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DTSTART;TZID=America/Los_Angeles:20230501T120000
DTEND;TZID=America/Los_Angeles:20230501T140000
DTSTAMP:20231011T032408Z
CREATED:20231011T032408Z
LAST-MODIFIED:20231011T032408Z
UID:14155-1682942400-1682949600@www.cecs.uci.edu
SUMMARY:Cross-Layer Attacks and Defenses in the Cyber-Physical System (CPS)
DESCRIPTION:Name: Anomadarshi Barua\nChair: Mohammad Abdullah Al Faruque \nDate: May 1\, 2023 \nTime: 12 PM – 2 PM \nLocation: EH 2430-Harut Barsamian \nCommittee: Pramod P Khargonekar\, Zhou Li \nTitle: Cross-Layer Attacks and Defenses in the Cyber-Physical System (CPS) \nAbstract: \nHave you ever wondered how simple music can leak deadly pathogens from a bio-safety lab? Have you ever considered how an industrial robotic arm can be compromised from a cloud infrastructure? Have you ever imagined an attacker could use a magnetic field to shut down a weak microgrid?  If the above questions excite you\, please attend my PhD oral defense\, where my research answers the above questions with a practical demonstration of CPS attacks on industry-used infrastructure. With this seminar\, the audience will gain a deep insight into how hardware/software interaction in CPS’s cross-layer can open a “Pandora’s Box” of unknown threats that can come from very unconventional ways. \nMy research sits at the intersection of hardware and software cross-layers in CPSs\, exploring how interactions of cyber and physical components can affect the safety and controllability of closed-loop control from sensing to actuation. Most attacks on CPSs can be propagated from the physical domain to the cyber domain or vice-versa and hence\, can be termed cross-layer attacks. To understand these cross-layer attacks\, a very different set of methodologies and tools is needed. My research talks about how these tools can be made. Moreover\, the seminar will show how cross-layer vulnerabilities can be handled using new hardware/software co-design approaches to detect\, contain and isolate threats in CPSs. \nShort Bio: \nAnomadarshi Barua is a Ph.D. candidate studying Computer Engineering at the University of California Irvine. He received his M.S. degree in Embedded Computing Systems jointly at the University of Southampton (UoS) and the Norwegian University of Science and Technology (NTNU) in 2016. Now\, He is working under the supervision of Professor Al Faruque in the Autonomous and Intelligent Cyber-Physical Systems (AICPS) Lab. His current research focuses on the system-level design of embedded and Cyber-Physical-Systems (CPSs) with a special interest in CPS security\, low-power design\, and computer microarchitecture. He publishes in the security conferences like ACM CCS\, USENIX\, ACSAC\, RAID\, HOST\, and CHES and system conferences like ICCPS and ICCAD. He also has worked in several industries in North America and Europe\, such as Intel Corporation (USA)\, Solidigm (USA)\, Nordic Semiconductor (Norway)\, and IDEAS (Norway). In his leisure time\, he likes to travel to new places and go for hiking.
URL:https://www.cecs.uci.edu/event/cross-layer-attacks-and-defenses-in-the-cyber-physical-system-cps/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230505T153000
DTEND;TZID=America/Los_Angeles:20230505T163000
DTSTAMP:20231011T032408Z
CREATED:20231011T032408Z
LAST-MODIFIED:20231011T032408Z
UID:14156-1683300600-1683304200@www.cecs.uci.edu
SUMMARY:Context-Aware and Energy-Efficient Autonomous Systems
DESCRIPTION:Name: Arnav Malawade\nChair: Mohammad Al Faruque \nDate: May 5\, 2023 \nTime: 3:30 PM \nLocation: ISEB 1200 \nCommittee: Marco Levorato\, Yasser Shoukry \nTitle:  Context-Aware and Energy-Efficient Autonomous Systems \nAbstract: \nAutonomous systems (AS) are beginning to play a more significant role in modern society. AS include robotic systems that perceive and react to changes in their environment\, such as aerial drones\, ground/aquatic robots\, and consumer autonomous vehicles (AVs)\, among others. Breakthroughs in deep learning and increased consumer interest in ubiquitous autonomy have fueled recent advances in perception\, modeling\, and control algorithms. However\, these advances come with rising energy costs. Modern AS require large deep-learning (DL) models to perceive the environment and safely detect and avoid objects. The computational demands of these models significantly increase the hardware requirements of the complete system\, such that modern AV systems can require several kilowatts of power to enable autonomy\, reducing range and utility. These impacts affect most AS since AVs\, ground robots\, and drones are typically edge devices that operate in energy-constrained environments. \nThe need for large\, complex DL models is primarily driven by the fact that\, in the real world\, challenging and unpredictable scenarios can occur and must be modeled appropriately by the AS. However\, existing approaches are exceedingly cautious\, preferring to execute a large\, inefficient model even in typical scenarios just in case a difficult edge case arises. This approach to autonomy hinders both the utility of existing