58 citations · 160 across the 30 of their papers we have counts for
33 papers
ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation
Fariza Rashid, Duc Van Le, Rahat Masood +3
Time series imputation has progressed from statistical and deep learning approaches to diffusion-based models, which have shown strong recent performance. Existing diffusion-based…
QwenSafe: Multimodal Content Rating Description Identification via Preference-Aligned VLMs
Dishanika Denipitiyage, Aruna Seneviratne, Suranga Seneviratne
Mobile app marketplaces require developers to disclose standardized content rating descriptors (CRDs) to inform users about potentially sensitive or restricted content. Ensuring th…
Eliciting Least-to-Most Reasoning for Phishing URL Detection
Holly Trikilis, Pasindu Marasinghe, Fariza Rashid +1
Phishing continues to be one of the most prevalent attack vectors, making accurate classification of phishing URLs essential. Recently, large language models (LLMs) have demonstrat…
RankOOD -- Class Ranking-based Out-of-Distribution Detection
Dishanika Denipitiyage, Naveen Karunanayake, Suranga Seneviratne +1
We propose RankOOD, a rank-based Out-of-Distribution (OOD) detection approach based on training a model with the Placket-Luce loss, which is now extensively used for preference ali…
Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
Seunghyun Lee, Omid Tavallaie, Shuaijun Chen +4
Accommodating edge networks between IoT devices and the cloud server in Hierarchical Federated Learning (HFL) enhances communication efficiency without compromising data privacy. H…
BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection
Nishavi Ranaweera, Jiarui Xu, Suranga Seneviratne +1
Web access today occurs predominantly through mobile devices, with Android representing a significant share of the mobile device market. This widespread usage makes Android a prime…
A Framework to Assess Multilingual Vulnerabilities of LLMs
Likai Tang, Niruth Bogahawatta, Yasod Ginige +4
Large Language Models (LLMs) are acquiring a wider range of capabilities, including understanding and responding in multiple languages. While they undergo safety training to preven…
Federated Koopman-Reservoir Learning for Large-Scale Multivariate Time-Series Anomaly Detection
Long Tan Le, Tung-Anh Nguyen, Han Shu +3
The proliferation of edge devices has dramatically increased the generation of multivariate time-series (MVTS) data, essential for applications from healthcare to smart cities. Suc…
An Empirical Study of Code Obfuscation Practices in the Google Play Store
Akila Niroshan, Suranga Seneviratne, Aruna Seneviratne
The Android ecosystem is vulnerable to issues such as app repackaging, counterfeiting, and piracy, threatening both developers and users. To mitigate these risks, developers often…
Entailment-Driven Privacy Policy Classification with LLMs
Bhanuka Silva, Dishanika Denipitiyage, Suranga Seneviratne +2
While many online services provide privacy policies for end users to read and understand what personal data are being collected, these documents are often lengthy and complicated.…
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks
Omid Tavallaie, Kanchana Thilakarathna, Suranga Seneviratne +2
Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-sensitive applications that run on resource-constrained devices with non-Identically and Ind…
LLMs are One-Shot URL Classifiers and Explainers
Fariza Rashid, Nishavi Ranaweera, Ben Doyle +1
Malicious URL classification represents a crucial aspect of cyber security. Although existing work comprises numerous machine learning and deep learning-based URL classification mo…
Federated PCA on Grassmann Manifold for IoT Anomaly Detection
Tung-Anh Nguyen, Long Tan Le, Tuan Dung Nguyen +4
With the proliferation of the Internet of Things (IoT) and the rising interconnectedness of devices, network security faces significant challenges, especially from anomalous activi…
Detecting and Characterising Mobile App Metamorphosis in Google Play Store
D. Denipitiyage, B. Silva, K. Gunathilaka +4
App markets have evolved into highly competitive and dynamic environments for developers. While the traditional app life cycle involves incremental updates for feature enhancements…
A Survey of Deep Long-Tail Classification Advancements
Charika de Alvis, Suranga Seneviratne
Many data distributions in the real world are hardly uniform. Instead, skewed and long-tailed distributions of various kinds are commonly observed. This poses an interesting proble…
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey
Naveen Karunanayake, Ravin Gunawardena, Suranga Seneviratne +1
Deep neural networks (DNNs) deployed in real-world applications can encounter out-of-distribution (OOD) data and adversarial examples. These represent distinct forms of distributio…
Long-Tail Learning with Rebalanced Contrastive Loss
Charika De Alvis, Dishanika Denipitiyage, Suranga Seneviratne
Integrating supervised contrastive loss to cross entropy-based communication has recently been proposed as a solution to address the long-tail learning problem. However, when the c…
ExCeL : Combined Extreme and Collective Logit Information for Enhancing Out-of-Distribution Detection
Naveen Karunanayake, Suranga Seneviratne, Sanjay Chawla
Deep learning models often exhibit overconfidence in predicting out-of-distribution (OOD) data, underscoring the crucial role of OOD detection in ensuring reliability in prediction…
Federated Deep Equilibrium Learning: Harnessing Compact Global Representations to Enhance Personalization
Long Tan Le, Tuan Dung Nguyen, Tung-Anh Nguyen +4
Federated Learning (FL) has emerged as a groundbreaking distributed learning paradigm enabling clients to train a global model collaboratively without exchanging data. Despite enha…
Usability of Privacy Controls in Top Health Websites
Ravin Gunawardena, Yuemeng Yin, Yi Huang +5
With the increasing awareness and concerns around privacy, many service providers offer their users various privacy controls. Through these controls, users gain greater authority o…
Non-Contrastive Learning-based Behavioural Biometrics for Smart IoT Devices
Oshan Jayawardana, Fariza Rashid, Suranga Seneviratne
Behaviour biometrics are being explored as a viable alternative to overcome the limitations of traditional authentication methods such as passwords and static biometrics. Also, the…
Privacy-Preserving Spam Filtering using Functional Encryption
Sicong Wang, Naveen Karunanayake, Tham Nguyen +1
Traditional spam classification requires the end-user to reveal the content of its received email to the spam classifier which violates the privacy. Spam classification over encryp…
Security Apps under the Looking Glass: An Empirical Analysis of Android Security Apps
Weixian Yao, Yexuan Li, Weiye Lin +4
Third-party security apps are an integral part of the Android app ecosystem. Many users install them as an extra layer of protection for their devices. There are hundreds of such s…
A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play Store
Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake +2
Counterfeit apps impersonate existing popular apps in attempts to misguide users to install them for various reasons such as collecting personal information or spreading malware. M…