Contextual Anomaly Detection Methods for Addressing Intrusion Detection
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[1] Jill Slay,et al. Novel Geometric Area Analysis Technique for Anomaly Detection Using Trapezoidal Area Estimation on Large-Scale Networks , 2019, IEEE Transactions on Big Data.
[2] Jugal K. Kalita,et al. An effective unsupervised network anomaly detection method , 2012, ICACCI '12.
[3] Xizhao Wang,et al. Covariance-Matrix Modeling and Detecting Various Flooding Attacks , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[4] Vyas Sekar,et al. An empirical evaluation of entropy-based traffic anomaly detection , 2008, IMC '08.
[5] Patrick Brézillon,et al. Context in problem solving: a survey , 1999, The Knowledge Engineering Review.
[6] Carlos García Garino,et al. Automatic network intrusion detection: Current techniques and open issues , 2012, Comput. Electr. Eng..
[7] Sushma Jain,et al. Hybrid Genetic Fuzzy Rule Based Inference Engine to Detect Intrusion in Networks , 2014, ISI.
[8] Sanjay Ranka,et al. Conditional Anomaly Detection , 2007, IEEE Transactions on Knowledge and Data Engineering.
[9] Jill Slay,et al. Anomaly Detection System Using Beta Mixture Models and Outlier Detection , 2018 .
[10] Xiangjian He,et al. Intrusion detection method based on nonlinear correlation measure , 2014, Int. J. Internet Protoc. Technol..
[11] Jugal K. Kalita,et al. Network Anomaly Detection: Methods, Systems and Tools , 2014, IEEE Communications Surveys & Tutorials.
[12] Kensuke Fukuda,et al. Non-linear regression for bivariate self-similarity identification — application to anomaly detection in Internet traffic based on a joint scaling analysis of packet and byte counts , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[13] Weiru Liu,et al. Natural Laws as a Baseline for Network Anomaly Detection , 2016, 2016 IEEE Trustcom/BigDataSE/ISPA.
[14] Taghi M. Khoshgoftaar,et al. Intrusion detection and Big Heterogeneous Data: a Survey , 2015, Journal of Big Data.
[15] Yizhou Sun,et al. On community outliers and their efficient detection in information networks , 2010, KDD.
[16] Qiang Chen,et al. Multivariate Statistical Analysis of Audit Trails for Host-Based Intrusion Detection , 2002, IEEE Trans. Computers.
[17] Tai-hoon Kim,et al. Linear Correlation-Based Feature Selection for Network Intrusion Detection Model , 2013, SecNet.
[18] Biming Tian,et al. Anomaly detection in wireless sensor networks: A survey , 2011, J. Netw. Comput. Appl..
[19] George Karabatis,et al. Beyond data: contextual information fusion for cyber security analytics , 2016, SAC.
[20] Xiao Qin,et al. A relevant subspace based contextual outlier mining algorithm , 2016, Knowl. Based Syst..
[21] S. T. Sarasamma,et al. Hierarchical Kohonenen net for anomaly detection in network security , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[22] Milos Hauskrecht,et al. Conditional Anomaly Detection with Soft Harmonic Functions , 2011, 2011 IEEE 11th International Conference on Data Mining.
[23] Jill Slay,et al. Big Data Analytics for Intrusion Detection System: Statistical Decision-Making Using Finite Dirichlet Mixture Models , 2017 .
[24] Dong Xiang,et al. Information-theoretic measures for anomaly detection , 2001, Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001.
[25] Mohiuddin Ahmed,et al. Network traffic analysis based on collective anomaly detection , 2014, 2014 9th IEEE Conference on Industrial Electronics and Applications.
[26] Jonathon A. Chambers,et al. Adding contextual information to Intrusion Detection Systems using Fuzzy Cognitive Maps , 2016, 2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA).
[27] George Karabatis,et al. Contextual information fusion for intrusion detection: a survey and taxonomy , 2017, Knowledge and Information Systems.
[28] Roberto Battiti,et al. Identifying intrusions in computer networks with principal component analysis , 2006, First International Conference on Availability, Reliability and Security (ARES'06).
[29] José M. Fernandez,et al. Semantic-based context-aware alert fusion for distributed Intrusion Detection Systems , 2013, 2013 International Conference on Risks and Security of Internet and Systems (CRiSIS).
[30] Gabriel Maciá-Fernández,et al. Anomaly-based network intrusion detection: Techniques, systems and challenges , 2009, Comput. Secur..
[31] Elizabeth Chang,et al. Cyber Situational Awareness for CPS, 5G and IoT , 2017 .
[32] Achim P. Karduck,et al. SIM in light of big data , 2015, 2015 11th International Conference on Innovations in Information Technology (IIT).
[33] M. Shyu,et al. A Novel Anomaly Detection Scheme Based on Principal Component Classifier , 2003 .
[34] Lauro Snidaro,et al. Context-Enhanced Information Fusion , 2016, Advances in Computer Vision and Pattern Recognition.