AS and the broad-scale adoption of AS. Instead\, an AS should be able to understand the context of its surrounding environment and adapt its model to fit each situation. \nThis dissertation explores methods for developing context-aware AS to improve perception and prediction performance\, reduce energy consumption\, and enable adaptation to dynamic environments. Three different perspectives are studied: (i) scene-graph embedding approaches are proposed and evaluated for improved semantic understanding and state estimation; (ii) methods for implementing split ML models for dynamic\, resource-efficient computation offloading from edge to cloud within real-time latency constraints are studied; and (iii) context-aware\, dynamic ML perception models that jointly optimize performance and energy efficiency are studied from an algorithmic and system-wide optimization perspective. Overall\, the results show that context-aware models achieve state-of-the-art performance across applications\, and dynamic architectures conditioned on context can help resolve the energy-performance trade-off by enabling the joint optimization of these objectives. \nShort Bio: \nArnav Malawade is a PhD Candidate in Computer Engineering and works in the Autonomous and Intelligent Cyber-Physical Systems (AICPS) Lab under the supervision of Professor Mohammad Al Faruque. He received his B.S. in Computer Science and Engineering and his M.S. in Computer Engineering at UC Irvine in 2018 and 2021\, respectively. He was a Doctorate Intern at HRL Laboratories in 2022. His research interests include autonomous driving\, graph-based perception\, dynamic neural networks\, and smart manufacturing.
URL:https://www.cecs.uci.edu/event/context-aware-and-energy-efficient-autonomous-systems/
LOCATION:ISEB 1200\, Interdisciplinary Science and Engineering Building University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230522T090000
DTEND;TZID=America/Los_Angeles:20230522T110000
DTSTAMP:20231011T032434Z
CREATED:20231011T032434Z
LAST-MODIFIED:20231011T032434Z
UID:14157-1684746000-1684753200@www.cecs.uci.edu
SUMMARY:Graph Learning for Robust Embedded and Cyber-Physical Systems
DESCRIPTION:Name: Shih-Yuan Yu\nChair: Mohammad Al Faruque \nDate: May 22\, 2023 \nTime: 9:00 AM – 11:00 AM \nLocation: EH 2430 \nCommittee: Pramod Khargonekar\, Yanning Shen \nTitle: Graph Learning for Robust Embedded and Cyber-Physical Systems \nAbstract: \nSince the appearance of microprocessors\, miscellaneous categories of computer systems\, including Embedded and Cyber-Physical Systems (ECPS)\, have become an integral part of our modern society. In contrast to general-purpose computers\, an ECPS is a computing system designed to perform a dedicated or narrow range of functions with minimal human intervention. Today\, the advanced standards in device networking\, ubiquitous access to the Internet\, miniaturization of processors\, and reduced power consumption have taken this field to the next level. However\, despite these technological growths\, designing a robust ECPS remains an open research challenge where the goal is to achieve better functionalities\, encompass complex\, uncertain\, and changing environments\, and ensure system security. Minor failures on ECPS may cause severe collapses or cyberattacks\, impeding progress toward increasing automation and modernizing our computing environments. \nTo achieve robustness\, this dissertation studies the embedding of Computational Intelligence (CI) into emerging Graph Learning (GL) technologies. The CI paradigms mimic the nature of humans\, aiming at solving complex problems and exhibiting a cognitive ability to learn or adapt to new situations to generalize\, abstract\, discover\, and associate. As a prerequisite of this research direction\, Machine Learning (ML) has become increasingly ubiquitous\, with existing works exploring various fields\, from self-driving vehicles\, facial recognition systems\, and real-time language translation to security surveillance\, innovative home applications\, and health monitoring. However\, conventional ML algorithms typically require appropriate vectorized representations crafted by domain experts to accomplish the desired goals. Graph-structured data have imposed unprecedented challenges on ML due to their inherent complexity. Unlike text\, audio\, and images\, graphs are embedded in an irregular dimension\, making some essential operations of ML inapplicable. GL has attracted much attention to new research ideas in several fields. To date\, many researchers have proven the usefulness of GL in social computing\, information retrieval\, computer vision\, bioinformatics\, economics\, and e-commerce. However\, its applications in subfields of ECPS still need to be explored. \nIn this dissertation\, we will cover GL applications for ECPS in robust Integrated Circuit (IC) design analysis for hardware security\, robust binary analysis for enabling software security\, and ultimately how GL brings the scene-understanding capabilities of autonomous driving systems to the next level. In IC design analysis (Chapter 2)\, we explore how