: In the recent years IOT becomes an emerging technology it provides high to accessibility wireless network and at the same time it produces privacy issues. The devices using IoT are needs to be connected to the internet constantly. Due to this, it becomes vulnerable to the malware which makes easier to the hackers to access the devices. Malware or malicious software is designed to cause the device or network. It introduce evasive techniques like polymorphic or metamorphic malware which can change its behavior frequently and it is difficult to predict . Malware classification can be carried out to understand about the malware by categorizing the datasets. The malware classification acts like a preventive technique to the malware behavior and it's types can be analyzed. To deal with security concerns deep learning techniques are launched. Deep learning works with neural networks which imitates how human train and gain knowledge. The datasets are trained and tested. The RNN- LSTM classifier is used for classification of malware because of its good prediction and the evaluation metrics are increased efficiently. The evaluation metrics such as accuracy, recall, precision, F1Score, and confusion matrix are evaluated with better performance.
Keywords:Accessibility, Malware, Metamorphic, Polymorphic, RNN-LSTM.