[35] Srinivasan Parthasarathy,et al. Robust Contextual Outlier Detection: Where Context Meets Sparsity , 2016, CIKM.
[36] Bill N. Schilit,et al. Context-aware computing applications , 1994, Workshop on Mobile Computing Systems and Applications.
[37] Jill Slay,et al. The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set , 2016, Inf. Secur. J. A Glob. Perspect..
[38] Jian Ma,et al. A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering , 2010, Expert Syst. Appl..
[39] Ian Davidson,et al. Discovering Contexts and Contextual Outliers Using Random Walks in Graphs , 2009, 2009 Ninth IEEE International Conference on Data Mining.
[40] Samuel Kounev,et al. Evaluating Computer Intrusion Detection Systems , 2015, ACM Comput. Surv..
[41] Nour Moustafa,et al. UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) , 2015, 2015 Military Communications and Information Systems Conference (MilCIS).
[42] Pat Langley,et al. Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..
[43] Tharam S. Dillon,et al. CorrCorr: A feature selection method for multivariate correlation network anomaly detection techniques , 2019, Comput. Secur..
[44] Jennifer E. Rowley,et al. The wisdom hierarchy: representations of the DIKW hierarchy , 2007, J. Inf. Sci..
[45] Yuefei Zhu,et al. A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks , 2017, IEEE Access.
[46] Shouhuai Xu,et al. Spatiotemporal Patterns and Predictability of Cyberattacks , 2015, PloS one.
[47] Karl N. Levitt,et al. GrIDS A Graph-Based Intrusion Detection System for Large Networks , 1996 .
[48] Stefan Axelsson,et al. Intrusion Detection Systems: A Survey and Taxonomy , 2002 .
[49] Xiangjian He,et al. Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm , 2016, IEEE Transactions on Computers.
[50] Philip K. Chan,et al. PHAD: packet header anomaly detection for identifying hostile network traffic , 2001 .
[51] Aiko Pras,et al. An Overview of IP Flow-Based Intrusion Detection , 2010, IEEE Communications Surveys & Tutorials.
[52] Jaideep Srivastava,et al. Contextual Anomaly Detection in Text Data , 2012, Algorithms.
[53] Bernhard Pfahringer,et al. Winning the KDD99 classification cup: bagged boosting , 2000, SKDD.
[54] Charu C. Aggarwal,et al. Outlier Detection for Temporal Data: A Survey , 2014, IEEE Transactions on Knowledge and Data Engineering.
[55] Wenlong Fu,et al. A Neural Network Based Intrusion Detection Data Fusion Model , 2010, 2010 Third International Joint Conference on Computational Science and Optimization.
[56] James Bailey,et al. Mining multidimensional contextual outliers from categorical relational data , 2013, SSDBM.
[57] Charu C. Aggarwal. Spatial Outlier Detection , 2013 .
[58] Fabio Roli,et al. Information fusion for computer security: State of the art and open issues , 2009, Inf. Fusion.
[59] Marcin Szpyrka,et al. An Entropy-Based Network Anomaly Detection Method , 2015, Entropy.
[60] Hans-Peter Kriegel,et al. Outlier Detection in Arbitrarily Oriented Subspaces , 2012, 2012 IEEE 12th International Conference on Data Mining.
[61] Li Guo,et al. Survey and Taxonomy of Feature Selection Algorithms in Intrusion Detection System , 2006, Inscrypt.
[62] Hsin-Hui Chiu,et al. CEO Bonus Pay and Firm Credit Risk , 2020, International Journal of Risk and Contingency Management.
[63] Simone A. Ludwig. Intrusion detection of multiple attack classes using a deep neural net ensemble , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).
[64] Heiko Paulheim,et al. A decomposition of the outlier detection problem into a set of supervised learning problems , 2015, Machine Learning.
[65] Julia Kiseleva,et al. Context mining and integration into predictive web analytics , 2013, WWW.
[66] Xiangjian He,et al. A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis , 2011, IEEE Transactions on Parallel and Distributed Systems.
[67] James Llinas,et al. An introduction to multisensor data fusion , 1997, Proc. IEEE.
[68] VARUN CHANDOLA,et al. Anomaly detection: A survey , 2009, CSUR.
[69] M. Otto,et al. Outliers in Time Series , 1972 .
[70] Vern Paxson,et al. Outside the Closed World: On Using Machine Learning for Network Intrusion Detection , 2010, 2010 IEEE Symposium on Security and Privacy.
[71] Miriam A. M. Capretz,et al. Contextual anomaly detection framework for big sensor data , 2015, Journal of Big Data.
[72] D. S. Yeung,et al. Network intrusion detection in covariance feature space , 2007, Pattern Recognit..
[73] Xiangjian He,et al. A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis , 2014, IEEE Transactions on Parallel and Distributed Systems.
[74] Mohiuddin Ahmed,et al. A survey of network anomaly detection techniques , 2016, J. Netw. Comput. Appl..
[75] Sui Song,et al. Flow-based Statistical Aggregation Schemes for Network Anomaly Detection , 2006, 2006 IEEE International Conference on Networking, Sensing and Control.