Walter Scott, Jr. College of Engineering

Graduate Exam Abstract

Vipin Kumar Kukkala
Ph.D. Final
Jan 26, 2022, 11:00 am - 1:00 pm
Abstract: Modern vehicles are examples of complex cyber-physical systems with tens to hundreds of interconnected Electronic Control Units (ECUs) that manage various vehicular subsystems. With the shift towards autonomous driving, emerging vehicles are being characterized by an increase in the number of hardware ECUs, greater complexity of applications (software), and more sophisticated in-vehicle networks. These advances have resulted in numerous challenges that impact the reliability, security, and real-time performance of these emerging automotive systems. Some of the challenges include coping with computation and communication uncertainties (e.g., jitter), developing robust control software, detecting cyber-attacks, ensuring data integrity, and enabling confidentiality during communication. However, solutions to overcome these challenges incur additional overhead, which can catastrophically delay the execution of real-time automotive tasks and message transfers. Hence, there is a need for a holistic approach to a system-level solution for resource management in automotive cyber-physical systems that enables robust and secure automotive system design while satisfying a diverse set of system-wide constraints.
ECUs in vehicles today run a variety of automotive applications ranging from simple vehicle window control to highly complex Advanced Driver Assistance System (ADAS) applications. The aggressive attempts of automakers to make vehicles fully autonomous have increased the complexity and data rate requirements of applications and further led to the adoption of advanced artificial intelligence (AI) based techniques for improved perception and control. Additionally, modern vehicles are becoming increasingly connected with various external systems to realize more robust vehicle autonomy. These paradigm shifts have resulted in significant overheads in resource constrained ECUs and increased the complexity of the overall automotive system (including heterogeneous ECUs, network architectures, communication protocols, and applications), which has severe performance and safety implications on modern vehicles. The increased complexity of automotive systems introduces several computation and communication uncertainties in automotive subsystems that can cause delays in applications and messages, resulting in missed real-time deadlines. Missing deadlines for safety-critical automotive applications can be catastrophic, and this problem will be further aggravated in the case of future autonomous vehicles. Additionally, due to the harsh operating conditions (such as high temperatures, vibrations, electromagnetic interference (EMI), etc.) of automotive embedded systems, there is a significant risk to the integrity of the data that is exchanged between ECUs which can lead to faulty vehicle control. These challenges demand a more reliable design of automotive systems that is resilient to uncertainties and supports data integrity goals. Additionally, the increased connectivity of modern vehicles has made them highly vulnerable to various kinds of sophisticated security attacks. Hence, it is also vital to ensure the security of automotive systems, and it will become crucial as connected and autonomous vehicles become more ubiquitous. However, imposing security mechanisms on the resource constrained automotive systems can result in additional computation and communication overhead, potentially leading to further missed deadlines. Therefore, it is crucial to design techniques that incur very minimal overhead (lightweight) when trying to achieve the above-mentioned goals and ensure the real-time performance of the system.
We address these issues by designing a holistic resource management framework called ROSETTA that enables robust and secure automotive cyber-physical system design while satisfying a diverse set of constraints related to reliability, security, real-time performance, and energy consumption. To achieve reliability goals, we have developed several techniques for reliability-aware scheduling and multi-level monitoring of signal integrity. To achieve security objectives, we have proposed a lightweight security framework that provides confidentiality and authenticity while meeting both security and real-time constraints. We have also introduced multiple deep learning based intrusion detection systems (IDS) to monitor and detect cyber-attacks in the in-vehicle network. Lastly, we have introduced novel techniques for jitter management and security management and deployed lightweight IDSs on resource constrained automotive ECUs while ensuring the real-time performance of the automotive systems.
Adviser: Dr. Sudeep Pasricha
Co-Adviser: N/A
Non-ECE Member: Dr. Thomas Bradley
Member 3: Anthony Maciejewski
Addional Members: Ali Pezeshki
Journal publications:
• V. K. Kukkala, S. Pasricha, T. Bradley, “Advanced Driver-Assistance Systems: A path toward autonomous vehicles,” in IEEE Consumer Electronics Magazine (CEM), Vol. 7, Iss. 5, September, 2018.
• V. K. Kukkala, S. Pasricha, T. Bradley, “JAMS-SG: A Framework for Jitter-Aware Message Scheduling for Time-Triggered Automotive Networks,” in ACM Transactions on Design Automation of Electronic Systems (TODAES), Vol. 24, Iss. 6, September, 2019.
• V. Kukkala, S. Pasricha, T. Bradley, “SEDAN: Security-Aware Design of Time-Critical Automotive Networks,” in IEEE Transaction on Vehicular Technology (TVT), Vol. 69, Iss. 8, August, 2020.
• V. K. Kukkala, S. V. Thiruloga, S. Pasricha, “INDRA: Intrusion Detection using Recurrent Autoencoders in Automotive Embedded Systems,” in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), Vol. 39, Iss. 11, November, 2020.
• V. K. Kukkala, S. V. Thiruloga, S. Pasricha, “LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platforms,” in ACM Transactions on Embedded Computing Systems (TECS), Vol. 20, No. 5s, Article 67, August, 2021.
• V. K. Kukkala, S. V. Thiruloga, S. Pasricha, “Roadmap for Cybersecurity in Autonomous Vehicles,” in IEEE Consumer Electronics Magazine (CEM) (under review), 2022.

Conference publications:
• V. K. Kukkala, T. Bradley, S. Pasricha, "Priority-based Multi-level Monitoring of Signal Integrity in a Distributed Powertrain Control System,” in 4th IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling, July, 2015.
• V. K. Kukkala, T. Bradley, S. Pasricha, "Uncertainty Analysis and Propagation for an Auxiliary Power Module,” in IEEE Transportation Electrification Conference (TEC), June, 2017.
• V. K. Kukkala, S. Pasricha, T. Bradley, "JAMS: Jitter-Aware Message Scheduling for FlexRay Automotive Networks,” in IEEE/ACM International Symposium on Network-on-Chip (NOCS), October, 2017.
• G. C. DiDomenico, J. Bair, V. K. Kukkala, et al., “Colorado State University EcoCAR 3 Final Technical Report,” in SAE World Congress Experience (WCX), April, 2019.
• S. V. Thiruloga, V. K. Kukkala, S. Pasricha, “TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems,” in IEEE/ACM Asia & South Pacific Design Automation Conference (ASPDAC), January, 2022.

Book Chapters:
• V. K. Kukkala, S. V. Thiruloga, S. Pasricha, “AI for Cybersecurity in Distributed Automotive IoT Systems,” in Electronic Design for AI, IoT and Hardware Security (to appear), Springer, 2022.
Program of Study:
CS 545
CS 556
ECE 520
ECE 554
ECE 561
ECE 571
ECE 612
ECE 666