Two stage malware detection model in internet of vehicles (IoV) using deep learning-based explainable artificial intelligence with optimization algorithms

Internet of Vehicles (IoV) is a multi-node network which switches data in an open and wireless environment. Numerous interaction activities occur among IoV entities to share significant information, which is essential for network operation. As part of intellectual transportation, IoV is a hot topic for researchers because it faces numerous unresolved challenges, particularly regarding privacy and security. The development of recent malicious software with the expanding use of digital services has increased the likelihood of stealing data, corrupting data, or other cybercrimes by malware threats. Hence, malicious software should be perceived previously. It impacts a vast amount of computers. Researchers have proposed numerous malware detection solutions for the past few years. Machine learning (ML) and deep learning (DL)-based detection models can decrease analysis time and increase malware detection accuracy. This study proposes a novel Malware Detection Model in the Internet of Vehicles Using Deep Learning-Based Explainable Artificial Intelligence (MDMIoV-DLXAI). The main intention of the MDMIoV-DLXAI model is to enhance the malware detection and classification model in IoV by utilizing advanced two-tier optimization models. Initially, the data normalization stage is performed by the min-max normalization to convert input data into a beneficial format. Besides, the proposed MDMIoV-DLXAI model utilizes the reptile search algorithm (RSA) model for feature selection. Furthermore, the hybrid of bidirectional long short-term memory with a multi-head self-attention (BiLSTM-MHSA) model is employed for the malware classification process. The parameter tuning process is performed through the pelican optimization algorithm (POA) to improve the classification performance of the BiLSTM-MHSA classifier. Finally, SHAP is utilized as an XAI technique to enhance malware detection and decision-making processes of AI-driven security systems. The experimental evaluation of the MDMIoV-DLXAI method is examined under the malware dataset. The comparison study of the MDMIoV-DLXAI method demonstrated a superior accuracy value of 97,393% over existing techniques.