Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are increasingly being deployed across multiple functionalities, ranging from healthcare devices and wearables to critical infrastructures, e.g., nuclear power plants, autonomous vehicles, smart cities, and smart homes. These devices are inherently not secure across their comprehensive software, hardware, and network stacks, thus presenting a large attack surface that can be exploited by hackers. In this article, we present an innovative technique for detecting unknown system vulnerabilities, managing these vulnerabilities, and improving incident response when such vulnerabilities are exploited. The novelty of this approach lies in extracting intelligence from known real-world CPS/IoT attacks, representing them in the form of regular expressions, and employing machine learning (ML) techniques on this ensemble of regular expressions to generate new attack vectors and security vulnerabilities. Our results show that 10 new attack vectors and 122 new vulnerability exploits can be successfully generated that have the potential to exploit a CPS or an IoT ecosystem. The ML methodology achieves an accuracy of 97.4% and enables us to predict these attacks efficiently with an 87.2% reduction in the search space. We demonstrate the application of our method to the hacking of the in-vehicle network of a connected car. To defend against the known attacks and possible novel exploits, we discuss a defense-in-depth mechanism for various classes of attacks and the classification of data targeted by such attacks. This defense mechanism optimizes the cost of security measures based on the sensitivity of the protected resource, thus incentivizing its adoption in real-world CPS/IoT by cybersecurity practitioners.
翻译:网络物理系统(CPS)和互联网电话(IoT)装置正在越来越多地在多种功能之间部署,从医疗设备和可磨损设备到关键基础设施,例如核电厂、自主车辆、智能城市和智能家庭。这些装置在其综合软件、硬件和网络堆叠中本质上是不安全的,从而形成黑客可以利用的巨大攻击面。在文章中,我们展示了一种发现未知系统弱点、管理这些弱点和在利用这种弱点时改进事件反应的创新技术。这一方法的新颖之处在于从已知的真实的CPS/IoT攻击中提取情报,以常规表达的形式代表这些情报,以及使用机器学习技术来进行这种常规表达,以产生新的攻击矢量和安全弱点。我们的结果表明,10个新的攻击矢量和122个新的脆弱性利用可以成功地产生利用CPS或IOT生态系统的潜力。ML方法通过97.4%的精确度,使我们能够以87.2%的准确性预测这些攻击,从而以常规表达的形式来代表这些攻击,从而在搜索中利用我们所知道的C-SR攻击的网络中,从而利用我们所知道的精确地利用了我们所知道的防御机制。