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Unsupervised Deep Learning-based Side-channel Analysis

Savu, Ioana (author)

Side-channel attacks (SCA) play a crucial role in assessing the security of the implementation of cryp- tographic algorithms. Still, traditional profiled attacks require a nearly identical reference device to the target, limiting their practicality. This thesis focuses on non-profiled SCA, which provides a re- alistic alternative when the...

master thesis 2023

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Hardware-based implementations in Side-Channel Analysis: A comparison study of DL SCA attacks against HW and SW AES and a novel methodology

Bubberman, Wolf (author)

Side-Channel Attacks (SCA) attempt to recover the secret cryptographic key from an electronic device by exploiting the unintended physical leakages of said device. With the devices that are being attacked becoming more sophisticated, so is SCA. In the past few years, the focus of the research in the field of SCA shifted towards the application...

master thesis 2022

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On the use of ResNet architec- tures for Side-Channel Analysis

Karayalçin, Sengim (author)

Some of the most prominent types of attacks against modern cryptographic implementations are side-channel attacks. These attacks leverage some unintended, often physical, leakage of the implementation to retrieve secret information. In recent times, a large part of the focus of side-channel research has been on deep learning methods. These...

master thesis 2022

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Neuroevolution Applied to Profiled Side-Channel Attacks

Schijlen, Fiske (author)

Side-channel attacks (SCA) can obtain secret information related to the private key used during encryption executed on some device by exploiting leakage in power traces produced by the device. In recent years, researchers found that a neural network (NN) can be employed to execute a powerful profiled SCA, even on targets protected with...

master thesis 2022

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Side-channel Attacks on Inner Rounds of AES and PRESENT: A deeper look into the inner rounds of SPN based block ciphers and how this vision can help us attack the intermediate bytes using Deep Learning

Swaminathan, Sudharshan (author)

Side-channel attacks (SCA) focus on vulnerabilities caused by insecure implementations and exploit them to deduce useful information about the data being processed or the data itself through leakages obtained from the device. There have been many studies exploiting these side-channel leakages, and most of the state-of-the-art attacks have been...

master thesis 2021

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Adversarial Examples as a defense against Side-Channel Attacks: A novel approach for countermeasure design

Korpas Kamarianos, Alexandros (author)

Over the last decades, side-channel attacks (SCAs) have been proven as a substantial weakness of cryptographic devices, while the recent growth of deep learning (DL) dramatically improved the performance of SCA. More and more researches present ways to build lightweight deep neural network (DNN) models that can retrieve the secret encryption key...

master thesis 2021

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Covert DNS Storage Channel Detection: Uncovering surreptitious data exchange using the phonebook of the internet

van Hal, Sven (author)

The cyber arms race has red and blue teams continuously at their toes to keep ahead. Increasingly capable cyber actors breach secure networks at a worrying scale. While network monitoring and analysis should identify blatant data exfiltration attempts, covert channels bypass these measures and facilitate surreptitious information extraction. The...

master thesis 2021

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Socio-technical design for clinical decision support algorithms

de Witte, Sven (author)

Clinical decision support systems is a collection name for a lot of Artificial Intelligence systems used in healthcare. These systems are designed to help health workers make decisions faster and make the healthcare environment as a whole more efficient. Decisions made by these systems often weigh heavy on the ethical side giving advice on what...

bachelor thesis 2021

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Using Machine Learning for University Admission: Mapping the Socio-Technical Issue

Niri, Omri (author)

Machine learning algorithms were used in in the past decade to assist humans with recruitment and grades assessments in the academic field. For the most part, the algorithms either exacerbated existing biases or output unfair results. This could often be traced back to an ill-implementation of the systems in the social context. The academic...

bachelor thesis 2021

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How Can Socio-Technical Systems Design Approaches Ensure Autonomy in Hiring Practices?

