most citedMTS-DVGAN: Anomaly Detection in Cyber-Physical Systems using a Dual Variational Generative Adversarial Network

29 citations · 34 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CR20242 cited

Research and application of artificial intelligence based webshell detection model: A literature review

Mingrui Ma, Lansheng Han, Chunjie Zhou

Webshell, as the "culprit" behind numerous network attacks, is one of the research hotspots in the field of cybersecurity. However, the complexity, stealthiness, and confusing natu…

cs.LG2024

HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate Detection of Industrial Multivariate Time Series Anomalies

Haili Sun, Yan Huang, Lansheng Han +2

Multivariate Time Series (MTS) anomaly detection focuses on pinpointing samples that diverge from standard operational patterns, which is crucial for ensuring the safety and securi…

cs.LG2024

Unsupervised Spatio-Temporal State Estimation for Fine-grained Adaptive Anomaly Diagnosis of Industrial Cyber-physical Systems

Haili Sun, Yan Huang, Lansheng Han +2

Accurate detection and diagnosis of abnormal behaviors such as network attacks from multivariate time series (MTS) are crucial for ensuring the stable and effective operation of in…

cs.LG20243 cited

Research and application of Transformer based anomaly detection model: A literature review

Mingrui Ma, Lansheng Han, Chunjie Zhou

Transformer, as one of the most advanced neural network models in Natural Language Processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire res…

cs.CR202329 cited

MTS-DVGAN: Anomaly Detection in Cyber-Physical Systems using a Dual Variational Generative Adversarial Network

Haili Sun, Yan Huang, Lansheng Han +3

Deep generative models are promising in detecting novel cyber-physical attacks, mitigating the vulnerability of Cyber-physical systems (CPSs) without relying on labeled information…

cs.CR2023

Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning

Haili Sun, Yan Huang, Lansheng Han +1

The rapid development of Industry 4.0 has amplified the scope and destructiveness of industrial Cyber-Physical System (CPS) by network attacks. Anomaly detection techniques are emp…