International Journal of Science, Engineering and TechnologyAuthors: Associate Professor K.Jagadeesh, Betapudi Gagana, Kota Chekitha Nagalakshmi, Beeraka Yasaswi Subhagatri, Kunduru Keerthi
Abstract: The number of Internet of Things (IoT) devices has exponentially increased the amount of fresh data from real-time sources in various fields such as smart cities, healthcare, manufacturing, and critical infrastructure. Nevertheless, the problem of handling and interpreting the huge volume of diverse data produced by these devices still stands, especially when it comes to promptly identifying threats and reacting to them. This paper presents a merged IoT-cloud threat response system that interconnects the heterogeneous IoT sensors with the cloud-based processing accompanied by machine learning techniques to sense the irregularities and foresee the security risks even before they emerge. At the same time, edge computing is cutting down on the delay and improving the responsiveness. Notably, the system is overcoming the issues of infinite scalability, data diversification, communication lags, and security loopholes via the effective handling of data, the initializing of self-adjusting learning models, and the use of the safe transmission protocols. The experimental results confirm the system's superiority in terms of detection accuracy, response time, and operational efficiency over the competitive methods. As the discussed scheme makes threat management more proactive and predictive, it can be considered as a versatile and scalable next-gen solution for smart surveillance and cybersecurity scenarios.
