Advanced Electromagnetic and Machine Learning techniques towards Secure & Trusted Microelectronics

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Name of the Speaker : Dr. Debayan Das
Name of the Organizer : Dr. Janakiraman Viraraghavan
Venue: ESB 244 (Seminar Hall)
Date/Time : 23rd September 2022, 11:00 AM - 12:00 noon

The huge gamut of today’s internet-connected embedded devices has led to increasing concerns regarding the security and confidentiality of data. To address these requirements, most embedded devices employ cryptographic algorithms, which are computationally secure. Despite such mathematical guarantees, as these algorithms are implemented on a physical platform, they leak critical information in the form of power consumption, electromagnetic (EM) radiation, timing, cache hits and misses, and so on, leading to side-channel analysis (SCA) attacks. My work focusses on developing advanced side-channel attacks using machine learning (ML) and circuit-level low-overhead generic countermeasures. I will present a cross-device deep learning-based profiling power side-channel attack (X-DeepSCA) which can break the secret key of an AES-128 encryption engine running on an Atmel microcontroller using just a single power trace. Thus, physical leakage coupled with ML techniques can increase the threat surface of embedded devices significantly.

Despite all these advancements, most works till date, both attacks as well as countermeasures, treat the crypto engine as a black box, and hence most protection techniques incur high power/area overheads. In this talk, I will present the first white-box modeling of the EM leakage from a crypto hardware, leading to the understanding of the genesis of the EM leakage. Combining the 2 key techniques – current-domain signature attenuation (CDSA) and local lower metal routing shows >350x signature suppression in measurements on our fabricated 65nm CMOS test chip, leading to SCA resiliency beyond 1B encryptions. This key principle of killing the physical side-channel leakage at its source achieved a 100x improvement in both EM and power SCA protection over the prior works with comparable overheads.

Next, considering the continuous growth of wearable and implantable devices around a human body, this talk also focuses on analyzing the security of medical/personal devices, particularly for the internet-ofbody (IoB) and proposes electro-quasistatic human body communication (EQS-HBC) to form a covert body area network. While the traditional wireless body area network (WBAN) signals can be intercepted even at a distance of 5m, the EQS-HBC signals can be detected only up to 0.15m, which is practically in physical contact with the person. Thus, this pioneering work proposing EQS-HBC promises >30x improvement in private space compared to the traditional WBAN, enhancing physical security. In the long run, EQS-HBC can potentially enable several applications in the domain of connected healthcare, electroceuticals, augmented and virtual reality, and so on.

Finally, I will conclude with my vision towards developing secure, efficient, and ubiquitous IoT/IoB devices and cyber-physical systems through the combination of physical fields and their interaction with the network and system, which adds a new dimension to the analysis of the cyber-physical security.

Bio: Debayan Das received his PhD and MS in Electrical and Computer Engineering from Purdue University, USA in 2021 and the Bachelor of Electronics and Telecommunication Engineering degree from Jadavpur University, India, in 2015. He is currently a Research Scientist at Intel Corporation, USA. Prior to his Ph.D., he worked as an Analog Design Engineer at a startup based in India. He has interned with the Security Research Lab, Intel Labs, USA, over the summers of 2018 and 2020. His research interests include mixed-signal IC design and hardware security.

Dr. Das was a recipient of the IEEE HOST Best Student Paper Award in 2017 and 2019, the Third Best Poster Award in the IEEE HOST 2018, and the 2nd Best Demo Award in HOST 2020. In 2019, one of his papers was recognized as a Top Pick in Hardware and Embedded Security published over the span of the last six years. He was recognized as the winner (third place) of the ACM ICCAD 2020 Student Research Competition (SRC). During his Ph.D., he has been awarded the ECE Fellowship during 2016– 2018, the Bilsland Dissertation Fellowship in 2020–2021, the SSCS Pre-doctoral Achievement Award in 2021, and the Outstanding Graduate Student Research Award by the College of Engineering, Purdue University, in 2021 for his outstanding overall achievements. He has authored/co-authored more than 45 peer-reviewed conferences and journals including 2 book chapters and 1 US patent. He has been serving as a primary reviewer for multiple reputed journals and conferences including JSSC, TCAS-I, TVLSI, TCAD, Design & Test, TODAES, JETCAS, TBME, IEEE Access, IoTJ, DAC, VLSI Design, HOST.