T
Tian Huang
Researcher at University of Cambridge
Publications - 39
Citations - 497
Tian Huang is an academic researcher from University of Cambridge. The author has contributed to research in topics: Anomaly detection & Cloud computing. The author has an hindex of 11, co-authored 35 publications receiving 405 citations. Previous affiliations of Tian Huang include Shanghai Jiao Tong University & Agency for Science, Technology and Research.
Papers
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Journal ArticleDOI
A Case Study of Sensor Data Collection and Analysis in Smart City: Provenance in Smart Food Supply Chain
TL;DR: A smart sensor data collection strategy for IoT is proposed, which would improve the efficiency and accuracy of provenance with the minimized size of data set at the same time, and algorithms of tracing contamination source and back tracking potential infected food in the markets are presented.
Journal ArticleDOI
Anomaly detection and identification scheme for VM live migration in cloud infrastructure
TL;DR: An adaptive scheme that mines data from the cloud infrastructure in order to detect abnormal statistics when VMs are migrated to new hosts with higher detection rates and lower false alarm rate and would serve as a novel anomaly detection tool to improve security framework in VM management for cloud computing.
Journal ArticleDOI
Statistical Learning for Anomaly Detection in Cloud Server Systems: A Multi-Order Markov Chain Framework
TL;DR: The testing results show that the multi-order approach is able to produce more effective indicators: in addition to the absolute values given by an individual single-order model, the changes in ranking positions of outputs from different-order ones also correlate closely with abnormal behaviours.
Journal ArticleDOI
An Integrated Data Preprocessing Framework Based on Apache Spark for Fault Diagnosis of Power Grid Equipment
TL;DR: An integrated data preprocessing framework DPF based on Apache Spark is presented to improve the prediction accuracy for data sets with missing data points and classification accuracy with noise data as well as to meet the big data requirement, which mainly combines missing data prediction, data fusion, data cleansing and fault type classification.
Proceedings ArticleDOI
An LOF-Based Adaptive Anomaly Detection Scheme for Cloud Computing
TL;DR: This work presents an adaptive anomaly detection scheme for cloud computing based on LOF, which learns behaviors of applications both in training and detecting phase and enables the ability to detect contextual anomalies.