158 citations · 461 across the 37 of their papers we have counts for
6 papers · 1 filter
Evaluating The Explainability of State-of-the-Art Deep Learning-based Network Intrusion Detection Systems
Ayush Kumar, Vrizlynn L. L. Thing
State-of-the-art deep learning (DL)-based network intrusion detection systems (NIDSs) offer limited "explainability". For example, how do they make their decisions? Do they suffer…
CPE-Identifier: Automated CPE identification and CVE summaries annotation with Deep Learning and NLP
Wanyu Hu, Vrizlynn L. L. Thing
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 Platfo…
Privacy-Preserving Intrusion Detection using Convolutional Neural Networks
Martin Kodys, Zhongmin Dai, Vrizlynn L. L. Thing
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 imp…
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…