Searched for: subject%3A%22Adversarial%255C+examples%22
(1 - 9 of 9)
document
Pigmans, Max (author)
Most of the adversarial attacks suitable for attacking decision tree ensembles work by doing multiple local searches from randomly selected starting points, around the to be attacked victim. In this thesis we investigate the impact of these starting points on the performance of the attack, and find that the starting points significantly impact...
master thesis 2024
document
Thomas, Wessel (author)
Network Intrusion Detection Systems (NIDSs) defend our computer networks against malicious network attacks. Anomaly-based NIDSs use machine learning classifiers to categorise incoming traffic. Research has shown that classifiers are vulnerable to adversarial examples, perturbed inputs that lead the classifier into misclassifying the input....
master thesis 2023
document
Nowroozi, Ehsan (author), Mohammadi, Mohammadreza (author), Savas, Erkay (author), Mekdad, Yassine (author), Conti, M. (author)
In the past few years, Convolutional Neural Networks (CNN) have demonstrated promising performance in various real-world cybersecurity applications, such as network and multimedia security. However, the underlying fragility of CNN structures poses major security problems, making them inappropriate for use in security-oriented applications,...
journal article 2023
document
Vos, D.A. (author), Verwer, S.E. (author)
Decision trees are popular models for their interpretation properties and their success in ensemble models for structured data. However, common decision tree learning algorithms produce models that suffer from adversarial examples. Recent work on robust decision tree learning mitigates this issue by taking adversarial perturbations into...
conference paper 2023
document
Bartlett, A.J. (author), Liem, C.C.S. (author), Panichella, A. (author)
Deep learning (DL) models are known to be highly accurate, yet vulnerable to adversarial examples. While earlier research focused on generating adversarial examples using whitebox strategies, later research focused on black-box strategies, as models often are not accessible to external attackers. Prior studies showed that black-box approaches...
conference paper 2023
document
Aliyu, Ibrahim (author), van Engelenburg, S.H. (author), Mu'azu, Muhammed Bashir (author), Kim, Jinsul (author), Lim, Chang Gyoon (author)
The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in-vehicle network from cyber-attacks. Blockchain-based Federated Forests (BFFs) could be...
journal article 2022
document
van der Werf, Daan (author)
In recent years financial fraud has seen substantial growth due to the advent of electronic financial services opening many doors for fraudsters. Consequently, the industry of fraud detection has seen a significant growth in scale, but moves slowly in comparison to the ever-changing nature of fraudulent behavior. As the monetary losses...
master thesis 2021
document
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
document
Bilstra, Cas (author)
Machine learning models are increasing in popularity and are nowadays used in a wide range of critical applications in fields such as Automotive, Aviation and Medical. Among machine learning models, tree ensemble models are a popular choice due to their competitive performance and high degree of explainability. Like most machine learning models...
master thesis 2021
Searched for: subject%3A%22Adversarial%255C+examples%22
(1 - 9 of 9)