79 citations · 155 across the 13 of their papers we have counts for
13 papers
SoK: Can Trajectory Generation Combine Privacy and Utility?
Erik Buchholz, Alsharif Abuadbba, Shuo Wang +2
While location trajectories represent a valuable data source for analyses and location-based services, they can reveal sensitive information, such as political and religious prefer…
Reconstruction Attack on Differential Private Trajectory Protection Mechanisms
Erik Buchholz, Alsharif Abuadbba, Shuo Wang +2
Location trajectories collected by smartphones and other devices represent a valuable data source for applications such as location-based services. Likewise, trajectories have the…
Unraveling Threat Intelligence Through the Lens of Malicious URL Campaigns
Mahathir Almashor, Ejaz Ahmed, Benjamin Pick +6
The daily deluge of alerts is a sombre reality for Security Operations Centre (SOC) personnel worldwide. They are at the forefront of an organisation's cybersecurity infrastructure…
Towards Web Phishing Detection Limitations and Mitigation
Alsharif Abuadbba, Shuo Wang, Mahathir Almashor +4
Web phishing remains a serious cyber threat responsible for most data breaches. Machine Learning (ML)-based anti-phishing detectors are seen as an effective countermeasure, and are…
PublicCheck: Public Integrity Verification for Services of Run-time Deep Models
Shuo Wang, Sharif Abuadbba, Sidharth Agarwal +6
Existing integrity verification approaches for deep models are designed for private verification (i.e., assuming the service provider is honest, with white-box access to model para…
Robust Training Using Natural Transformation
Shuo Wang, Lingjuan Lyu, Surya Nepal +3
Previous robustness approaches for deep learning models such as data augmentation techniques via data transformation or adversarial training cannot capture real-world variations th…
OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning
Shuo Wang, Surya Nepal, Kristen Moore +3
The diversity and quantity of data warehouses, gathering data from distributed devices such as mobile devices, can enhance the success and robustness of machine learning algorithms…
Adversarial Defense by Latent Style Transformations
Shuo Wang, Surya Nepal, Alsharif Abuadbba +2
Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we investigate an attack…
Defending Adversarial Attacks via Semantic Feature Manipulation
Shuo Wang, Tianle Chen, Surya Nepal +3
Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we propose a one-off and…
OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning
Shuo Wang, Tianle Chen, Shangyu Chen +3
Anomaly detection aims to recognize samples with anomalous and unusual patterns with respect to a set of normal data. This is significant for numerous domain applications, such as…
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models
Shuo Wang, Surya Nepal, Carsten Rudolph +3
Transfer learning provides an effective solution for feasibly and fast customize accurate \textit{Student} models, by transferring the learned knowledge of pre-trained \textit{Teac…
Generating Semantic Adversarial Examples via Feature Manipulation
Shuo Wang, Surya Nepal, Carsten Rudolph +3
The vulnerability of deep neural networks to adversarial attacks has been widely demonstrated (e.g., adversarial example attacks). Traditional attacks perform unstructured pixel-wi…
Defeating Misclassification Attacks Against Transfer Learning
Bang Wu, Shuo Wang, Xingliang Yuan +3
Transfer learning is prevalent as a technique to efficiently generate new models (Student models) based on the knowledge transferred from a pre-trained model (Teacher model). Howev…