The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.
翻译:主计长地区网络(CAN)公交车是实时车辆网络系统(IVN)中简单、合适和稳健建筑的重要协议,由于复杂的数据密集结构大大增加了未经授权的网络的可及性和各种网络攻击的可能性,IVN装置的风险仍然不安全和脆弱,因此,检测IVN装置的网络攻击越来越引起人们的兴趣。随着IVN装置的迅速发展和不断演变的威胁类型,传统的机器学习数据库必须更新,以应对当前环境的安全需要。如今,深层学习、深层转移学习及其在若干领域的影响性结果的演变,作为网络入侵探测的有效解决办法,指导了深入学习、深层转移学习和效果。这一手稿提议为IV网络提出了深度转移基于学习的IDS模式,同时与其他现有模式相比,提高了绩效。独特的贡献包括有效选择属性,这最适合识别恶意的CPN信息并准确检测正常和不正常的活动,设计了深层传输的LNet模式,并评估了现实世界数据。为这一目的,广泛的实验性业绩分析与深层次的测试模型一起,进行了广泛的实验性测试,并展示了深层次的测试。