2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 16 , Issue 1 , PP: 101-117, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Hitesh Kumar Sharma 1 * , Samta Jain Goyal 2 , Sumit Kumar 3 , Abhishek Kumar 4
Doi: https://doi.org/10.54216/FPA.160108
As IoT devices increase, accuracy and data security become increasingly crucial. This research recommends a powerful threat detection system that accelerates message responses to improve IoT security. The recommended strategy finds dangers in using many data sources. Our deep learning system is DenseNet. It groups photographs nicely. We show how the approach works using real-world experiments. It has few false positives and negatives and is effective at recognizing items. Through ablation research, we examine how design and component selections impact technique performance. This clarifies the method's fundamentals. The research reveals that feature selection, fusion, and DenseNet design improve the technique. We discuss the need for fine-tuning hyperparameters to improve approaches and monitor more individuals. The strategy makes IoT communities safer and more robust by laying the groundwork for threat detection and response. This approach solves message transmission delay concerns, making the IoT safer. These discoveries may benefit hacking specialists. They improve and speed up IoT security.
Detection , Efficiency , Fusion , IoT , Optimization , Response Time , Security , Threat , Timeliness , Validation.
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