Match!
Li Cheng
National University of Defense Technology
Pattern recognitionComputer scienceAnomaly detectionOutlierData stream mining
9Publications
2H-index
7Citations
What is this?
Publications 9
Newest
#1Li Cheng (National University of Defense Technology)H-Index: 2
#2Yijie Wang (National University of Defense Technology)H-Index: 9
Last. Xingkong Ma (National University of Defense Technology)H-Index: 2
view all 3 authors...
Source
#1Li Cheng (National University of Defense Technology)H-Index: 2
#2Yijie Wang (National University of Defense Technology)H-Index: 2
Last. Xingkong Ma (National University of Defense Technology)H-Index: 2
view all 3 authors...
Abstract Unsupervised outlier detection for categorical data is important and essential for broad applications in various domains. The complex interactions between attributes and the relevance of attributes make it a stem challenge. Existing methods, including patterns-based and couplings-based methods, either fail to capture the complex interactions or cannot handle the diverse attributes well. In this paper, we propose a novel Neural Probabilistic Outlier Detection method for categorical data,...
2 CitationsSource
#1Rui Ma (National University of Defense Technology)
#2Yijie Wang (National University of Defense Technology)H-Index: 2
Last. Li Cheng (National University of Defense Technology)H-Index: 2
view all 3 authors...
Without class label, unsupervised feature selection methods choose a subset of features that faithfully maintain the intrinsic structure of original data. Conventional methods assume that the exact value of pairwise samples distance used in structure regularization is effective. However, this assumption imposes strict restrictions to feature selection, and it causes more features to be kept for data representation. Motivated by this, we propose Unsupervised Feature Selection via Local Total-orde...
Source
Oct 17, 2018 in CIKM (Conference on Information and Knowledge Management)
#1Hongzuo Xu (National University of Defense Technology)H-Index: 3
#2Yongjun Wang (National University of Defense Technology)H-Index: 2
Last. Xingkong Ma (National University of Defense Technology)H-Index: 2
view all 5 authors...
Unavoidable noise in real-world categorical data presents significant challenges to existing outlier detection methods because they normally fail to separate noisy values from outlying values. Feature subspace-based methods inevitably mix noisy values when retaining an entire feature because a feature may contain both outlying values and noisy values. Pattern-based methods are normally based on frequency and are easily misled by noisy values, resulting in many faulty patterns. This paper introdu...
3 CitationsSource
Oct 4, 2018 in ICANN (International Conference on Artificial Neural Networks)
#1Zongren Li (National University of Defense Technology)
#2Yijie Wang (National University of Defense Technology)H-Index: 2
Last. Xingkong Ma (National University of Defense Technology)H-Index: 2
view all 5 authors...
The detection of distance-based outliers from streaming data is critical for modern applications ranging from telecommunications to cybersecurity. However, existing works mainly concentrate on improving the responding speed, none of these proposals can perform well in streams with varying data distribution. In this paper, we propose a Fast and Robust Outlier Detection method (FROD in short) to solve this dilemma and achieve the promotion in both detection performance and processing throughput. S...
Source
#1Li Cheng (National University of Defense Technology)H-Index: 2
#2Yijie Wang (National University of Defense Technology)H-Index: 2
Last. Xingkong Ma (National University of Defense Technology)H-Index: 2
view all 4 authors...
1 CitationsSource
Dec 1, 2017 in UbiComp (Ubiquitous Computing)
#2Yijie Wang (National University of Defense Technology)H-Index: 2
Last. Li Cheng (National University of Defense Technology)H-Index: 2
view all 4 authors...
The application of Support Vector Machine (SVM) over data stream is growing with the increasing real-time processing requirements in classification field, like anomaly detection and real-time image processing. However, the dynamic live data with high volume and fast arrival rate in data streams make it challenging to apply SVM in data stream processing. Existing SVM implementations are mostly designed for batch processing and hardly satisfy the efficiency requirement of stream processing for its...
Source
Nov 14, 2017 in ICONIP (International Conference on Neural Information Processing)
#1Runfan Wu (National University of Defense Technology)
#2Yijie Wang (National University of Defense Technology)H-Index: 2
Last. Li Cheng (National University of Defense Technology)H-Index: 2
view all 4 authors...
Resource prediction promotes dynamic scheduling and energy saving in cloud computing. However, resource prediction becomes a challenge with the diversity and dynamicity of the cloud environment. Existing methods merely focus on single specific resource and ignore the correlation among resources, resulting in inaccurate predictions. Therefore, we propose a trend-matching resources coupled prediction method (TMRCP) based on incremental learning over data stream, which consists of three algorithms....
Source
Aug 1, 2016 in TrustCom (Trust, Security And Privacy In Computing And Communications)
#1Li Cheng (National University of Defense Technology)H-Index: 2
#2Yijie Wang (National University of Defense Technology)H-Index: 2
Last. Yongjun Wang (National University of Defense Technology)H-Index: 2
view all 4 authors...
Causal-based alert correlation is one of the mainstream techniques to detect multi-step threat behaviors. However, because large-scale network generates high-speed alerts and alert type distribution in dataflow changes over time, it is challenging to increase generality, scalability and reduce overhead for causal alert correlation method. In this paper, we propose a novel general, scalable and low-overhead alert correlation method, called GSLAC. GSLAC first presents a "dispatch-aggregate" scheme...
1 CitationsSource
1