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Intrusion Detection

Part of the book series: Cognitive Intelligence and Robotics ((CIR))

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Abstract

Over the last two decades, information systems have revolutionized with more computer networking, Internets, World Wide Web (www), and Internet of Things (IoT). This resulted in a voluminous increase in both static and dynamic data size, offering a high chance of having potential threats to the global information infrastructure.

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References

  1. Z. Yu, J.J.P. Tsai, Intrusion Detection, A Machine Learning Approach, vol. 3. (Imperial College Press, 2011), ISBN-13: 978-1848164475

    Google Scholar 

  2. M. Abdalla, X. Boyen, C. Chevalier, D. Pointcheval, in Distributed Public-Key Cryptography from Weak Secrets, ed. by S. Jarecki, G. Tsudik Public Key Cryptography—PKC 2009, LNCS 5443, (© Springer, 2009), pp. 139–159

    Google Scholar 

  3. U. Somani, K. Lakhani, M. Mundra, in Implementing Digital Signature with RSA Encryption Algorithm to Enhance the Data Security of Cloud in Cloud Computing. 1st International Conference on Parallel, Distributed and Grid Computing (PDGC—2010) (2010)

    Google Scholar 

  4. D.E. Denning, An intrusion-detection model. IEEE Trans. Softw. Eng. SE-13(2), 222–232 (1987)

    Article  Google Scholar 

  5. M. Ali Aydin, A. Halim Zaim, K. Gökhan Ceylan, A hybrid intrusion detection system design for computer network security. Comput. Electr. Eng. 35, 517–526 (2009)

    Google Scholar 

  6. D. Anderson, T. Lunt, H. Javitz, A. Tamaru, A. Valdes, Safeguard final report: detecting unusual program behavior using the NIDES statistical component. Technical report, Computer Science Laboratory, SRI International, Menlo Park, CA, 1993

    Google Scholar 

  7. A.H. Sung, S. Mukkamala, in Identifying Important Features for Intrusion Detection Using Support Vector Machines and Neural Networks. Proceedings of International Symposium on Applications and the Internet (SAINT 2003) (2003), pp. 209–217

    Google Scholar 

  8. L. Zhou, F. Liu, A swarm-intelligence-based intrusion detection technique. IJCSNS Int. J. Comput. Sci. Netw. Secur. 6(7B) (2006)

    Google Scholar 

  9. S. Owais, V. Snasel, P. Kromer, A. Abraham, Survey: using genetic algorithm approach in intrusion detection systems techniques, in CISIM 2008 (IEEE, 2008), pp. 300–307

    Google Scholar 

  10. P. LaRoche, A. Nur ZincirHeywood, in 802.11 Network Intrusion Detection using Genetic Programming. Proceeding GECCO ‘05 Proceedings of the 2005 Workshops on Genetic and Evolutionary Computation (2005), pp. 170–171

    Google Scholar 

  11. Y. Lin Ying, Y. Zhang, Y.-J. Ou, in The Design and Implementation of Host-based Intrusion Detection System. Third International Symposium on Intelligent Information Technology and Security Informatics (2010)

    Google Scholar 

  12. N. Devarakonda, S. Pamidi, V.V. Kumari, A. Govardhan, Integrated Bayes network and hidden markov model for host based IDS. Int. J. Comput. Appl. 41(20), 45–49 (2012)

    Google Scholar 

  13. J. Shun, H.A. Malki, in Network Intrusion Detection System Using Neural Networks. Fourth International Conference on Natural Computation (2008)

    Google Scholar 

  14. Y.-a. Huang, W. Lee, in A Cooperative Intrusion Detection System for Ad hoc Networks. Proceedings of the 1st ACM workshop on Security of ad hoc and sensor networks (2003), pp. 135–147

    Google Scholar 

  15. Y. Wang, H. Yang, X. Wang, R. Zhang, in Distributed Intrusion Detection System Based on Data Fusion Method. Fifth World Congress on Intelligent Control and Automation. WCICA 2004 (2004)

    Google Scholar 

  16. A. Abraham, R. Jain, J. Thomas, S.Y. Han, D-SCIDS: distributed soft computing intrusion detection system. J. Netw. Comput. Appl. 30(1), 81–98 (2007)

