When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems
Many research papers have recently focused on behavioral-based driver authentication systems in vehicles. Pushed by Artificial Intelligence (AI) advancements, these works propose powerful models to identify drivers through their unique biometric behavior. However, practitioners have not yet shown any interest in the topic. Indeed, several limitations and oversights make implementing the state-of-the-art impractical, such as the computational resources required for training and the management of false positives. Furthermore, while being proposed as security measures, researchers neglect possible attacks on these systems that can make them counterproductive. Driven by the significant gap between research and practical application, this paper seeks to connect these two domains. We develop two lightweight behavioral-based driver authentication systems based on Machine Learning (ML) and Deep Learning (DL) architectures designed for our constrained environments. We formalize a realistic system and threat model reflecting a real-world vehicle's network for their implementation. When evaluated on real driving data, our models outclass the state-of-the-art with an accuracy of up to 0.999 in identification and authentication. Motivated by the inherent vulnerabilities of ML and DL models, we are the first to propose GAN-CAN, a class of novel evasion attacks, showing how attackers can still exploit these systems with a perfect attack success rate (up to 1.000). Our attacks are effective under different assumptions on the attacker's knowledge and allow stealing a vehicle in less than 22 minutes. Finally, we formalize requirements for deploying driver authentication systems securely and avoiding attacks such as GAN-CAN. Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
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ACM/IEEE第23屆模型驅動工程語言和系統國際會議，是模型驅動軟件和系統工程的首要會議系列，由ACM-SIGSOFT和IEEE-TCSE支持組織。自1998年以來，模型涵蓋了建模的各個方面，從語言和方法到工具和應用程序。模特的參加者來自不同的背景，包括研究人員、學者、工程師和工業專業人士。MODELS 2019是一個論壇，參與者可以圍繞建模和模型驅動的軟件和系統交流前沿研究成果和創新實踐經驗。今年的版本將為建模社區提供進一步推進建模基礎的機會，并在網絡物理系統、嵌入式系統、社會技術系統、云計算、大數據、機器學習、安全、開源等新興領域提出建模的創新應用以及可持續性。
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The development of unmanned aerial vehicles (UAVs) has been gaining momentum in recent years owing to technological advances and a significant reduction in their cost. UAV technology can be used in a wide range of domains, including communication, agriculture, security, and transportation. It may be useful to group the UAVs into clusters/flocks in certain domains, and various challenges associated with UAV usage can be alleviated by clustering. Several computational challenges arise in UAV flock management, which can be solved by using machine learning (ML) methods. In this survey, we describe the basic terms relating to UAVS and modern ML methods, and we provide an overview of related tutorials and surveys. We subsequently consider the different challenges that appear in UAV flocks. For each issue, we survey several machine learning-based methods that have been suggested in the literature to handle the associated challenges. Thereafter, we describe various open issues in which ML can be applied to solve the different challenges of flocks, and we suggest means of using ML methods for this purpose. This comprehensive review may be useful for both researchers and developers in providing a wide view of various aspects of state-of-the-art ML technologies that are applicable to flock management.
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