158 citations · 163 across the 10 of their papers we have counts for
11 papers
SAGEGAN: Style-Based Anomaly Detection with Gaussian Embeddings using Generative Adversarial Networks
Thesath Wijayasiri, Kar Wai Fok, Vrizlynn L. L. Thing
Malware evolves faster than rule-based and signature-driven detection pipelines. This paper presents SAGEGAN, a benign-only trained malware anomaly detection framework that convert…
HilEnT: Hilbert, Entropy Transformed Image Based Malware Detection
Rahul Kale, Thesath Wijayasiri, Kar Wai Fok +1
With the increasing threat of malware across various software related domains, malware detection and classification is critical to determine the response actions. Different strateg…
TIER: Trajectory-Invariant Explanation Regularization for Membership Privacy
Varun Sharma, Kar Wai Fok, Vrizlynn L. L. Thing
Explainability is central to building trustworthy AI, yet explanation interfaces can inadvertently provide adversaries with an expanded privacy-related attack surfaces. Recent stud…
DE-FIVE: Detecting Malicious Image Prompts via Fourier Features and Image Vector Embeddings
Xingwei Zhong, Varun Sharma, Kar Wai Fok +1
Vision language models (VLMs) employ both visual and textual modalities to enable advanced vision-language inference. However, incorporating visual modalities expands the attack su…
Enhanced MLLM Black-Box Jailbreaking Attacks and Defenses
Xingwei Zhong, Kar Wai Fok, Vrizlynn L. L. Thing
Multimodal large language models (MLLMs) comprise of both visual and textual modalities to process vision language tasks. However, MLLMs are vulnerable to security-related issues,…
ExpIDS: A Drift-adaptable Network Intrusion Detection System With Improved Explainability
Ayush Kumar, Kar Wai Fok, Vrizlynn L. L. Thing
Despite all the advantages associated with Network Intrusion Detection Systems (NIDSs) that utilize machine learning (ML) models, there is a significant reluctance among cyber secu…
Enhanced Consistency Bi-directional GAN (CBiGAN) for Malware Anomaly Detection
Thesath Wijayasiri, Kar Wai Fok, Vrizlynn L. L. Thing
Static malware analysis remains a core technique in cybersecurity due to its ability to assess potentially malicious software without execution. Nevertheless, many existing static…
Enhancing Network Intrusion Detection Performance using Generative Adversarial Networks
Xinxing Zhao, Kar Wai Fok, Vrizlynn L. L. Thing
Network intrusion detection systems (NIDS) play a pivotal role in safeguarding critical digital infrastructures against cyber threats. Machine learning-based detection models appli…
Privacy preserving layer partitioning for Deep Neural Network models
Kishore Rajasekar, Randolph Loh, Kar Wai Fok +1
MLaaS (Machine Learning as a Service) has become popular in the cloud computing domain, allowing users to leverage cloud resources for running private inference of ML models on the…
Exploring Emerging Trends in 5G Malicious Traffic Analysis and Incremental Learning Intrusion Detection Strategies
Zihao Wang, Kar Wai Fok, Vrizlynn L. L. Thing
The popularity of 5G networks poses a huge challenge for malicious traffic detection technology. The reason for this is that as the use of 5G technology increases, so does the risk…
Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Study
Zihao Wang, Kar-Wai Fok, Vrizlynn L. L. Thing
As people's demand for personal privacy and data security becomes a priority, encrypted traffic has become mainstream in the cyber world. However, traffic encryption is also shield…