    Article  Google Scholar 

  17. R.P. Lippmann, A. Vitae, R.K. Cunningham, Improving intrusion detection performance using keyword selection and neural networks. Comput. Netw. 34(4), 597–603 (2000)

    Article  Google Scholar 

  18. Nsl-kdd data set for network-based intrusion detection systems. http://nsl.cs.unb.ca/KDD/NSL-KDD.html (2009)

  19. G. MeeraGandhi, K. Appavoo, S.K. Srivatsa, Effective network intrusion detection using classifiers decision trees and decision rules. Int. J. Adv. Netw. Appl. 02(03), 686–692 (2010)

    Google Scholar 

  20. D. Gamberger, N. Lavrac, S. Dzeroski, Noise detection and elimination in data preprocessing: experiments in medical domains. Appl. Artif. Intell. 14, 205–223 (2000)

    Article  Google Scholar 

  21. O.P. Rud, Data Mining Cookbook (Wiley Inc., 2001)

    Google Scholar 

  22. S. Chakrabarti, E. Cox, E. Frank, R.H. Güting, J. Han, X. Jiang, M. Kamber, S.S. Lightstone, T.P. Nadeau, R.E. Neapolitan, D. Pyle, M. Refaat, M. Schneider, T.J. Teorey, I.H. Witten, Data Mining: Know It All, (Elsevier, 2005)

    Google Scholar 

  23. E. Rahm, H.H. Do, Data cleaning: problems and current approaches, bulletin of the technical committee on data engineering. IEEE Comput. Soc. 23(4) (2000)

    Google Scholar 

  24. M.L. Lee, H. Lu, T.W. Ling, Y.T. Ko, in Cleansing Data for Mining and Warehousing. Proceedings of 10th DEXA (1999)

    Google Scholar 

  25. Y. Haitovsky, Missing data in regression analysis. J. Roy. Stat. Soc. 30(1) (1968)

    MATH  Google Scholar 

  26. S. Oba, M. Sato, I. Takemasa, M. Monden, K. Matsubara, S. Ishii, A Bayesian missing value estimation method. Bioinformatics 19, 2088–2096 (2003)

    Article  MATH  Google Scholar 

  27. M. Saar-Tsechansky, F. Provost, Handling missing values when applying classification models. J. Mach. Learn. Res. 8, 1625–1657 (2007)

    MATH  Google Scholar 

  28. J. Han, M. Kamber, J. Pei, in Data Mining: Concepts and Techniques”, Third Edition”, The Morgan Kaufmann Series in Data Management Systems, ISBN-10: 0123814790), Morgan Kaufmann Publishers, 2011

    Google Scholar 

  29. Eibe Frank, Mark A. Hall, Ian H. Witten, in Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. The Morgan Kaufmann Series in Data Management Systems (2011)

    Google Scholar 

  30. M.H. Dunham, Data Mining: Introductory and Advanced Topics (Prentice-Hall, 2002). ISBN 0-13-088892-3

    Google Scholar 

  31. D. Hand, H. Mannila, R. Smyth, Principles of Data Mining (MIT, Cambridge, MA, United States, 2001). ISBN 0-262-08290-X

    Google Scholar 

  32. L.T. Jolliffe, Principal Component Analysis (Springer, Berlin, 1986)

    Book  MATH  Google Scholar 

  33. W.S. Torgerson, Multidimensional Scaling. Psychometrika 17, 401–419 (1952)

    Article  MathSciNet  MATH  Google Scholar 

  34. R. Fisher, The use of multiple measurements in taxonomic problems. Ann. Eugenics 7, 179–188 (1936)

    Article  Google Scholar 

  35. R. Jensen, Q. Shen, Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches (Wiley-IEEE Press, 2008)

    Google Scholar 

  36. J.H. Friedman, J.W. Tukey, A projection pursuit algorithm for exploratory data analysis. IEEE Trans. Comput. C 23(9), 881–890 (1974)