GL can be leveraged to resolve challenging problems in hardware security. We propose HW2VEC and demonstrate how it can be utilized for Hardware Trojan (HT) detection and Intellectual Property (IP) piracy detection. Next\, in the binary analysis (Chapter 3)\, we demonstrate how a more advanced GL methodology\, called CFG2VEC\, can reverse-engineer the semantic source-level information lost during the compilation process\, thus making the binary software patching tasks more efficient. Then\, we describe our novel methodology\, SG2VEC\, to enhance autonomous driving systems’ scene understanding capabilities (Chapter 4). To enable this cognitive capability\, we propose to use scene-graph to encode the surrounding traffic participants and a pipeline of spatiotemporal scene-graph embedding networks to process scene-graphs and learn toward goals. During the courses of these projects\, we have developed many research tools to confront the technical challenges (mentioned in Appendices). \nShort Bio: \nShih-Yuan Yu is a 5-th year Ph.D. candidate in Computer Engineering and works in the Autonomous and Intelligent Cyber-Physical Systems lab under the supervision of Professor Mohammad Al Faruque. He received his B.S. and M.S. degrees in Computer Science and Information Engineering from the National Taiwan University in 2014. Before his Ph.D.\, Shih-Yuan worked as a software engineer at MediaTek for 4+ years. During his Ph.D.\, he interned at TuSimple (an autonomous driving company) and Siemens (industrial-academia collaborated projects). During his Ph.D.\, he has published 20+ papers in conferences and journals. His interests are making embedded cyber-physical systems more robust with data-driven methodologies enabled by graph learning.
URL:https://www.cecs.uci.edu/event/graph-learning-for-robust-embedded-and-cyber-physical-systems/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230523T140000
DTEND;TZID=America/Los_Angeles:20230523T150000
DTSTAMP:20231011T032546Z
CREATED:20231010T160244Z
LAST-MODIFIED:20231011T032546Z
UID:13940-1684850400-1684854000@www.cecs.uci.edu
SUMMARY:Reasoning About Emergent Behaviors at Runtime
DESCRIPTION:Name: Caio Batista de Melo\nChair: Nikil Dutt \nDate: May 23\, 2023 \nTime: 2:00 PM \nLocation: DBH 4011 \nCommittee: Fadi Kurdahi\, Tony Givargis \nTitle: Reasoning About Emergent Behaviors at Runtime \nAbstract: \nAs Autonomous Vehicles (AVs) become more common in the market\, the development of new technologies and solutions for them advances quickly. However\, there still are safety concerns regarding the increasing presence of AVs on public roads. For example\, the Cali- fornia Department of Motor Vehicles reported an approximate average of 3 disengagement events for each AV under test in 2021 and 1 event for every 1\,500 driven miles. Considering that a vehicle drives\, on average\, 15\,000 miles per year\, an AV would have ten such incidents annually. With this in mind\, work needs to be done to enable AVs to handle unexpected sit- uations (i.e.\, emergent behaviors) that can arise while driving\, increasing the safety for both humans and AVs on the road. This dissertation discusses possible approaches to improve the detection and reasoning of emergent behaviors at runtime. \nFirst\, it describes SAFER\, a framework to monitor individual systems using runtime verifica- tion alongside machine learning anomaly detection to detect emergent behaviors at runtime. SAFER achieves an average F1-score of 83% over four different example systems\, showcas- ing its efficacy to be on par with related work while being applicable to different classes of applications. Next\, it discusses DRIVE\, a framework for monitoring collective systems that can work together to find global anomalies that affect collective safety by identifying violations in local properties. Throughout a state-of-the-art inspired truck platooning case study\, DRIVE can detect all safety property violations\, with most queries resolved under 1 ms and a maximum latency of 11 ms per query\, demonstrating its promise. Lastly\, it presents LOCoCAT\, a low-overhead framework for emergent behavior reasoning to classify anomaly types based on vehicular data. LOCoCAT achieves an F1-score of up to 99.16% within the first 50 ms of the anomaly\, allowing the system to react quickly. These three techniques can be combined to allow future AVs to operate more safely on the road. \nShort Bio: \nCaio Batista del Melo is a Ph.D. candidate at the Computer Science Department of the University of California\, Irvine\, where he works with Prof. Nikil Dutt on the Information Processing Factory project. His research focuses on emergent behaviors and fault detection of systems. From a big-picture view\, he explored how we can enable self-driving vehicles to detect unexpected situations and decide how to best react to them. Prior to joining UCI\, he attended the University of Brasília\, in Brazil\, where he got a B.Sc. and an M.Sc. in Computer Science. While at UnB\, he worked with Prof. Genaína Rodrigues researching dependable distributed systems\, more specifically\, implied scenarios.
