Abstract
Internet of Things is an emerging technology in the modern world and its network is expanding constantly. Meanwhile, IoT devices are a soft target and vulnerable to attackers. The battle between malware attackers and security analysts is persistent and everlasting. Because malware is evolving constantly and thus asserting pressure on researchers and security analysts to cope up with modern threats by improving their defense systems. Complexity and diversity of current malicious software present immense challenges for protecting IoT networks from malware attacks. In this paper, we have explored the potential of neural networks for detection and classification of malware using IoT network dataset comprising of total 4,61,043 records with 3,00,000 as benign while 1,61,043 as malicious. With the proposed methodology, malware is detected with an accuracy of 94.17% while classified with 97.08% accuracy.