Secure IoT Communication Through Machine Learning-Based Anomaly Detection

Abstract

Cybersecurity threats and network vulnerabilities have expanded dramatically due to the Internet of Things' (IoT) fast expansion in smart homes, healthcare, transportation, and industrial automation. In IoT contexts, traditional security measures and signature-based intrusion detection systems frequently fail to detect novel and changing cyberthreats. This study proposes a machine learningbased anomaly detection and attack categorization framework for secure IoT networks in order to tackle these issues. In order to identify anomalous behavior and categorize malicious activity, the suggested system continuously monitors IoT network traffic, preprocesses data, extracts features, and uses machine learning algorithms like Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Denial of Service (DoS), Distributed Denial of Service (DDoS), virus assaults, reconnaissance attacks, botnet operations, and unauthorized access attempts are just a few of the cyberattacks that the framework can detect. Through clever categorization techniques, the system increases real-time threat analysis, reduces false positives, and improves detection accuracy. The suggested method offers dependable performance in differentiating between legitimate and malicious communication patterns in IoT settings, according to experimental study. The created framework provides a scalable, economical, and effective way to improve IoT security and shield linked devices from contemporary cyberthreats.