    Article  MATH  Google Scholar 

  37. J.H. Friedman, W. Stuetzle, Projection pursuit regression. J. Am. Stat. Assoc. 76, 817–823 (1981)

    Article  MathSciNet  Google Scholar 

  38. C. Bregler, S. M. Omoundro, in Nonlinear Image Interpolation Using Manifold Learning, ed. by G. Tesauro, D.S. Touretzky, T.K. Leen. Advances in Neural Information Processing Systems, vol. 7 (The MIT Press, 1995), pp. 973–980

    Google Scholar 

  39. M.A. Kramer, Nonlinear principal component analysis using auto associative neural networks. AIChE J. 37(2), 233–243 (1991)

    Article  Google Scholar 

  40. J.B. Tenenbaum, V. de Silva, J.C. Langford, A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)

    Article  Google Scholar 

  41. S.T. Roweis, L.K. Saul, Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)

    Article  Google Scholar 

  42. Z. Pawlak, Rough Sets—Theoretical Aspects of Reasoning About Data (Kluwer Academic Publishers, Boston, London, Dordrecht, 1991), p. 229

    Google Scholar 

  43. Z. Pawlak, Rough set theory and its applications to data analysis. Cybern. Syst. 29, 661–688 (1998)

    Article  MATH  Google Scholar 

  44. R.W. Swiniarski, Rough sets methods in feature reduction and classification. Int. J. Appl. Math. Comput. Sci. 11(3), 565–582 (2001)

    MathSciNet  MATH  Google Scholar 

  45. Y. Zhao, F. Luo, S.K.M. Wong, Y.Y. Yao, in A General Definition of an Attribute Reduct. Rough Sets and Knowledge Technology, Second International Conference, RSKT 2007, Proceedings, LNAI 4481 (2007), pp. 101–108

    Google Scholar 

  46. C. Liu, Y. Li, Y. Qin, in Research on Anomaly Intrusion Detection Based on Rough Set Attribute Reduction. The 2nd International Conference on Computer Application and System Modeling (Published by Atlantis Press, Paris, France, 2012)

    Google Scholar 

  47. Y.Y. Yao, Y. Zhao, Discernibility matrix simplification for constructing attribute reducts. Inf. Sci. 179(5), 867–882 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  48. Y. Zhao, Y. Yao, F. Luo, Data analysis based on discernibility and indiscernibility. Inf. Sci. 177, 4959–4976 (2007)

    Article  MATH  Google Scholar 

  49. A. Murua, W. Stuetzle, J. Tantrum, S. Sieberts, Model based document classification and clustering. Int. J. Tomogr. Stat. 8(W08), 1–24 (2008)

    Google Scholar 

  50. A. Vimal, S.R. Valluri, K. Karlapalem, in An Experiment with Distance Measures for Clustering. International Conference on Management of Data COMAD 2008, Mumbai, India, 17–19 Dec 2008

    Google Scholar 

  51. V. Torra, in Fuzzy c-means for Fuzzy Hierarchical Clustering. Proceedings of FUZZ ‘05 the 14th IEEE International Conference on Fuzzy Systems, 25–25 May 2005. ISBN: 0-7803-9159-4, pp. 646–651

    Google Scholar 

  52. M.W. Trosset, Representing clusters: K-means clustering, self-organizing maps, and multidimensional scaling. Technical Report 08-03, Department of Statistics, Indiana University, Bloomington, IN, 20 Feb 2008

    Google Scholar 

  53. A. Semana, Z. Abu Bakara, A.M. Sapawia, Centre-based hard and soft clustering approaches for Y-STR data. J. Genet. Genealogy 6(1) (2010)

    Google Scholar 

  54. M. Setnes, Fuzzy relational classifier trained by fuzzy clustering. IEEE Trans. Syst., Man, Cybern. Part B: Cybern. 29(5) (1999)

    Article  Google Scholar 

  55. J. Gomez, D. Dasgupta, in Evolving Fuzzy Classifiers for Intrusion Detection. Proceedings of the 2002 IEEE, Workshop on Information Assurance, United States (2002)

