Almakayeel Naif
Department of Industrial Engineering, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia.
Sci Rep. 2024 Oct 24;14(1):25175. doi: 10.1038/s41598-024-74017-z.
With the growing popularity of autonomous vehicles (AVs), confirming their safety has become a significant concern. Vehicle manufacturers have combined the Android operating system into AVs to improve consumer comfort. However, the diversity and weaknesses of the Android operating system pose substantial safety risks to AVs, as these factors can expose them to threats, namely Android malware. The advanced behaviour of multi-data source fusion in autonomous driving models has mitigated recognition accuracy and effectualness for Android malware. To efficiently counter new malware variants, novel techniques distinct from conventional methods must be utilized. Machine learning (ML) techniques cannot detect every new and complex malware variant. The deep learning (DL) model is an efficient tool for detecting various malware variants. This manuscript proposes a Deep Learning-Based Improved Transformer Model on Android Malware Detection (DLBITM-AMD) technique for Internet vehicles (IoVs). The main aim of the presented DLBITM-AMD approach is to detect Android malware effectually and accurately. The DLBITM-AMD method performs a Z-score normalization process to convert the raw data into a standard form. Then, the DLBITM-AMD approach utilizes the binary grey wolf optimization (BGWO) model to select optimum feature subsets. An improved transformer is integrated with the RNN model and softmax to enhance classification for Android malware recognition. Finally, the snake optimizer algorithm (SOA) method is employed to select the optimum parameter for the classification method. An extensive experiment of the DLBITM-AMD method is accomplished on a benchmark dataset. The performance validation of the DLBITM-AMD technique portrayed a superior accuracy value of 99.26% over existing Android malware recognition models.
随着自动驾驶汽车（AVs）越来越受欢迎，确认其安全性已成为一个重大问题。汽车制造商已将安卓操作系统集成到自动驾驶汽车中，以提高消费者的舒适度。然而，安卓操作系统的多样性和弱点给自动驾驶汽车带来了重大安全风险，因为这些因素可能使它们面临威胁，即安卓恶意软件。自动驾驶模型中多数据源融合的先进行为降低了对安卓恶意软件的识别准确性和有效性。为了有效对抗新的恶意软件变种，必须采用不同于传统方法的新技术。机器学习（ML）技术无法检测到每一个新的和复杂的恶意软件变种。深度学习（DL）模型是检测各种恶意软件变种的有效工具。本文提出了一种基于深度学习的改进变压器模型用于互联网车辆（IoVs）的安卓恶意软件检测（DLBITM-AMD）技术。所提出的DLBITM-AMD方法的主要目的是有效且准确地检测安卓恶意软件。DLBITM-AMD方法执行Z分数归一化过程，将原始数据转换为标准形式。然后，DLBITM-AMD方法利用二进制灰狼优化（BGWO）模型选择最优特征子集。一种改进的变压器与循环神经网络（RNN）模型和softmax集成，以增强对安卓恶意软件识别的分类。最后，采用蛇优化算法（SOA）方法为分类方法选择最优参数。在一个基准数据集上完成了DLBITM-AMD方法的广泛实验。DLBITM-AMD技术的性能验证显示，与现有的安卓恶意软件识别模型相比，其准确率高达99.26%。
