Internet of Things (IoT) technology provides the basic infrastructure for a hyper connected society where all things are connected and exchange information through the Internet. IoT technology is fused with 5G and artificial intelligence (AI) technologies for use various fields such as the smart city and smart factory. As the demand for IoT technology increases, security threats against IoT infrastructure, applications, and devices have also increased. A variety of studies have been conducted on the detection of IoT malware to avoid the threats posed by malicious code. While existing models may accurately detect malicious IoT code identified through static analysis, detecting the new and variant IoT malware quickly being generated may become challenging. Due to the complexity of design and implementation in both hardware and software, as well as the lack of security functions and abilities, IoT devices are becoming an attractive target for cyber criminals who take advantage of weak authentication, outdated firmware’s , and malwares to compromise IoT devices .This project provides the light on the system named as malware classification and detection of IOT devices, used to detect the cyber-attacks caused by malware on IOT devices by using machine learning techniques. The malware classification and detection system detect and identifies the various types of malwares using static analysis with the help of machine learning algorithm. An easy-to-use user interface for easy uploading of files and checking for virus is designed. Also, acceptance testing is performed on the application to remove vulnerabilities.

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