@article{aydin_aysu_2024, title={Leaking secrets in homomorphic encryption with side-channel attacks}, ISSN={["2190-8516"]}, DOI={10.1007/s13389-023-00340-2}, journal={JOURNAL OF CRYPTOGRAPHIC ENGINEERING}, author={Aydin, Furkan and Aysu, Aydin}, year={2024}, month={Jan} }
 @article{sayadi_aliasgari_aydin_potluri_aysu_edmonds_tehranipoor_2022, title={Towards AI-Enabled Hardware Security: Challenges and Opportunities}, ISSN={["1942-9398"]}, DOI={10.1109/IOLTS56730.2022.9897507}, abstractNote={Recent developments in Artificial Intelligence (AI) and Machine Learning (ML), driven by a substantial increase in the size of data in emerging computing systems, have led into successful applications of such intelligent techniques in various disciplines including security. Traditionally, integrity of data has been protected with various security protocols at the software level with the underlying hardware assumed to be secure. This assumption however is no longer true with an increasing number of attacks reported on the hardware. The emergence of new security threats (e.g., malware, side-channel attacks, etc.) requires patching/updating the software-based solutions that needs a vast amount of memory and hardware resources. Therefore, the security should be delegated to the underlying hardware, building a bottom-up solution for securing computing devices rather than treating it as an afterthought. This paper highlights the growing role of AI/ML techniques in hardware and architecture security field and provides insightful discussions on pressing challenges, opportunities, and future directions of designing accurate and efficient machine learning-based attacks and defense mechanisms in response to emerging hardware security vulnerabilities in modern computer systems and next generation of cryptosystems.}, journal={2022 IEEE 28TH INTERNATIONAL SYMPOSIUM ON ON-LINE TESTING AND ROBUST SYSTEM DESIGN (IOLTS 2022)}, author={Sayadi, Hossein and Aliasgari, Mehrdad and Aydin, Furkan and Potluri, Seetal and Aysu, Aydin and Edmonds, Jack and Tehranipoor, Sara}, year={2022} }
 @article{kashyap_aydin_potluri_franzon_aysu_2021, title={2Deep: Enhancing Side-Channel Attacks on Lattice-Based Key-Exchange via 2-D Deep Learning}, volume={40}, ISSN={["1937-4151"]}, url={http://dx.doi.org/10.1109/tcad.2020.3038701}, DOI={10.1109/TCAD.2020.3038701}, abstractNote={Advancements in quantum computing present a security threat to classical cryptography algorithms. Lattice-based key exchange protocols show strong promise due to their resistance to theoretical quantum-cryptanalysis and low implementation overhead. By contrast, their physical implementations have shown vulnerability against side-channel attacks (SCAs) even with a single power measurement. The state-of-the-art SCAs are, however, limited to simple, sequentialized executions of post-quantum key-exchange (PQKE) protocols, leaving the vulnerability of complex, parallelized architectures unknown. This article proposes 2Deep—a deep-learning (DL)-based SCA—targeting parallelized implementations of PQKE protocols, namely, <monospace>Frodo</monospace> and <monospace>NewHope</monospace> with data augmentation techniques. Specifically, we explore approaches that convert 1-D time-series power measurement data into 2-D images to formulate SCA an image recognition task. The results show our attack’s superiority over conventional techniques including horizontal differential power analysis (DPA), template attacks (TAs), and straightforward DL approaches. We demonstrate improvements up to <inline-formula> <tex-math notation="LaTeX">$1.5\times $ </tex-math></inline-formula> to recover a 100% success rate compared to DL with 1-D input data while using fewer data. We furthermore show that machine learning improves the results up to <inline-formula> <tex-math notation="LaTeX">$1.25\times $ </tex-math></inline-formula> compared to TAs. Furthermore, we perform cross-device attacks that obtain profiles from a single device, which has never been explored. Our 2-D approach is especially favored in this setting, improving the success rate of attacking <monospace>Frodo</monospace> from 20% to 99% compared to the 1-D approach. Our work thus urges countermeasures even on parallel architectures and single-trace attacks.}, number={6}, journal={IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Kashyap, Priyank and Aydin, Furkan and Potluri, Seetal and Franzon, Paul D. and Aysu, Aydin}, year={2021}, month={Jun}, pages={1217–1229} }
 @article{regazzoni_bhasin_pour_alshaer_aydin_aysu_beroulle_di natale_franzon_hely_et al._2020, title={Machine Learning and Hardware security: Challenges and Opportunities -Invited Talk}, ISSN={["1933-7760"]}, DOI={10.1145/3400302.3416260}, abstractNote={Machine learning techniques have significantly changed our lives. They helped improving our everyday routines, but they also demonstrated to be an extremely helpful tool for more advanced and complex applications. However, the implications of hardware security problems under a massive diffusion of machine learning techniques are still to be completely understood. This paper first highlights novel applications of machine learning for hardware security, such as evaluation of post quantum cryptography hardware and extraction of physically unclonable functions from neural networks. Later, practical model extraction attack based on electromagnetic side-channel measurements are demonstrated followed by a discussion of strategies to protect proprietary models by watermarking them.}, journal={2020 IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER AIDED-DESIGN (ICCAD)}, author={Regazzoni, Francesco and Bhasin, Shivam and Pour, Amir Ali and Alshaer, Ihab and Aydin, Furkan and Aysu, Aydin and Beroulle, Vincent and Di Natale, Giorgio and Franzon, Paul and Hely, David and et al.}, year={2020} }