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Damith C. Ranasinghe

Publications and source records attributed to Damith C. Ranasinghe.

60 records · Page 4Linked to original sources

Exploiting PUF Models for Error Free Response Generation

Physical unclonable functions (PUF) extract secrets from randomness inherent in manufacturing processes. PUFs are utilized for basic cryptographic tasks such as authentication and key generation, and more recently, to realize key exchange and bit commitment requiring a large number of error free responses from a strong PUF. We propose an approach to eliminate the need to implement expensive on-chip error correction logic implementation and the associated helper data storage to reconcile naturally noisy PUF responses. In particular, we exploit a statistical model of an Arbiter PUF (APUF) constructed under the nominal operating condition during the challenge response enrollment phase by a trusted party to judiciously select challenges that yield error-free responses even across a wide operating conditions, specifically, a $ \pm 20\% $ supply voltage variation and a $ 40^{\crc} $ temperature variation. We validate our approach using measurements from two APUF datasets. Experimental results indicate that large number of error-free responses can be generated on demand under worst-case when PUF response error rate is up to 16.68\%.

cs.CR↗

PUF-FSM: A Controlled Strong PUF

This paper presents the PUF finite state machine (PUF-FSM) that is served as a practical {\it controlled} strong PUF. Previous controlled PUF designs have the difficulties of stabilizing the noisy PUF responses where the error correction logic is required. In addition, the computed helper data to assist error correcting, however, leaks information, which poses the controlled PUF under the threatens of fault attacks or reliability-based attacks. The PUF-FSM eschews the error correction logic and the computation, storage and loading of the helper data on-chip by only employing error-free responses judiciously determined on demand in the absence of an Arbiter PUF with a large CRP space. In addition, the access to the PUF-FSM is controlled by the trusted entity. Control in means of i) restricting challenges presented to the PUF and ii) further preventing repeated response evaluations to gain unreliability side-channel information are foundations of defensing the most powerful modeling attacks. The PUF-FSM goes beyond authentications/identifications to such as key generations and advanced cryptographic applications built upon a shared key.

cs.CR↗

Nano-Intrinsic True Random Number Generation

Recent advances in predictive data analytics and ever growing digitalization and connectivity with explosive expansions in industrial and consumer Internet-of-Things (IoT) has raised significant concerns about security of people's identities and data. It has created close to ideal environment for adversaries in terms of the amount of data that could be used for modeling and also greater accessibility for side-channel analysis of security primitives and random number generators. Random number generators (RNGs) are at the core of most security applications. Therefore, a secure and trustworthy source of randomness is required to be found. Here, we present a differential circuit for harvesting one of the most stochastic phenomenon in solid-state physics, random telegraphic noise (RTN), that is designed to demonstrate significantly lower sensitivities to other sources of noises, radiation and temperature fluctuations. We use RTN in amorphous SrTiO3-based resistive memories to evaluate the proposed true random number generator (TRNG). Successful evaluation on conventional true randomness tests (NIST tests) has been shown. Robustness against using predictive machine learning and side-channel attacks have also been demonstrated in comparison with non-differential readouts methods.

cs.ET↗

A Physical Unclonable Function with Redox-based Nanoionic Resistive Memory

A unique set of characteristics are packed in emerging nonvolatile reduction-oxidation (redox)-based resistive switching memories (ReRAMs) such as their underlying stochastic switching processes alongside their intrinsic highly nonlinear current-voltage characteristic, which in addition to known nano-fabrication process variation make them a promising candidate for the next generation of low-cost, low-power, tiny and secure Physically Unclonable Functions (PUFs). This paper takes advantage of this otherwise disadvantageous ReRAM feature using a combination of novel architectural and peripheral circuitry. We present a physical one-way function, nonlinear resistive Physical Unclonable Function (nrPUF), potentially applicable in variety of cyber-physical security applications given its performance characteristics. We experimentally verified performance of Valency Change Mechanism (VCM)-based ReRAM in nano-fabricated crossbar arrays across multiple dies and runs. In addition to a massive pool of Challenge-Response Pairs (CRPs), using a combination of experimental and simulation, our proposed PUF shows a reliability of 98.67%, a uniqueness of 49.85%, a diffuseness of 49.86%, a uniformity of 47.28%, and a bit-aliasing of 47.48%.

cs.ET↗

Learning from Imbalanced Multiclass Sequential Data Streams Using Dynamically Weighted Conditional Random Fields

The present study introduces a method for improving the classification performance of imbalanced multiclass data streams from wireless body worn sensors. Data imbalance is an inherent problem in activity recognition caused by the irregular time distribution of activities, which are sequential and dependent on previous movements. We use conditional random fields (CRF), a graphical model for structured classification, to take advantage of dependencies between activities in a sequence. However, CRFs do not consider the negative effects of class imbalance during training. We propose a class-wise dynamically weighted CRF (dWCRF) where weights are automatically determined during training by maximizing the expected overall F-score. Our results based on three case studies from a healthcare application using a batteryless body worn sensor, demonstrate that our method, in general, improves overall and minority class F-score when compared to other CRF based classifiers and achieves similar or better overall and class-wise performance when compared to SVM based classifiers under conditions of limited training data. We also confirm the performance of our approach using an additional battery powered body worn sensor dataset, achieving similar results in cases of high class imbalance.

cs.LG↗

Future Large-Scale Memristive Device Crossbar Arrays: Limits Imposed by Sneak-Path Currents on Read Operations

Passive crossbar arrays based upon memristive devices, at crosspoints, hold great promise for the future high-density and non-volatile memories. The most significant challenge facing memristive device based crossbars today is the problem of sneak-path currents. In this paper, we investigate a memristive device with intrinsic rectification behavior to suppress the sneak-path currents in crossbar arrays. The device model is implemented in Verilog-A language and is simulated to match device characteristics readily available in the literature. Then, we systematically evaluate the read operation performance of large-scale crossbar arrays utilizing our proposed model in terms of read margin and power consumption while considering different crossbar sizes, interconnect resistance values, HRS/LRS (High Resistance State/Low Resistance State) values, rectification ratios and different read-schemes. The outcomes of this study are understanding the trade-offs among read margin, power consumption, read-schemes and most importantly providing a guideline for circuit designers to improve the performance of a memory based crossbar structure. In addition, read operation performance comparison of the intrinsic rectifying memristive device model with other memristive device models are studied.

cs.ET↗