Biblio

Autonomous vehicles (AVs) are capable of making driving decisions autonomously using multiple sensors and a complex autonomous driving (AD) software. However, AVs introduce numerous unique security challenges that have the potential to create safety consequences on the road. Security mechanisms require a benchmark suite and an evaluation framework to generate comparable results. Unfortunately, AVs lack a proper benchmarking framework to evaluate the attack and defense mechanisms and quantify the safety measures. This paper introduces BenchAV – a security benchmark suite and evaluation framework for AVs to address current limitations and pressing challenges of AD security. The benchmark suite contains 12 security and performance metrics, and an evaluation framework that automates the metric collection process using Carla simulator and Robot Operating System (ROS).

Video Conferencing has emerged as a new paradigm of communication in the age of COVID-19 pandemic. This technology is allowing us to have real-time interaction during the social distancing era. Even before the current crisis, it was increasingly commonplace for organizations to adopt a video conferencing tool. As people adopt video conferencing tools and access data with potentially less secure equipment and connections, meetings are becoming a target to cyber attackers. Enforcing appropriate security and privacy settings prevents attackers from exploiting the system. To design the video conferencing system's security and privacy model, an exhaustive threat model must be adopted. Threat modeling is a process of optimizing security by identifying objectives, vulnerabilities, and defining the plan to mitigate or prevent potential threats to the system. In this paper, we use the widely accepted STRIDE threat modeling technique to identify all possible risks to video conferencing tools and suggest mitigation strategies for creating a safe and secure system.

6LoWPAN is a widely used protocol for communication over IPV6 Low-power Wireless Personal Area Networks. Unfortunately, the 6LoWPAN packet fragmentation mechanism possesses vulnerabilities that adversaries can exploit to perform network attacks. Lack of fragment authentication, payload integrity verification, and sender IP address validation lead to fabrication, duplication, and impersonation attacks. Moreover, adversaries can abuse the poor reassembly buffer management technique of the 6LoWPAN layer to perform buffer exhaustion and selective forwarding attacks. In this paper, we propose SecuPAN - a security scheme for mitigating fragmentation-based network attacks in 6LoWPAN networks and devices. We propose a Message Authentication Code based per-fragment integrity and authenticity verification scheme to defend against fabrication and duplication attacks. We also present a mechanism for computing datagram-tag and IPv6 address cryptographically to mitigate impersonation attacks. Additionally, our reputation-based buffer management scheme protects 6LoWPAN devices from buffer reservation attacks. We provide an extensive security analysis of SecuPAN to demonstrate that SecuPAN is secure against strong adversarial scenarios. We also implemented a prototype of SecuPAN on Contiki enabled IoT devices and provided a performance analysis of our proposed scheme.