Linru Ma
8 Papers
3 Citations
Linru Ma is an academic researcher. The author has contributed to research in topics: Computer science & Intrusion detection system. The author has an hindex of 2, co-authored 3 publications.
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Papers
A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection
TL;DR: By comparing with the two SOM-based intrusion detection systems, the overall goal of this survey is to comprehensively compare the primitive components and properties of SOM- based intrusion detection.
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Statistics-Enhanced Direct Batch Growth Self-Organizing Mapping for Efficient DoS Attack Detection
TL;DR: A new model of the “statistic-enhanced directed batch growth self-organizing mapping” is proposed, which renew the definition of the growth threshold used to evaluate/control neuron expansion, and first introduces the inner distribution factor for fine-grained data distinguishing.
Consensus tracking control for uncertain non‐strict feedback multi‐agent system under cyber attack via resilient neuroadaptive approach
Xiaoshan Ma,Li Yang,Linru Ma,Wenhan Dong,Ming Jin,Long Zhang,Feng Yang,Yan Lin +7 more
TL;DR: A new resilient neuroadaptive dynamic surface control scheme for non‐linear MASs with potential cyber attacks (false data injection and denial‐of‐service), system uncertainty, unknown control gain, and output constraints which are usually seen on CPSs.
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Direct Batch Growth Hierarchical Self-Organizing Mapping Based on Statistics for Efficient Network Intrusion Detection
TL;DR: Numerical experiments of network intrusion detection were carried out on the datasets of KDD99, Moore and CICIDS2017, where the good performance validated the superiority of the proposed method.
Memory-Augmented Insider Threat Detection with Temporal-Spatial Fusion
TL;DR: This paper proposes a novel insider threat detection method, namely, Memory-Augmented Insider Threat Detection (MAITD), which captures the temporal and spatial correlation of user behaviors by constructing compound behavioral matrix and common group model, and combines specific application scenarios to integrate the detection results.
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