    Google Scholar 

  56. N.R. Pal, J.C. Bezdek, On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 3, 370–379 (1995)

    Article  Google Scholar 

  57. D. Dembele, P. Kaster, Fuzzy c-means method for clustering microarray data. Bioinformatics 19(8), 973–980 (2003)

    Article  Google Scholar 

  58. J.C. Bezdek, in Partitioning the Variables for Alternating Optimization of Real-Valued Scalar Fields. Proceedings NAFIPS. 2002 Annual Meeting of the North American Fuzzy Information Processing Society (2002)

    Google Scholar 

  59. S.C.G. Kirkpatrick, C.D. Gelatt, M. Vecchi, Optimization by simulated annealing. Science 220(1983), 49–58 (1983)

    MathSciNet  MATH  Google Scholar 

  60. Y. Wang, Fuzzy clustering analysis by using genetic algorithm. ICIC Exp. Lett. 2(4), 331–337 (2008)

    Google Scholar 

  61. B.I. Wohlmuth, in Discretization Methods and Iterative Solvers Based on Domain Decomposition. Lecture Notes in Computational Science and Engineering (Springer, 2001)

    Google Scholar 

  62. K. Das, O.P. Vyas, A suitability study of discretization methods for associative classifiers. Int. J. Comput. Appl. 5(10) (2010)

    Article  Google Scholar 

  63. G. Agre, S. Peev, On supervised and unsupervised discretization. Cybern. Inf. Tech. 2(2) (2002)

    Google Scholar 

  64. Q. Zhu, L. Lin, M.-L. Shyu, S.-C. Chen, in Effective Supervised Discretization for Classification Based on Correlation Maximization”, (IRI 2011), pp. 390–395

    Google Scholar 

  65. N. Girard, K. Bertet, M. Visani, in A Local Discretization of Continuous Data for Lattices: Technical Aspects, ed. by A. Napoli, V. Vychodil, (CLA 2011). ISBN 978-2-905267-78-8, pp. 409–412

    Google Scholar 

  66. H. Liu, F. Hussain, C.L. Tan, M. Dash, Discretization: an enabling technique. Data Min. Knowl. Disc. 6, 393–423 (2002)

    Google Scholar 

  67. E. Frank, I.H. Witten, in Making Better Use of Global Discretization. Proceedings of the Sixteenth International Conference on Machine Learning (1999), pp. 115–123

    Google Scholar 

  68. J. Gama, L. Torgo, C. Soares, in Dynamic Discretization of Continuous Attributes. Proceeding IBERAMIA ‘98 Proceedings of the 6th Ibero-American Conference on AI: Progress in Artificial Intelligence (Springer, London, UK, 1998), pp. 160–169. ISBN:3-540-64992-1

    Google Scholar 

  69. R. Kohavi, M. Sahami, in Error-Based and Entropy-Based Discretization of Continuous Features. Proceedings of the Second International Conference on Knowledge and Data Mining (AAAI Press, Menlo Park, 1996)

    Google Scholar 

  70. J. Gama, C. Pinto, in Discretization from Data Streams: Applications to Histograms and Data Mining. Proceeding of the 2006 ACM Symposium on Applied Computing (2006), pp. 662–667. ISBN:1-59593-108-2, 2006

    Google Scholar 

  71. H. Liu, R. Setiono, in Chi2: Feature Selection and Discretization of Numeric Attributes. Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence (1995)

    Google Scholar 

  72. F.E.H. Tay, L. Shen, A modified Chi2 algorithm for discretization. IEEE Trans. Knowl. Data Eng. 14(3) (2002)

    Article  Google Scholar 

  73. R.-P. Li, Z.-O. Wang, in An Entropy-Based Discretization Method for Classification Rules with Inconsistency Checking. Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, 4–5 Nov 2002

    Google Scholar 

  74. R. Jin, Y. Breitbart, C. Muoh, Data discretization unification. J. Knowl. Inf. Syst. Arch. 19(1), 1–29 (2009)

    Article  Google Scholar 

  75. R. Bertelsen, T.R. Martinez, in Extending ID3 Through Discretization of Continuous Inputs, FLAIRS’94 Florida Artificial Intelligence Research Symposium (1994), pp. 122–125

