No abstract

Sensors play an integral role in numerous devices across a diverse range of domains. While cyber-physical systems and the Internet of things use them extensively, sensors can also be commonly found in many standalone electronic devices. Concerns over the susceptibility of sensors to malicious attacks have led academia to focus on the security of these sensors. To help unite these efforts, we propose a lexicon to easily differentiate between types and methods of attacks on sensors. Using these definitions, one can quickly and clearly understand the method and the target of an attack. We examine the most recent and influential attacks on sensors, especially when they are acting as edge nodes of systems, as well as defenses against said attacks. We then seek to categorize these methods according to our lexicon, demonstrating its usefulness and solidifying the meaning of proposed terms.

Deep Neural Network (DNN) is gaining popularity thanks to its ability to attain high accuracy and performance in various security-crucial scenarios. However, recent research shows that DNN based Automatic Speech Recognition (ASR) systems are vulnerable to adversarial attacks. Specifically, these attacks mainly focus on formulating a process of adversarial example generation as iterative, optimization-based attacks. Although these attacks make significant progress, they still take large generation time to produce adversarial examples, which makes them difficult to be launched in real-world scenarios. In this paper, we propose a real-time attack framework that utilizes the neural network trained by the gradient approximation method to generate adversarial examples on Keyword Spotting (KWS) systems. The experimental results show that these generated adversarial examples can easily fool a black-box KWS system to output incorrect results with only one inference. In comparison to previous works, our attack can achieve a higher success rate with less than 0.004 seconds. We also extend our work by presenting a novel ensemble audio adversarial attack and testing the attack on KWS systems equipped with existing defense mechanisms. The efficacy of the proposed attack is well supported by promising experimental results.

No abstract

Side Channel Analysis (SCA) presents a clear threat to privacy and security in modern computing systems. The vast majority of communications are secured through cryptographic algorithms. These algorithms are often provably-secure from a cryptographical perspective, but their implementation on real hardware introduces vulnerabilities. Adversaries can exploit these vulnerabilities to conduct SCA and recover confidential information, such as secret keys or internal states. The threat of SCA has greatly increased as machine learning, and in particular deep learning, enhanced attacks become more common. In this work, we will examine the latest state-of-the-art deep learning techniques for side channel analysis, the theory behind them, and how they are conducted. Our focus will be on profiling attacks using deep learning techniques, but we will also examine some new and emerging methodologies enhanced by deep learning techniques, such as non-profiled attacks, artificial trace generation, and others. Finally, different deep learning enhanced SCA schemes attempted against the ANSSI SCA Database (ASCAD) and their relative performance will be evaluated and compared. This will lead to new research directions to secure cryptographic implementations against the latest SCA attacks.