision recall, F1-score and model building time (MBT) is evaluated and analysed. Evaluation results are weighted against the various DL algorithms with the suggested model exhibiting better results than the other models by producing 0.96 accuracy, 0.956 precision, 0.955 recall and 0.9834s of MBT respectively. The proposed framework had proved its superiority in predicting the cloud attacks than the other existing frameworks.
Internet of things (IoT) , Machine Learning , Deep learning Algorithms , CAT-Swarm Optimization
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