Abstract: Security of IoT devices is a critical concern due to the widespread use of interconnected devices. These devices, ranging from smart thermostats to security cameras, often lack robust security measures, creating vulnerabilities that malicious actors can exploit. The primary risk is the potential for unauthorized access, allowing attackers to take control of devices, steal sensitive data, and use them to launch network attacks. In this paper, an ideal framework is proposed to detect and classify malware in IoT networks by analyzing traffic features using optimized Machine Learning (ML) algorithms. The recent IoT-23 dataset is applied to evaluate the model’s performance. Traditional approaches often struggle with handling large datasets and accurately categorizing attack traffic. The proposed model not only detects malware but also classifies it into multiple categories. Additionally, upon detecting malware, an alert is generated, ensuring that timely action can be taken for its prevention and mitigation. This enhances security by enabling swift responses to potential threats. The proposed model detects malware with an impressive accuracy of 99%. This exceptional performance and early-stage notification demonstrate the model's efficiency in minimizing the risks of malware and further prevention of threats.

Key words: cybersecurity, internet of things, IoT 23, malware detection, machine learning