Related papers: Time/memory/data trade-off attack to a psuedo-rand…
We present an iterative approach to constructing pseudorandom generators, based on the repeated application of mild pseudorandom restrictions. We use this template to construct pseudorandom generators for combinatorial rectangles and…
Despite the critical role of timing infrastructure in enabling essential services, from public key infrastructure and smart grids to autonomous navigation and high-frequency trading, modern timing stacks remain highly vulnerable to…
Generative models learn the distribution of data from a sample dataset and can then generate new data instances. Recent advances in deep learning has brought forth improvements in generative model architectures, and some state-of-the-art…
Although cryptographic algorithms may be mathematically secure, it is often possible to leak secret information from the implementation of the algorithms. Timing and power side-channel vulnerabilities are some of the most widely considered…
Here we present a new protocol for controlled quantum key agreement and another protocol for key agreement with a specific focus on the security analysis. Specifically, detailed security proof is provided against impersonated fraudulent…
Distributed-phase-reference quantum key distribution stands out for its easy implementation with present day technology. Since many years, a full security proof of these schemes in a realistic setting has been elusive. For the first time,…
Deep neural networks (DNNs) are vulnerable to adversarial attack despite their tremendous success in many AI fields. Adversarial attack is a method that causes the intended misclassfication by adding imperceptible perturbations to…
Multimodal pretrained models are vulnerable to backdoor attacks, yet most existing methods rely on visual or multimodal triggers, which are impractical since visually embedded triggers rarely occur in real-world data. To overcome this…
Multivariate Time Series (MTS) forecasting is a fundamental task with numerous real-world applications, such as transportation, climate, and epidemiology. While a myriad of powerful deep learning models have been developed for this task,…
Machine learning models are famously vulnerable to adversarial attacks: small ad-hoc perturbations of the data that can catastrophically alter the model predictions. While a large literature has studied the case of test-time attacks on…
Reliable operation in power systems is becoming more difficult as the penetration of random renewable resources increases. In particular, operators face the risk of not scheduling enough traditional generators in the times when renewable…
The security of quantum key distribution relies on the validity of quantum mechanics as a description of nature and on the non-existence of leaky degrees of freedom in the practical implementations. We experimentally demonstrate how, in…
Many adversarial attacks target natural language processing systems, most of which succeed through modifying the individual tokens of a document. Despite the apparent uniqueness of each of these attacks, fundamentally they are simply a…
Caches have been exploited to leak secret information due to the different times they take to handle memory accesses. Cache timing attacks include non-speculative cache side and covert channel attacks and cache-based speculative execution…
Cybercriminals use Return Oriented Programming techniques to attack systems and IoT devices. While defenses have been developed, not all of them are applicable to constrained devices. We present Shakedown, which is a compile-time…
In this paper, we propose a novel class of symmetric key distribution protocols that leverages basic security primitives offered by low-cost, hardware chipsets containing millions of synchronized self-powered timers. The keys are derived…
With the great popularity of Graph Neural Networks (GNNs), their robustness to adversarial topology attacks has received significant attention. Although many attack methods have been proposed, they mainly focus on fixed-budget attacks,…
Machine learning models are routinely deployed on a wide range of computing hardware. Although such hardware is typically expected to produce identical results, differences in its design can lead to small numerical variations during…
In this paper, a new pseudo-random number generator (PRNG) based on chaotic iterations is proposed. This method also combines the digits of two XORshifts PRNGs. The statistical properties of this new generator are improved: the generated…
While password managers are a vital tool for internet security, they can also create a massive central point of failure, as evidenced by several major recent data breaches. For over 20 years, deterministic password generators (DPGs) have…