Machine Learning Method for Reliable Malware Detection in IoT using Opcode Category Features

The number of threats has grown as a result of the Internet of Things’ (IoT) rapid expansion, making effective malware identification and classification techniques necessary. The goal of this project is to improve IoT malware detection systems (MDS) and categorization by utilizing machine learning techniques and opcode category properties. Using neural network methods, the method examines opcode sequences taken out of IoT device firmware. These sequences are converted into features of the opcode category, which are then fed into the CNN model. By means of comprehensive training and assessment, the model acquires the ability to differentiate between benign and dangerous opcode patterns, hence facilitating precise malware identification and classification. The malware identification and categorization system that utilizes CNN technology demonstrates encouraging performance metrics, attaining elevated levels of accuracy, precision, and recall. In particular, the model outperforms other machine learning techniques as well as conventional signature-based methods in identifying and classifying various forms of IoT malware. The results of this study highlight how well CNN algorithms and opcode category features work together to provide reliable IoT malware identification and classification. This method strengthens defense against new hazards in the IoT ecosystem and improves cybersecurity measures by precisely recognizing malicious behaviors within the firmware of IoT devices.