Alohali Manal Abdullah, Alahmari Sultan, Aljebreen Mohammed, Asiri Mashael M, Miled Achraf Ben, Albouq Sami Saad, Alrusaini Othman, Alqazzaz Ali
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
King Abdul Aziz City for Science and Technology (KACST), Cybersecurity Institute, Riyadh, Saudi Arabia.
Sci Rep. 2025 Jul 1;15(1):20615. doi: 10.1038/s41598-025-00269-y.
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.
车联网（IoV）是一个在开放无线环境中交换数据的多节点网络。车联网实体之间会发生大量交互活动以共享重要信息，这对网络运行至关重要。作为智能交通的一部分，车联网是研究人员的热门话题，因为它面临众多未解决的挑战，尤其是在隐私和安全方面。随着数字服务使用的不断扩展，近期恶意软件的发展增加了因恶意软件威胁而导致数据被盗、数据损坏或其他网络犯罪的可能性。因此，必须提前察觉恶意软件。它会影响大量计算机。在过去几年中，研究人员提出了许多恶意软件检测解决方案。基于机器学习（ML）和深度学习（DL）的检测模型可以减少分析时间并提高恶意软件检测准确率。本研究提出了一种基于深度学习的可解释人工智能的车联网恶意软件检测模型（MDMIoV-DLXAI）。MDMIoV-DLXAI模型的主要目的是通过利用先进的两层优化模型来增强车联网中的恶意软件检测和分类模型。首先，通过最小-最大归一化执行数据归一化阶段，将输入数据转换为有益的格式。此外，所提出的MDMIoV-DLXAI模型利用爬行动物搜索算法（RSA）模型进行特征选择。此外，双向长短期记忆与多头自注意力（BiLSTM-MHSA）模型的混合用于恶意软件分类过程。通过鹈鹕优化算法（POA）执行参数调整过程，以提高BiLSTM-MHSA分类器的分类性能。最后，使用SHAP作为一种可解释人工智能技术来增强人工智能驱动的安全系统的恶意软件检测和决策过程。在恶意软件数据集下对MDMIoV-DLXAI方法进行了实验评估。MDMIoV-DLXAI方法的比较研究表明，其准确率比现有技术高出97.393%。
Zotero 插件
