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The smart city landscape is rife with opportunities for mobility and economic optimization, but also presents many security concerns spanning the range of components and systems in the smart ecosystem. One key enabler for this ecosystem is smart transportation and transit, which is foundationally built upon connected vehicles. Ensuring vehicular security, while necessary to guarantee passenger and pedestrian safety, is itself challenging due to the broad attack surfaces of modern automotive systems. A single car contains dozens to hundreds of small embedded computing devices known as electronic control units (ECUs) executing 100s of millions of lines of code; the inherent complexity of this tightly-integrated cyber-physical system (CPS) is one of the key problems that frustrates effective security. We describe an approach to help reduce the complexity of security analyses by leveraging unsupervised machine learning to learn clusters of messages passed between ECUs that correlate with changes in the CPS state of a vehicle as it moves through the world. Our approach can help to improve the security of vehicles in a smart city, and can leverage smart city infrastructure to further enrich and refine the quality of the machine learning output. 

The automotive Controller Area Network (CAN) allows Electronic Control Units (ECUs) to communicate with each other and control various vehicular functions such as engine and braking control. Consequently CAN and ECUs are high priority targets for hackers. As CAN implementation details are held as proprietary information by vehicle manufacturers, it can be challenging to decode and correlate CAN messages to specific vehicle operations. To understand the precise meanings of CAN messages, reverse engineering techniques that are time-consuming, manually intensive, and require a physical vehicle are typically used. This work aims to address the process of reverse engineering CAN messages for their functionality by creating a machine learning classifier that analyzes messages and determines their relationship to other messages and vehicular functions. Our work examines CAN traffic of different vehicles and standards to show that it can be applied to a wide arrangement of vehicles. The results show that the function of CAN messages can be determined without the need to manually reverse engineer a physical vehicle. 

Changing Electrical and Computer Engineering Department Culture from the Bottom Up: Action Plans Generated from Faculty Interviews We prefer a Lessons Learned Paper. In a collaborative effort between a RED: Revolutionizing Engineering and Computer Science Departments (RED) National Science Foundation grant awarded to an electrical and computer engineering department (ECpE) and a broader, university-wide ADVANCE program, ECpE faculty were invited to participate in focus groups to evaluate the culture of their department, to further department goals, and to facilitate long-term planning. Forty-four ECpE faculty members from a large Midwestern university participated in these interviews, which were specifically focused on departmental support and challenges, distribution of resources, faculty workload, career/family balance, mentoring, faculty professional development, productivity, recruitment, and diversity. Faculty were interviewed in groups according to rank, and issues important to particular subcategories of faculty (e.g., rank, gender, etc.) were noted. Data were analyzed by a social scientist using the full transcript of each interview/focus group and the NVivo 12 Qualitative Research Software Program. She presented the written report to the entire faculty. Based on the results of the focus groups, the ECpE department developed an action plan with six main thrusts for improving departmental culture and encouraging departmental change and transformation. 1. Department Interactions – Encourage open dialogue and consider department retreats. Academic areas should be held accountable for the working environment and encouraged to discuss department-related issues. 2. Mentoring, Promotion, and Evaluation – Continue mentoring junior faculty. Improve the clarity of P&T operational documents and seek faculty input on the evaluation system. 3. Teaching Loads – Investigate teaching assistant (TA) allocation models and explore models for teaching loads. Develop a TA performance evaluation system and return TA support to levels seen in the 2010 timeframe. Improvements to teaching evaluations should consider differential workloads, clarifying expectations for senior advising, and hiring more faculty for undergraduate-heavy areas. 4. Diversity, Equity, and Inclusion – Enact an explicit focus on diversity in hiring. Review departmental policies on inclusive teaching and learning environments. 5. Building – Communicate with upper administration about the need for a new building. Explore possibilities for collaborations with Computer Science on a joint building. 6. Support Staff – Increase communication with the department regarding new service delivery models. Request additional support for Human Resources, communications, and finance. Recognize staff excellence at the annual department banquet and through college/university awards. 

A modern Graphics Processing Unit (GPU) utilizes L1 Data (L1D) caches to reduce memory bandwidth requirements and latencies. However, the L1D cache can easily be overwhelmed by many memory requests from GPU function units, which can bottleneck GPU performance. It has been shown that the performance of L1D caches is greatly reduced for many GPU applications as a large amount of L1D cache lines are replaced before they are re-referenced. By examining the cache access patterns of these applications, we observe L1D caches with low associativity have difficulty capturing data locality for GPU applications with diverse reuse patterns. These patterns result in frequent line replacements and low data re-usage. To improve the efficiency of L1D caches, we design a Dynamic Line Protection scheme (DLP) that can both preserve valuable cache lines and increase cache line utilization. DLP collects data reuse information from the L1D cache. This information is used to predict protection distances for each memory instruction at runtime, which helps maintain a balance between exploitation of data locality and over-protection of cache lines with long reuse distances. When all cache lines are protected in a set, redundant cache misses are bypassed to reduce the contention for the set. The evaluation result shows that our proposed solution improves cache hits while reducing cache traffic for cache-insufficient applications, achieving up to 137% and an average of 43% IPC improvement over the baseline. 

The landscape of automotive in-vehicle networks is changing driven by the vast options for infotainment features and progress toward fully-autonomous vehicles. However, the security of automotive networks is lagging behind feature-driven technologies, and new vulnerabilities are constantly being discovered. In this paper, we introduce a road map towards a security solution for in-vehicle networks that can detect anomalous and failed states of the network and adaptively respond in real-time to maintain a fail-operational system.