Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Thing, Vrizlynn L. L."'
Autor:
Kumar, Ayush, Thing, Vrizlynn L. L.
Network Intrusion Detection Systems (NIDSs) which use machine learning (ML) models achieve high detection performance and accuracy while avoiding dependence on fixed signatures extracted from attack artifacts. However, there is a noticeable hesitance
Externí odkaz:
http://arxiv.org/abs/2408.14040
CPE-Identifier: Automated CPE identification and CVE summaries annotation with Deep Learning and NLP
Autor:
Hu, Wanyu, Thing, Vrizlynn L. L.
With the drastic increase in the number of new vulnerabilities in the National Vulnerability Database (NVD) every year, the workload for NVD analysts to associate the Common Platform Enumeration (CPE) with the Common Vulnerabilities and Exposures (CV
Externí odkaz:
http://arxiv.org/abs/2405.13568
Privacy-preserving analytics is designed to protect valuable assets. A common service provision involves the input data from the client and the model on the analyst's side. The importance of the privacy preservation is fuelled by legal obligations an
Externí odkaz:
http://arxiv.org/abs/2404.09625
Network intrusion detection systems (NIDS) play a pivotal role in safeguarding critical digital infrastructures against cyber threats. Machine learning-based detection models applied in NIDS are prevalent today. However, the effectiveness of these ma
Externí odkaz:
http://arxiv.org/abs/2404.07464
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 their data. However, ensuring user input privacy and secure inference exe
Externí odkaz:
http://arxiv.org/abs/2404.07437
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 of malicious traffic activity on 5G networks. Malicious traffic activ
Externí odkaz:
http://arxiv.org/abs/2402.14353
With the rising concern on model interpretability, the application of eXplainable AI (XAI) tools on deepfake detection models has been a topic of interest recently. In image classification tasks, XAI tools highlight pixels influencing the decision gi
Externí odkaz:
http://arxiv.org/abs/2312.06627
Autor:
Kumar, Ayush, Thing, Vrizlynn L. L.
5G networks are susceptible to cyber attacks due to reasons such as implementation issues and vulnerabilities in 3GPP standard specifications. In this work, we propose lateral movement strategies in a 5G Core (5GC) with network slicing enabled, as pa
Externí odkaz:
http://arxiv.org/abs/2312.01681
Autor:
Kumar, Ayush, Thing, Vrizlynn L. L.
The 3GPP 5G Service-based Architecture (SBA) security specifications leave several details on how to setup an appropriate Public Key Infrastructure (PKI) for 5G SBA, unspecified. In this work, we propose 5G-SBA-PKI, a public key infrastructure for se
Externí odkaz:
http://arxiv.org/abs/2309.14659
Autor:
Loh, Randolph, Thing, Vrizlynn L. L.
Self-Healing Cyber-Physical Systems (SH-CPS) effectively recover from system perceived failures without human intervention. They ensure a level of resilience and tolerance to unforeseen situations that arise from intrinsic system and component degrad
Externí odkaz:
http://arxiv.org/abs/2305.08335