Structural TLR algorithm for anomaly detection based on Danger Theory

2012 9th International ISC Conference on Information Security and Cryptology ◽

10.1109/iscisc.2012.6408214 ◽

2012 ◽

Author(s):

Fahimeh Soltani Nejad ◽

Reyhaneh Salkhi ◽

Reza Azmi ◽

Boshra Pishgoo

Keyword(s):

Anomaly Detection ◽

Danger Theory

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Multi-agents Artificial Immune System (MAAIS) Inspired by Danger Theory for Anomaly Detection

2007 International Conference on Computational Intelligence and Security Workshops (CISW 2007) ◽

10.1109/cisw.2007.4425560 ◽

2007 ◽

Cited By ~ 10

Author(s):

Haidong Fu ◽

Xiguo Yuan ◽

Na Wang

Keyword(s):

Immune System ◽

Anomaly Detection ◽

Artificial Immune System ◽

Artificial Immune ◽

Danger Theory ◽

Multi Agents

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Biological inspired anomaly detection based on danger theory

The 5th Conference on Information and Knowledge Technology ◽

10.1109/ikt.2013.6620047 ◽

2013 ◽

Author(s):

Soudeh Behrozinia ◽

Reza Azmi ◽

M Reza Keyvanpour ◽

Boshra Pishgoo

Keyword(s):

Anomaly Detection ◽

Danger Theory

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SHADuDT: Secure hypervisor-based anomaly detection using danger theory

Computers & Security ◽

10.1016/j.cose.2013.08.005 ◽

2013 ◽

Vol 39 ◽

pp. 268-288 ◽

Cited By ~ 5

Author(s):

Reza Azmi ◽

Boshra Pishgoo

Keyword(s):

Anomaly Detection ◽

Danger Theory

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An Anomaly Detection Model Based on Cloud Model and Danger Theory

Trustworthy Computing and Services - Communications in Computer and Information Science ◽

10.1007/978-3-662-43908-1_15 ◽

2014 ◽

pp. 115-122 ◽

Cited By ~ 3

Author(s):

Wenhao Wang ◽

Chen Zhang ◽

Quan Zhang

Keyword(s):

Anomaly Detection ◽

Cloud Model ◽

Danger Theory ◽

Detection Model ◽

Model Based

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A Two-Stage Temporal Anomaly Detection Algorithm Based on Danger Theory

Advances in Intelligent Systems and Computing - International Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 2018 ◽

10.1007/978-3-319-98776-7_53 ◽

2018 ◽

pp. 485-494

Author(s):

Min Hu ◽

Zhiwei Ji ◽

Ye Guo ◽

Ke Yan ◽

Haigen Hu

Keyword(s):

Anomaly Detection ◽

Detection Algorithm ◽

Two Stage ◽

Danger Theory

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Anomaly Detection in Sensor Data

Journal of Applied Reliability ◽

10.33162/jar.2018.03.18.1.20 ◽

2018 ◽

Vol 18 (1) ◽

pp. 20-32 ◽

Cited By ~ 2

Author(s):

Jong-Min Kim ◽

Jaiwook Baik

Keyword(s):

Anomaly Detection ◽

Sensor Data

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An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.136.363 ◽

2016 ◽

Vol 136 (3) ◽

pp. 363-372

Author(s):

Takaaki Nakamura ◽

Makoto Imamura ◽

Masashi Tatedoko ◽

Norio Hirai

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Time Series Data ◽

Detection Method ◽

Series Data

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Proposal of the Anomaly Detection Method of Live-alone by LOF using Multiple Sensors

IEEJ Transactions on Fundamentals and Materials ◽

10.1541/ieejfms.135.749 ◽

2015 ◽

Vol 135 (12) ◽

pp. 749-755

Author(s):

Taiyo Matsumura ◽

Ippei Kamihira ◽

Katsuma Ito ◽

Takashi Ono

Keyword(s):

Anomaly Detection ◽

Detection Method ◽

Multiple Sensors

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Large-Scale Data Learning Method for Anomaly Detection using Machine Learning for Monitoring Vibration in Vehicle Equipment

IEEJ Transactions on Industry Applications ◽

10.1541/ieejias.140.480 ◽

2020 ◽

Vol 140 (6) ◽

pp. 480-487

Author(s):

Minoru Kondo

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Large Scale ◽

Learning Method ◽

Large Scale Data ◽

Scale Data

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CKMSA: an Anomaly Detection Process Based on K-Means and Simulated Annealing Algorithms

International Review on Computers and Software (IRECOS) ◽

10.15866/irecos.v11i4.9552 ◽

2016 ◽

Vol 11 (4) ◽

pp. 373

Author(s):

Hamza Kamal Idrissi ◽

Zaid Kartit ◽

Ali Kartit ◽

Mohamed El Marraki

Keyword(s):

Simulated Annealing ◽

Anomaly Detection ◽

Detection Process ◽

Annealing Algorithms

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