URL:https://www.cecs.uci.edu/event/reasoning-about-emergent-behaviors-at-runtime/
LOCATION:DBH 4011\, Donald Bren Hall\, University of California\, Irvine\, Irvine\, CA\, 92617\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230524T150000
DTEND;TZID=America/Los_Angeles:20230524T160000
DTSTAMP:20231011T032435Z
CREATED:20231011T032435Z
LAST-MODIFIED:20231011T032435Z
UID:14158-1684940400-1684944000@www.cecs.uci.edu
SUMMARY:Integrating Machine Learning for Enhanced Cross-Layer Security and Reliability in Hardware and Cyber-Physical Systems
DESCRIPTION:Name: Rozhin Yasaei\nChair: Mohammad Al Faruque \nDate: May 24\, 2023 \nTime: 3:00 PM \nLocation: EH 2430-Harut Barsamian \nCommittee: Fadi Kurdahi\, Zhou Li \nTitle: Integrating Machine Learning for Enhanced Cross-Layer Security and Reliability in Hardware and Cyber-Physical Systems \nAbstract: \nThis Ph.D. thesis presents a comprehensive investigation into addressing security and reliability challenges in hardware design and cyber-physical systems. The research leverages advanced techniques such as Graph Neural Networks (GNN) and machine learning to develop novel methodologies for anomaly detection\, hardware Trojan detection\, IP piracy detection\, and cross-layer security analysis.\nThe thesis focuses on hardware security\, specifically Hardware Trojan (HT) detection. A golden reference-free approach\, GNN4TJ\, is proposed for detecting HTs at the Register Transfer Level (RTL). By modeling hardware designs as graphs and employing GNN\, GNN4TJ achieves high recall rates for identifying both known and unknown HTs. \nMoving to the IoT domain\, the thesis addresses security challenges in IoT systems through the development of adaptive anomaly detection methods. IoT-CAD utilizes IoT sensor data and fog computing to ensure data integrity and detect anomalous incidents. The proposed methodology incorporates sensor association algorithms\, LSTM neural networks\, and Gaussian estimation for real-time anomaly detection. The thesis further extends the research to multi-modal data fusion\, where the integration of sensor and communication data using GNN enables improved anomaly detection\, source identification\, and recovery in IoT systems. \nIn addition\, the thesis explores the security aspect of wearable devices. A machine learning-based approach is proposed to detect anomalies in wearable devices that rely on NFC protocols. By leveraging system context and sensor correlations\, the methodology achieves high accuracy in detecting anomalies from multiple wearable sensors.\nFurthermore\, the thesis delves into IP piracy detection\, introducing a novel methodology that utilizes GNN to assess circuit similarities and detect IP piracy. The proposed approach proves effective in detecting IP piracy even in the presence of obfuscation techniques\, providing a robust solution for protecting intellectual properties. \nOverall\, this thesis showcases the application of advanced techniques such as GNN and machine learning in enhancing security and reliability in hardware design\, IoT systems\, and wearable devices. The proposed methodologies for anomaly detection\, hardware Trojan detection\, IP piracy detection\, and cross-layer security analysis contribute to advancing the state-of-the-art in ensuring the integrity and security of critical systems in the digital era. \nShort Bio: \nRozhin Yasaei is currently a Ph.D. candidate at the University of California Irvine\, studying computer engineering under Professor Mohammad Al Faruque. She received her M.Sc. degree in computer engineering from the University of California Irvine in 2021 and her B.Sc. in electrical engineering from Sharif University of technology in 2018. Her research interests lie in applied machine learning for embedded and cyber-physical systems security. She develops new methodologies that bring principles and techniques from machine learning\, such as graph neural networks\, to cross-layer system security. Rozhin conducts research that spans the areas of hardware\, embedded systems\, and cyber-physical systems while maintaining relevance to security and reliability. \nRozhin was selected for UC Irvine’s pedagogical fellowship in 2021 and\, through one year of training\, received a teaching excellence certificate and the qualification to design and implement a 3-day training program for incoming graduate students to prepare them for teaching assistance. Furthermore\, she received the Rising Stars in EECS Award in 2022\, the iREDEFINE Professional Development Award in 2022\, UCI UROP Research Discovery Fellowship in 2022\, UCI Henry Samueli Endowed Scholarship in 2018\, and several scholarships from different conferences. \nRozhin is devoted to promoting diversity and inclusion and supporting her fellow collages\, especially the underrepresented population in STEM\, by actively volunteering for panels\, workshops\, and conferences with similar visions and serving the community. She has been the president of the UCI chapter of the Women in CyberSecurity (WiCyS) organization since 2021 to foster women’s participation in this field. Qualified with mentoring excellence certificate\, she has been selected by several programs at UCI to mentor undergraduate and graduate students.
URL:https://www.cecs.uci.edu/event/integrating-machine-learning-for-enhanced-cross-layer-security-and-reliability-in-hardware-and-cyber-physical-systems/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
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