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Profiling side-channel attacks in which an adversary creates a “profile” of a sensitive device and uses such a profile to model a target device with similar implementation has received the lion’s share of attention in the recent years. In particular, template attacks are known to be the most powerful profiling side-channel attacks from an information theoretic point of view. When launching such an attack, the adversary first builds a model based on the leakage of the profiling (training) device in his disposal, which is then exploited in the second phase of the attack (i.e., matching) to extract the key from the target device. Discrepancies between the device used for modeling and the target device affect the attack success. The effect of process variation and temperature misalignment between the profiling and target devices in the template attack’s success has been studied extensively in the literature, while the impact of device aging on the template attack’s success is yet to be investigated thoroughly. This article moves one step forward and studies the impact of device aging, mainly bias temperature instability (BTI) and hot carrier injection (HCI), in the devices that have been protected against power analysis attacks via dual rail logics. In particular, we focus on the wave dynamic differential logic (WDDL) circuits, and via extensive transistor-level simulations, we will show how device aging misalignments between the profiling and target devices can hinder template attacks for both unprotected and WDDL protected counterparts. We mounted several attacks on the PRESENT cipher, with and without WDDL protection, at different temperatures and aging times. Our results show that the attack is more difficult if there is an aging-duration mismatch between the training and target devices, and the attack-efficiency decrease is especially significant for mismatches of few weeks. [ABSTRACT FROM AUTHOR]

Unintentional uncontrollable variations in the manufacturing process of integrated circuits are used to realize silicon primitives known as physical unclonable functions (PUFs). These primitives are used to create unique signatures for security purposes. Investigating the vulnerabilities of PUFs is of utmost importance to uphold their usefulness in secure applications. One such investigation includes exploring the susceptibility of PUFs to modeling attacks that aim at extracting the PUFs’ behavior. To date, these attacks have mainly focused on a single PUF instance where the targeted PUF is attacked using the model built based on the very same PUF’s challenge–response pairs or power side channel. In this article, we move one step forward and introduce Cross-PUF attacks where a model is created using the power consumption of one PUF instance to attack another PUF created from the same GDSII file. Through SPICE simulations, we show that these attacks are highly effective in modeling PUF behaviors even in the presence of noise and mismatches in temperature and aging of the PUF used for modeling versus the targeted PUF. To mitigate the Cross-PUF attacks, we then propose a lightweight countermeasure based on dual-rail and random initialization logic approaches called DRILL. We show that DRILL is highly effective in thwarting Cross-PUF attacks. [ABSTRACT FROM AUTHOR]

Detection of abnormal behaviors is essential in complex and/or strategic systems requiring a high level of safety and security. Sensing environmental conditions to ensure that the device is not operating out-of-specifications is highly useful in detecting anomalies caused by failures or malevolent actions. In this regard, digital sensors (DSs) are particularly attractive as they are portable and can be easily calibrated. In contrast to analog sensors, DSs have an interesting property that considers the operating environmental conditions as a whole, i.e., they are sensitive to temperature, voltage, and process altogether, without precise knowledge about each. This property endows DSs with fewer false positives compared to analog sensors. This article studies a low-cost DS, discusses its presilicon architecture and post-silicon calibration such that it detects system failures accurately in the designer’s preferable range of operating conditions. The impact of aging in this sensor is studied extensively. Tradeoffs between false positive and undetection rates are discussed. As an example, we target the substitution box (S-Box) of the PRESENT cipher assuming that it can be the target of fault injection attacks launched via abruptly changing the operating temperature and voltage. We show that such malfunction can be accurately detected by our DS, i.e., with a very negligible percentage of false and missed alarms (<1% totally). The results show that the number of false alarms raises with aging (while the rate is highly negligible), whereas the number of missed alarms remains at a reasonable low rate. [ABSTRACT FROM AUTHOR]

Estimating the aging-related degradation and failure of nanoscale integrated circuits (ICs), before they actually occur, is crucial for developing aging prevention/mitigation actions and in turn avoiding unexpected in-field circuit failures. Real-time monitoring of IC operating conditions can be efficiently used for predicting aging degradation and in turn timing failures caused by device aging. The existing approaches only take some specific operating conditions (e.g., workload or temperature) into account. In this paper, we propose a novel method for real-time IC aging prediction by extending the prediction schemes to a comprehensive model which takes into account any time-variant dynamic operating conditions relevant to aging prediction. Using a machine learning prediction model and the notion of equivalent aging time, we show that our approach outperforms the existing methods in terms of aging-prediction accuracy under different scenarios of time-variant operating conditions. [ABSTRACT FROM AUTHOR]