A5045570758- Cryptographic Implementations and Security
- Advanced Malware Detection Techniques
- Physical Unclonable Functions (PUFs) and Hardware Security
- Digital Media Forensic Detection
- Welding Techniques and Residual Stresses
- Advanced Welding Techniques Analysis
- Electrostatic Discharge in Electronics
- Chaos-based Image/Signal Encryption
- Antimicrobial Resistance in Staphylococcus
- Network Security and Intrusion Detection
- Fatigue and fracture mechanics
- Wireless Signal Modulation Classification
- Advanced Fiber Laser Technologies
- Additive Manufacturing Materials and Processes
- Mechanical stress and fatigue analysis
- Security and Verification in Computing
- Gas Sensing Nanomaterials and Sensors
- Fuel Cells and Related Materials
- Adversarial Robustness in Machine Learning
- Integrated Circuits and Semiconductor Failure Analysis
- Advancements in PLL and VCO Technologies
- Advanced Steganography and Watermarking Techniques
- Topic Modeling
- Advanced Optical Sensing Technologies
- Hydrogen embrittlement and corrosion behaviors in metals
Technical University of Darmstadt
2024
Radboud University Nijmegen
2024
Delft University of Technology
2020-2023
Hohai University
2018-2019
Deep learning represents a powerful set of techniques for profiling sidechannel analysis. The results in the last few years show that neural network architectures like multilayer perceptron and convolutional networks give strong attack performance where it is possible to break targets protected with various countermeasures. Considering deep commonly have plethora hyperparameters tune, clear such top can come high price preparing attack. This especially problematic as side-channel community...
Today, the deep learning-based side-channel analysis represents a widely researched topic, with numerous results indicating advantages of such an approach. Indeed, breaking protected implementations while not requiring complex feature selection made learning preferred option for profiling analysis. Still, this does mean it is trivial to mount successful One biggest challenges find optimal hyperparameters neural networks resulting in powerful attacks. This work proposes automated way...
One of the main promoted advantages deep learning in profiling sidechannel analysis is possibility skipping feature engineering process. Despite that, most recent publications consider selection as attacked interval from side-channel measurements pre-selected. This similar to worst-case security assumptions evaluations when random secret shares (e.g., mask shares) are known during phase: an evaluator can identify points ofinterest locations and efficiently trim trace interval. To broadly...
In the profiled side-channel analysis, deep learning-based techniques proved to be very successful even when attacking targets protected with countermeasures. Still, there is no guarantee that learning attacks will always succeed. Various countermeasures make significantly more complex, and such can further combined challenging. An intuitive solution improve performance of would reduce effect countermeasures.This paper investigates whether we consider certain types hiding as noise then use a...
In the last decade, machine learning-based side-channel attacks have become a standard option when investigating profiling attacks. At same time, previous state-of-the-art technique, template attack, started losing its importance and was more considered baseline to compare against. As such, most of results reported that learning (and especially deep learning) could significantly outperform attack. Nevertheless, attack still has certain advantages even compared learning. The significant one...
Abstract Deep learning is a powerful direction for profiling side-channel analysis as it can break targets protected with countermeasures even relatively small number of attack traces. Still, necessary to conduct hyperparameter tuning reach strong performance, which be far from trivial. Besides many options stemming the machine domain, recent years also brought neural network elements specially designed analysis. The loss function, calculates error or between actual and desired output, one...
The use of deep learning-based side-channel analysis is an effective way performing profiling attacks on power and electromagnetic leakages, even against targets protected with countermeasures. While many research papers have reported successful results, they typically focus attacking a single device, assuming that leakages are similar between devices the same type. However, this assumption not always realistic due to variations in hardware measurement setups, creating what known as...
The efficiency of the profiling side-channel analysis can be significantly improved with machine learning techniques. Although powerful, a fundamental limitation being data-hungry received little attention in community. In practice, maximum number leakage traces that evaluators/attackers obtain is constrained by scheme requirements or limited accessibility target. Even worse, various countermeasures modern devices increase conditions on size to break This work demonstrates practical approach...
Profiling side-channel analysis has gained widespread acceptance in both academic and industrial realms due to its robust capacity unveil protected secrets, even the presence of countermeasures. To harness this capability, an adversary must access a clone target device acquire profiling measurements, labeling them with leakage models. The challenge finding effective model, especially for dataset low signal-to-noise ratio or weak correlation between actual leakages labels, often necessitates...
Side-channel Collision Attacks (SCCA) is a classical method that exploits information dependency leaked during cryptographic operations. Unlike collision attacks seek instances where two different inputs to algorithm yield identical outputs, SCCAs specifically target the internal state, outputs are more likely. Although SCCA does not rely on pre-assumption of leakage model, it explicitly operates precise trace segments reflecting operation, which challenging perform when measurements noisy....
Profiling side-channel analysis, recognized for its robust attack performance in worst-case scenarios, necessitates adversaries to have a cloned device profiling measurements and secret information data labeling. On the other hand, nonprofiling attacks eschew these requirements by trying all key guesses. Although more suitable real-world they may suffer from mediocre due lack of leakage insight.This paper introduces novel weakly analysis method that bridges classical non-profiling analyses....
The adoption of deep neural networks for profiling side-channel attacks opened new perspectives leakage detection. Recent publications showed that cryptographic implementations featuring different countermeasures could be broken without feature selection or trace preprocessing. This success comes with a high price: an extensive hyperparameter search to find optimal learning models. As models usually suffer from overfitting due their fitting capacity, it is crucial avoid over-training...
Abstract Template attacks (TAs) are one of the most powerful side-channel analysis (SCA) attacks. The success such relies on effectiveness profiling model in modeling leakage information. A crucial step for TA is to select relevant features from measured traces, often called points interest (POIs), extract Previous research indicates that properly selecting input leaking could significantly increase attack performance. However, due presence SCA countermeasures and advancements technology...