Malware Detection in IOT Devices Using Machine Learning: A Comparative Study of Cyber Security

The rapid proliferation of Internet of Things (IoT) devices in smart healthcare, industrial automation, and smart city
applications has significantly increased the vulnerability of IoT ecosystems to cyberattacks. Malware attacks targeting resource constrained IoT devices pose serious threats, including data breaches, service disruption, and large-scale botnet formation. Traditional cybersecurity mechanisms, such as signature-based intrusion detection systems, are inadequate in detecting zero-day and evolving malware. To address these challenges, this study presents a omprehensive comparative analysis of malware detection approaches in IoT devices using machine learning techniques. A synthetic IoT malware dataset is generated to simulate realistic network traffic and device behavior. Multiple cybersecurity approaches, including classical machine learning models, deep learning architectures, and a hybrid CNN–LSTM framework, are implemented and evaluated. The models are
assessed using performance metrics such as ccuracy,precision, recall, F1-score, and detection latency. Experimental results demonstrate that deep learning models outperform traditional approaches, with the hybrid CNN–LSTM model achieving the highest detection accuracy and balanced performance. The findings highlight the effectiveness of hybrid learning architectures
for real-time IoT malware detection and provide insights into deploying intelligent cybersecurity solutions in resource
constrained environments.