Malware Attacks Detection in IoT Using Recurrent Neural Network (RNN)

Abstract

IoT (Internet of Things) devices are being used more and more in a variety of businesses and for a variety of tasks, such as environmental data collection in both civilian and military situations. They are a desirable attack target for malware intended to infect specific IoT devices due to their growing use in a variety of applications and their increasing computational and processing power. In this study, we investigate the possibility of detecting IoT malware using recurrent neural networks (RNNs). RNN is used in the proposed method to investigate the execution operation codes of ARM-based Internet of Things apps (OpCodes). To train our algorithms, we employ a dataset of IoT applications that includes 281 malicious and 270 benign pieces of software. The trained model is then put to the test using 100 brand-new IoT malware samples across three separate LSTM settings. Model exposure was not previously conducted on these samples. Detecting newly crafted malware samples with 2-layer neurons had the highest accuracy (98.18%) in the 10-fold cross validation experiment. A comparison of the LSTM technique to other machine learning classifiers shows that it yields the best results.