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Yanxin Wang
Researcher at Iowa State University
Publications - 6
Citations - 273
Yanxin Wang is an academic researcher from Iowa State University. The author has contributed to research in topics: Intrusion detection system & Anomaly-based intrusion detection system. The author has an hindex of 4, co-authored 6 publications receiving 269 citations.
Papers
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Journal ArticleDOI
Lightweight agents for intrusion detection
TL;DR: The design of the Multi-agent IDS is described and it is shown how lightweight agent capabilities allowed us to add communication and collaboration capabilities to the mobile agents in the authors' IDS.
Journal ArticleDOI
Software fault tree and coloured Petri net based specification, design and implementation of agent-based intrusion detection systems
Guy Helmer,Johnny Wong,Mark Slagell,Vasant Honavar,Les Miller,Yanxin Wang,Xia Wang,Natalia Stakhanova +7 more
TL;DR: In this paper, the integration of Software Fault Tree (SFT) which describes intrusions and Coloured Petri Nets (CPNs) that specifies design is examined for an Intrusion Detection System (IDS).
Journal ArticleDOI
Towards the automatic generation of mobile agents for distributed intrusion detection system
Yanxin Wang,Smruti Ranjan Behera,Johnny Wong,Guy Helmer,Vasant Honavar,Les Miller,Robyn R. Lutz,Mark Slagell +7 more
TL;DR: A tool that automatically translates CPNs that specify IDS design into software intrusion detection agents in MAIDS, which can automatically generate intrusion detection software agents from a high level description of intrusions is presented.
DissertationDOI
A hybrid intrusion detection system
Yanxin Wang,Johnny Wong +1 more
TL;DR: An anomaly detection approach, using STIDE kernel and Markov Chain kernel based one class SVM, that does not need labeled training data is proposed, which can detect known attacks as well as novel unknown attacks.
Book ChapterDOI
Improving feature selection in anomaly intrusion detection using specifications
TL;DR: It is demonstrated through experimental results, that extended finite state machine (EFSA) based anomaly detectors performs better than either the EFSA and SVM anomaly detectors individually.