Social distancing (SD) is an effective measure to prevent the spread of the infectious Coronavirus Disease 2019 (COVID-19). However, a lack of spatial awareness may cause unintentional violations of this new measure. Against this backdrop, we propose an active surveillance system to slow the spread of COVID-19 by warning individuals in a region-of-interest. Our contribution is twofold. First, we introduce a vision-based real-time system that can detect SD violations and send non-intrusive audio-visual cues using state-of-the-art deep-learning models. Second, we define a novel critical social density value and show that the chance of SD violation occurrence can be held near zero if the pedestrian density is kept under this value. The proposed system is also ethically fair: it does not record data nor target individuals, and no human supervisor is present during the operation. The proposed system was evaluated across real-world datasets.

Modern automobiles are equipped with connectivity features to enhance the user’s comfort. Bluetooth is one such communication technology that is used to pair a personal device with an automotive infotainment unit. Upon pairing, the user could access the personal information on the phone through the automotive head unit with minimum distraction while driving. However, such connectivity introduces a possibility for privacy attacks. Hence, performing an in-depth analysis of the system with privacy constraints is extremely important to prevent unauthorized access to personal information. In this work, we perform a systematic analysis of the Bluetooth network of an automotive infotainment unit to exploit security and privacy-related vulnerabilities. We model the identified threat with respect to privacy constraints of the system, emphasize the severity of attacks through a standardized rating metric and then provide potential countermeasures to prevent the attack. We perform System Theoretic Process Analysis for Privacy as a part of the systematic analysis and use the Common Vulnerability Scoring System to derive attack severity. The identified vulnerabilities are due to design flaws and assumptions on Bluetooth protocol implementation on automotive infotainment systems. We then elicit the vulnerability by performing a privacy attack on the Automotive system in an actual vehicle. We use Android Open-Source Project to report our findings and propose defense strategies.

Security risk analysis and quantification for automotive systems is a challenging task. This challenge is exacerbated when physical systems are integrated with computation and communication networks to form Cyber-Physical Systems (CPS). The complexity arises from the multitude of attack possibilities within the overall system. This work proposes an attack index based on redundancy in the system and the computational sequence of residual generators. This work considers a nonlinear dynamic model of an automotive system with a communication network. The approach involves using system dynamics to model attack vectors, which are based on the vulnerabilities in the system that are exploited through open network components (like On-Board-Diagnosis (OBD-II)), network segmentation (due to improper gateway implementation), and sensors that are susceptible to adversarial attacks. The redundant and non-redundant parts of the system are identified by considering the sensor configuration and unknown variables. Then, an attack index is derived by analyzing the placement of attack vectors in relation to the redundant and non-redundant parts, using the canonical decomposition of the structural model. The security implications of the residuals are determined by analyzing the computational sequence and the placement of the sensors. Thus, this work promotes the notion of security by design by proposing sensor placement strategies to enhance the overall security index. Finally, it is verified how the proposed attack index and its analysis could be used to enhance automotive security using Model-In-Loop (MIL) simulations.