Kedem, Liam (author)

Artificial Intelligence (AI) is widely used in hiring practices to identify the most suitable candidate for a vacancy. This is due to the promise of higher overall efficiency and lower costs. However, these AI-powered tools may create an inaccurate conception of the applicant's suitability to the vacancy by numerically quantifying context...

bachelor thesis 2021

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Design approaches to tackle ethical issues for proficiency estimation in AI-enabled recruitment

Lesichkov, Lassie (author)

Companies have been looking for automation in their hiring practices and Artificial Intelligence offers a solution. A popular opinion on the topic is that AI-enabled hiring will enhance talent acquisition overall, eliminating bias and bettering results. However, deployed AI hiring tools often do not offer a solution but worsen the problem by...

bachelor thesis 2021

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ELM Decision Branching: Improving streaming clustering with decision branching

Moree, Jarno (author)

Clustering is a commonly used method in data analysis. It is a complex problem that can be very time consuming, especially when clustering large datasets with many features. Most clustering algorithms scale exponentially in time when increasing the dataset size, making it infeasible to use them for large datasets. Streaming algorithms do not...

master thesis 2021

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Mitigating Inference Attacks in Collaborative Credit Card Fraud Detection using Secure Multi-Party Selection

van Tetering, Daphne (author)

The convenient service offered by credit cards and the technological advances in e-commerce have caused the number of online payment transactions to increase daily. With this rising number, the opportunity for fraudsters to obtain cardholder details via online credit card fraud has also increased. As a result, according to the European Central...

master thesis 2021

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Backdoor Attacks in Neural Networks

Koffas, Stefanos (author)

Deep learning has made tremendous success in the past decade. As a result, it is becoming widely deployed in various safety and security-critical applications like autonomous driving, malware detection, fingerprint identification, and financial fraud detection. It was recently shown that deep neural networks are susceptible to multiple attacks...

master thesis 2021

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Data Augmentation Techniques using Generative Adversarial Neural Networks on Side Channel Analysis

Vlogiaris, Achilleas (author)

Side-channel Attacks can be performed in various ways by measuring the power consumption, the electromagnetic emission, or even by measuring an algorithm's execution time on the targeted device. More or less sophisticated methods can be used to utilize this information in order to perform a Side-channel attack, more specifically by training...

master thesis 2021

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Investigating the efficiency of detection methods against network covert timing channels

Faber, Julian (author)

Network covert timing channels are techniques to covertly transmit information over computer networks, by utilizing the time between subsequent network packets. Previous work on the detection of the various techniques has introduced numerous new methods, with high reported success. From these previous works we have noticed that there is little...

master thesis 2021

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Exploiting structure in counterexamples to speed up equivalence checking in the minimally adequate teacher framework: Active Learning

Catshoek, Tom (author)

Active state machine learning algorithms are a class of algorithms that allow us to infer state machines representing certain systems. These algorithms interact with a system and build a hypothesis of what the state machine describing that system looks like according to the behavior they observed. Once the algorithm arrives at a hypothesis, it...

master thesis 2021

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Predicting True Vulnerabilities from Static Analyzer Warnings in Industry: An Attempt to Faster Releasing Software in Industry

Bisesser, Dinesh (author)

An increasing digital world, comes with many benefits but unfortunately also many drawbacks. The increase of the digital world means an increase in data and software. Developing more software unfortunately also means a higher probability of vulnerabilities, which can be exploited by adversaries. Adversaries taking advantage of users and software...

master thesis 2020

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Privacy-Preserving Electronic Healthcare with Self-Monitoring Devices using Trusted Execution Environments

To, Seu Man (author)

The adoption of smart wearable devices has been on the rise over the past few years. These wearables are able to track the user's vital signs, making them valuable for use in the healthcare industry. Sharing this information with the user's healthcare provider has the potential to improve medical care by reducing medical misinformation....

master thesis 2020

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Learning State Machines in Real-Time on a Small Dedicated Hardware Device

Cao, Clinton (author)

The Internet is a technology that was invented in the 1960s and was used only by a few users to do simple communications between computers. Fast forward to 2020, the Internet has become a technology that is being used by billions of users. It allows users to communicate with each other across the world and even allows users to access data...

master thesis 2020