Related papers: Horizontal SCA Attacks against kP Algorithm Using …
Power side-channel attacks, which can deduce secret data via statistical analysis, have become a serious threat. Masking is an effective countermeasure for reducing the statistical dependence between secret data and side-channel…
Industrial Control Systems are under increased scrutiny. Their security is historically sub-par, and although measures are being taken by the manufacturers to remedy this, the large installed base of legacy systems cannot easily be updated…
Most electronic devices utilize mechanical keyboards to receive inputs, including sensitive information such as authentication credentials, personal and private data, emails, plans, etc. However, these systems are susceptible to acoustic…
SCADA protocols for Industrial Control Systems (ICS) are vulnerable to network attacks such as session hijacking. Hence, research focuses on network anomaly detection based on meta--data (message sizes, timing, command sequence), or on the…
Kernel Principal Component Analysis (KPCA) is a key machine learning algorithm for extracting nonlinear features from data. In the presence of a large volume of high dimensional data collected in a distributed fashion, it becomes very…
Elliptic Curve Scalar Multiplication denoted as kP operation is the basic operation in all Elliptic Curve based cryptographic protocols. The atomicity principle and different atomic patterns for kP algorithms were proposed in the past as…
In this paper, we aim to give a tutorial for undergraduate students studying statistical methods and/or bioinformatics. The students will learn how data visualization can help in genomic sequence analysis. Students start with a fragment of…
Side-channel attacks have become prominent attack surfaces in cyberspace. Attackers use the side information generated by the system while performing a task. Among the various side-channel attacks, cache side-channel attacks are leading as…
Systems based on deep neural networks are vulnerable to adversarial attacks. Unrestricted adversarial attacks typically manipulate the semantic content of an image (e.g., color or texture) to create adversarial examples that are both…
Side-channel attacks allow to extract sensitive information from cryptographic primitives by correlating the partially known computed data and the measured side-channel signal. Starting from the raw side-channel trace, the preprocessing of…
CRYSTALS-Kyber has been standardized as the only key-encapsulation mechanism (KEM) scheme by NIST to withstand attacks by large-scale quantum computers. However, the side-channel attacks (SCAs) on its implementation are still needed to be…
Physical side channels emerge from the relation between internal computation or data with observable physical parameters of a chip. Previous works mostly focus on properties related to current consumption such as power consumption. The…
Deep learning has become the de-facto computational paradigm for various kinds of perception problems, including many privacy-sensitive applications such as online medical image analysis. No doubt to say, the data privacy of these deep…
This survey is on forward-looking, emerging security concerns in post-quantum era, i.e., the implementation attacks for 2022 winners of NIST post-quantum cryptography (PQC) competition and thus the visions, insights, and discussions can be…
In recent years a new class of side-channel attacks has emerged. Instead of targeting device emissions during dynamic computation, adversaries now frequently exploit the leakage or response behaviour of integrated circuits in a static…
This paper proposes an upgraded electro-magnetic side-channel attack that automatically reconstructs the intercepted data. A novel system is introduced, running in parallel with leakage signal interception and catching compromising data in…
Adversarial attacks are inputs that are similar to original inputs but altered on purpose. Speech-to-text neural networks that are widely used today are prone to misclassify adversarial attacks. In this study, first, we investigate the…
Smartphones handle sensitive tasks such as messaging and payment and may soon support critical electronic identification through initiatives such as the European Digital Identity (EUDI) wallet, currently under development. Yet the…
K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of the cluster centers. Numerous initialization…
The k-means algorithm is one of the most common clustering algorithms and widely used in data mining and pattern recognition. The increasing computational requirement of big data applications makes hardware acceleration for the k-means…