Abstract
The fast development of the Internet of Things (IoT) has introduced significant threats due to the growing number of connected devices and the vulnerability of data transmitted. Conventional security systems often fail to address the specific requirements of IoT networks because of the limited resources of individual devices and the complexity of the interconnected ecosystem in which they are implemented. The research introduces SecureNet-IoT, a modern security framework that will strengthen the security of connected systems and networks. The framework uses a tuned Intelligent Random Forest (ASSO-IRF) that uses an ASSO to predict the behaviour of IoT devices and distinguish between various types of attacks. SecureNet-IoT actively detects and neutralizes the possible vulnerabilities within the environments of IoT and fog computing. The information was gathered on IoT devices in smart homes and industrial IoTs, including device communication, communication logs, and network traffic, which were pre-processed with a Kalman filter to eliminate noise and normalization techniques to normalize the data. The model classifies devices as authentic, breached, or fake and uses a suggested model to forecast malicious acts. It approximates the possibilities of transitions between states, thus being able to detect threats early. In addition, the framework also analyses device communications to enhance predictive accuracy. The metrics used to assess performance were accuracy (98.96%), F1-score (96.31%), precision (98.78%), and recall (96.24%). The framework demonstrates the system’s effectiveness in preventing malicious behavior by successfully categorizing device states, estimating transition probabilities, and analyzing device communications, ultimately enhancing IoT system security and integrity.
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