Abstract:
Internet of Things (IoT) systems become more prevalent, and their security problems can be significant. Denial-of-service
attacks, malware, and phishing attacks can compromise data and services on networks. For comprehensive protection, machine
learning-based security measures in IoT systems should be developed with more robust models and integrated with multiple security
measures. A Ridge Classifier is used to detect anomalies in this study. With this approach, the proposed system can detect and predict
cyber-attacks accurately in smart networks using secure real-time information. In IoT systems, it detected and mitigated network threats
with a 97percent accuracy rate. In addition to improving the security and resilience of government and business networks, this system
can also protect the data from malicious threats..
