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1. iPUF: A Novel Security-by-Design Paradigm to Mitigate Data Manipulation and External Attacks in Cyber-Physical Systems

   Aarella, S G; Agarwal, S; Mohanty, S P; Kougianos, E; Iyer, V; Rout, B (February 2025, IEEE)

   Amsaad, F; Abdelgawad, A; Jamil, A (Ed.)

   Fault Injection attack is a type of side-channel attack on the Physical Unclonable Function (PUF) module that can induce faults in the PUF response by manipulating the PUF circuit behavior through voltage glitches, laser attacks, temperature manipulations, or any other attacks potentially leading to information loss or security system failure. This type of attack exposes the physical characteristics of PUFs that can be analyzed to predict or compromise the unique challenge response pairs (CRPs) reducing the security and reliability of the PUF. Mitigation strategies against such attacks typically include adding noise to the PUF output, using error-correcting codes, or enhanced cryptographic protocols that obscure physical side-channel attacks. In this research, we propose a Generative Adversarial Network (GAN) based security model, that monitors the PUF behavior and detects the variations in PUF response. The model can detect glitches in the PUF response and generate alerts to take mitigation measures. 

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2. Intrinsic Rowhammer PUFs: Leveraging the Rowhammer effect for improved security

   https://doi.org/10.1109/HST.2017.7951729  

   Schaller, Andre; Xiong, Wenjie; Anagnostopoulos, Nikolaos Athanasios; Saleem, Muhammad Umair; Gabmeyer, Sebastian; Katzenbeisser, Stefan; Szefer, Jakub (May 2017, Hardware Oriented Security and Trust)

   Physically Unclonable Functions (PUFs) have become an important and promising hardware primitive for device fingerprinting, device identification, or key storage. Intrinsic PUFs leverage components already found in existing devices, unlike extrinsic silicon PUFs, which are based on customized circuits that involve modification of hardware. In this work, we present a new type of a memory-based intrinsic PUF, which leverages the Rowhammer effect in DRAM modules - the Rowhammer PUF. Our PUF makes use of bit flips, which occur in DRAM cells due to rapid and repeated access of DRAM rows. Prior research has mainly focused on Rowhammer attacks, where the Rowhammer effect is used to illegitimately alter data stored in memory, e.g., to change page table entries or enable privilege escalation attacks. Meanwhile, this is the first work to use the Rowhammer effect in a positive context - to design a novel PUF. We extensively evaluate the Rowhammer PUF using commercial, off-the-shelf devices, not relying on custom hardware or an FPGA-based setup. The evaluation shows that the Rowhammer PUF holds required properties needed for the envisioned security applications, and could be deployed today. 

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3. Fortified-Edge 4.0: A ML-Based Error Correction Framework for Secure Authentication in Collaborative Edge Computing

   https://doi.org/10.1145/3649476.3660384  

   Aarella, Seema G; Yanambaka, Venkata Prasanth; Mohanty, Saraju P; Kougianos, Elias (June 2024, ACM)

   Physical Unclonable Functions (PUFs) are widely researched in the field of security because of their unique, robust, and reliable nature, PUFs are considered device-specific root keys that are hard to duplicate. There are many variants of PUFs that are being studied and implemented including hardware and software PUFs. Though PUFs are believed to be secure and reliable, they are not without challenges of their own. The efficient performance of PUF depends on various environmental factors, which leads to inefficiency. Bit flipping is one such problem that can bring down the reliability of the PUF. Memory-based PUFs are prone to unavoidable bit flips occurring in the hardware, similarly, sensor-based PUFs are prone to bit flips occurring due to temperature variation. The number of errors in the PUF response must be minimized to improve the reliability of the PUF in security applications. In this research we explore the Machine Learning (ML) model based on K-mer sequencing to detect and correct the bit flips in the PUFs, hence fortifying the PUF-based secure authentication system for authentication and authorization of Edge Data Centers (EDC) in a Collaborative Edge Computing (CEC) Environment. 

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4. Machine Learning-based Vulnerability Study of Interpose PUFs as Security Primitives for IoT Networks

   https://doi.org/10.1109/NAS51552.2021.9605405  

   Thapaliya, Bipana; Mursi, Khalid T.; Zhuang, Yu (October 2021, 2021 IEEE International Conference on Networking, Architecture and Storage (NAS))

   Security is of importance for communication networks, and many network nodes, like sensors and IoT devices, are resource-constrained. Physical Unclonable Functions (PUFs) leverage physical variations of the integrated circuits to produce responses unique to individual circuits and have the potential for delivering security for low-cost networks. But before a PUF can be adopted for security applications, all security vulnerabilities must be discovered. Recently, a new PUF known as Interpose PUF (IPUF) was proposed, which was tested to be secure against reliability-based modeling attacks and machine learning attacks when the attacked IPUF is of small size. A recent study showed IPUFs succumbed to a divide-and-conquer attack, and the attack method requires the position of the interpose bit known to the attacker, a condition that can be easily obfuscated by using a random interpose position. Thus, large IPUFs may still remain secure against all known modeling attacks if the interpose position is unknown to attackers. In this paper, we present a new modeling attack method of IPUFs using multilayer neural networks, and the attack method requires no knowledge of the interpose position. Our attack was tested on simulated IPUFs and silicon IPUFs implemented on FPGAs, and the results showed that many IPUFs which were resilient against existing attacks cannot withstand our new attack method, revealing a new vulnerability of IPUFs by re-defining the boundary between secure and insecure regions in the IPUF parameter space. 

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5. Investigation of Deep Learning Attacks on Nonlinear Silicon Photonic PUFs

   https://doi.org/10.1364/CLEO_QELS.2018.FM1G.4  

   Atakhodjaev, Iskandar; Bosworth, Bryan T.; Grubel, Brian C.; Kossey, Michael R.; Villalba, Jesús; Cooper, A. Brinton; Dehak, Najim; Foster, Amy C.; Foster, Mark A. (January 2018, Conference on Lasers and Electro-Optics)

   We demonstrate that nonlinear silicon photonic Physical Unclonable Functions (PUFs) are resistant to adversarial deep learning attacks. We find that this resistance is rooted in the optical nonlinearity of the silicon photonic PUF token. 

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