Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Seneviratne, Suranga"'
Autor:
Silva, Bhanuka, Denipitiyage, Dishanika, Seneviratne, Suranga, Mahanti, Anirban, Seneviratne, Aruna
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. As a result, the vast majority of users do not read them at all, leadi
Externí odkaz:
http://arxiv.org/abs/2409.16621
Autor:
Tavallaie, Omid, Thilakarathna, Kanchana, Seneviratne, Suranga, Seneviratne, Aruna, Zomaya, Albert Y.
Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-sensitive applications that run on resource-constrained devices with non-Identically and Independently Distributed (IID) data. Traditional FL frameworks adopt the
Externí odkaz:
http://arxiv.org/abs/2409.15067
Malicious URL classification represents a crucial aspect of cyber security. Although existing work comprises numerous machine learning and deep learning-based URL classification models, most suffer from generalisation and domain-adaptation issues ari
Externí odkaz:
http://arxiv.org/abs/2409.14306
Autor:
Nguyen, Tung-Anh, Le, Long Tan, Nguyen, Tuan Dung, Bao, Wei, Seneviratne, Suranga, Hong, Choong Seon, Tran, Nguyen H.
Publikováno v:
IEEE/ACM Transactions on Networking On page(s): 1-16 Print ISSN: 1063-6692 Online ISSN: 1558-2566 Digital Object Identifier: 10.1109/TNET.2024.3423780
With the proliferation of the Internet of Things (IoT) and the rising interconnectedness of devices, network security faces significant challenges, especially from anomalous activities. While traditional machine learning-based intrusion detection sys
Externí odkaz:
http://arxiv.org/abs/2407.07421
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 problem for machine learning, where most algorithms assume or work well with
Externí odkaz:
http://arxiv.org/abs/2404.15593
Deep neural networks (DNNs) deployed in real-world applications can encounter out-of-distribution (OOD) data and adversarial examples. These represent distinct forms of distributional shifts that can significantly impact DNNs' reliability and robustn
Externí odkaz:
http://arxiv.org/abs/2404.05219
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 class imbalance ratio is high, it requires adjusting the supervised con
Externí odkaz:
http://arxiv.org/abs/2312.01753
Autor:
Le, Long Tan, Nguyen, Tuan Dung, Nguyen, Tung-Anh, Hong, Choong Seon, Seneviratne, Suranga, Bao, Wei, Tran, Nguyen H.
Federated Learning (FL) has emerged as a groundbreaking distributed learning paradigm enabling clients to train a global model collaboratively without exchanging data. Despite enhancing privacy and efficiency in information retrieval and knowledge ma
Externí odkaz:
http://arxiv.org/abs/2309.15659
Autor:
Gunawardena, Ravin, Yin, Yuemeng, Huang, Yi, Masood, Rahat, Seneviratne, Suranga, Razzak, Imran, Tran, Nguyen, Seneviratne, Aruna
With the increasing awareness and concerns around privacy, many service providers offer their users various privacy controls. Through these controls, users gain greater authority over the collection, utilisation, and dissemination of their personal i
Externí odkaz:
http://arxiv.org/abs/2303.01838
Behaviour biometrics are being explored as a viable alternative to overcome the limitations of traditional authentication methods such as passwords and static biometrics. Also, they are being considered as a viable authentication method for IoT devic
Externí odkaz:
http://arxiv.org/abs/2210.12964