Anomaly intrusion detection based on genetic optimization and fuzzy rules mining

Journal of Computer Applications ◽

10.3724/sp.j.1087.2009.02227 ◽

2009 ◽

Vol 29 (8) ◽

pp. 2227-2229

Author(s):

Dong-sheng XU ◽

Xiao-yan AI ◽

Shi-liang YAN

Keyword(s):

Intrusion Detection ◽

Fuzzy Rules ◽

Genetic Optimization ◽

Anomaly Intrusion Detection

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Handling Web and Database Requests Using Fuzzy Rules for Anomaly Intrusion Detection

Journal of Computer Science ◽

10.3844/jcssp.2011.255.261 ◽

2011 ◽

Vol 7 (2) ◽

pp. 255-261 ◽

Author(s):

Kadirvelu

Keyword(s):

Intrusion Detection ◽

Fuzzy Rules ◽

Anomaly Intrusion Detection

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The Influence of Salp Swarm Algorithm-Based Feature Selection on Network Anomaly Intrusion Detection

IEEE Access ◽

10.1109/access.2021.3102095 ◽

2021 ◽

pp. 1-1

Author(s):

Alanoud Alsaleh ◽

Wojdan Binsaeedan

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Salp Swarm Algorithm ◽

Swarm Algorithm ◽

Anomaly Intrusion Detection

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Unsupervised Anomaly Intrusion Detection via Localized Bayesian Feature Selection

2011 IEEE 11th International Conference on Data Mining ◽

10.1109/icdm.2011.152 ◽

2011 ◽

Author(s):

Wentao Fan ◽

Nizar Bouguila ◽

Djemel Ziou

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Anomaly Intrusion Detection

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A Fraud Detection System Based on Anomaly Intrusion Detection Systems for E-Commerce Applications

Computer and Information Science ◽

10.5539/cis.v7n2p117 ◽

2014 ◽

Vol 7 (2) ◽

Author(s):

Daniel Massa ◽

Raul Valverde

Keyword(s):

Intrusion Detection ◽

Detection System ◽

Fraud Detection ◽

Intrusion Detection Systems ◽

Detection Systems ◽

Anomaly Intrusion Detection

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Using Naive Bayes with AdaBoost to Enhance Network Anomaly Intrusion Detection

2010 Third International Conference on Intelligent Networks and Intelligent Systems ◽

10.1109/icinis.2010.133 ◽

2010 ◽

Author(s):

Wei Li ◽

QingXia Li

Keyword(s):

Intrusion Detection ◽

Naive Bayes ◽

Naïve Bayes ◽

Anomaly Intrusion Detection

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Anomaly intrusion detection method for vehicular networks based on survival analysis

Vehicular Communications ◽

10.1016/j.vehcom.2018.09.004 ◽

2018 ◽

Vol 14 ◽

pp. 52-63 ◽

Author(s):

Mee Lan Han ◽

Byung Il Kwak ◽

Huy Kang Kim

Keyword(s):

Survival Analysis ◽

Intrusion Detection ◽

Vehicular Networks ◽

Detection Method ◽

Anomaly Intrusion Detection

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Ensemble Learning Methods for Anomaly Intrusion Detection System in Smart Grid

2021 IEEE International Conference on Electro Information Technology (EIT) ◽

10.1109/eit51626.2021.9491891 ◽

2021 ◽

Author(s):

Tala Talaei Khoei ◽

Ghilas Aissou ◽

When Chen Hu ◽

Naima Kaabouch

Keyword(s):

Intrusion Detection ◽

Ensemble Learning ◽

Intrusion Detection System ◽

Detection System ◽

Learning Methods ◽

Anomaly Intrusion Detection

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Anomaly Intrusion Detection for System Call Using the Soundex Algorithm and Neural Networks

10th IEEE Symposium on Computers and Communications (ISCC'05) ◽

10.1109/iscc.2005.33 ◽

2005 ◽

Author(s):

ByungRae Cha ◽

B. Vaidya ◽

Seungjo Han

Keyword(s):

Neural Networks ◽

Intrusion Detection ◽

System Call ◽

Anomaly Intrusion Detection

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Native API Based Windows Anomaly Intrusion Detection Method Using SVM

IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing -Vol 1 (SUTC'06) ◽

10.1109/sutc.2006.1636219 ◽

2006 ◽

Author(s):

Miao Wang ◽

Cheng Zhang ◽

Jingjing Yu

Keyword(s):

Intrusion Detection ◽

Detection Method ◽

Anomaly Intrusion Detection

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LDFGB Algorithm for Anomaly Intrusion Detection

Information and Communication Technology - Lecture Notes in Computer Science ◽

10.1007/978-3-642-55032-4_39 ◽

2014 ◽

pp. 396-404

Author(s):

Shang-nan Yin ◽

Zhi-guo Chen ◽

Sung-Ryul Kim

Keyword(s):

Intrusion Detection ◽

Anomaly Intrusion Detection

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