    Google Scholar 

  76. R. Butterworth, D.A. Simovici, G.S. Santos, L. O-M, A greedy algorithm for supervised discretization. J. Biomed. Inform. 37(4), 285–292 (2004)

    Article  Google Scholar 

  77. J.Y. Ching, K.C. Wong Andrew, K.K.C. Chan, Inductive learning from continuous and mixed-mode data. IEEE Trans. Pattern Anal. Mach. Intell. (1995)

    Google Scholar 

  78. R. Giraldez, J.S., Aguilar-ruiz et al., Discretization oriented to decision rules generation. Front. Artif. Intell. Appl. (2002)

    Google Scholar 

  79. T. Mitchell, Machine Learning (McGraw-Hill, New York, 1997)

    MATH  Google Scholar 

  80. J.-S. Roger Jang, C.-T. Sun, E. Mizutani, in Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (Prentice Hall, 1997). ISBN: 0-13-261066-3

    Google Scholar 

  81. I. Czarnowski, P. Jędrzejowicz, Instance reduction approach to machine learning and multi-database mining. Ann UMCS Inf AI 4, 60–71 (2006)

    Google Scholar 

  82. Y. Li, L. Guo, An active learning based TCM-KNN algorithm for supervised network intrusion detection, in 26th Computers and Security (2007), pp. 459–467

    Article  Google Scholar 

  83. A. Niculescu-Mizil, R. Caruana, in Predicting Good Probabilities with Supervised Learning. Proceedings of 22nd International Conference on Machine Learning (ICML-2005) (ACM Press, New York, NY, USA, 2005), pp. 625–632. ISBN 1-59593-180-52005

    Google Scholar 

  84. G.E. Batista, M.C. Monard, An analysis of four missing data treatment methods for supervised learning. Appl. Artif. Intell. 17(5–6), 519–533 (2003)

    Article  Google Scholar 

  85. S.B. Kotsiantis, D. Kanellopoulos, P.E. Pintelas, Data preprocessing for supervised leaning. Int. J. Comput. Sci. 1(2) (2006). ISSN 1306-4428

    Google Scholar 

  86. S.B. Kotsiantis, Supervised machine learning: a review of classification techniques. Informatica 31(2007), 249–268 (2007)

    MathSciNet  MATH  Google Scholar 

  87. T.B.K.A. Bouchard-Côté, J.D.D. Klein, in Painless Unsupervised Learning with Features. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL (Los Angeles, California). 2010 Association for Computational Linguistics (2010), pp. 582–590

    Google Scholar 

  88. Q.V. Le, M. Ranzato, R. Monga, M. Devin, K. Chen, G.S. Corrado, J. Dean, A.Y. Ng, in Building High-level Features Using Large Scale Unsupervised Learning. Appearing in Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland, UK, 2012

    Google Scholar 

  89. C. Liu, J. Xie,, J. Xie, J. Xie, in Stochastic Unsupervised Learning on Unlabeled Data. JMLR: Workshop and Conference Proceedings, 2012 Workshop on Unsupervised and Transfer Learning, vol. 27, pp. 111–122

    Google Scholar 

  90. J. Zhuang, J. Wang, X. Lan, in Unsupervised Multiple Kernel Learning. JMLR: Workshop and Conference Proceedings, Asian Conference on Machine Learning, vol. 20, (2011), pp. 129–144

    Google Scholar 

  91. T. Maul, S. Baba, Unsupervised learning in second-order neural networks for motion analysis. Neurocomputing 74, 884–895 (2011)

    Article  Google Scholar 

  92. L.P. Kaelbling, M.L. Littman, A.W. Moore, Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)

    Article  Google Scholar 

  93. R.S. Sutton, A.G. Barto, in Reinforcement Learning: An Introduction (The MIT Press, 1998). ISBN-10: 0262193981

    Google Scholar 

  94. M.A. Wiering, H.P. van Hasselt, Ensemble algorithms in reinforcement learning. IEEE Trans. Syst. Man, Cybern. Part B 38(4), 930–936 (2008)

    Article  Google Scholar 

  95. W. Maydl, B. Sick, in Recurrent and Non-recurrent Dynamic Network Paradigms: A Case Study. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000

    Google Scholar 

  96. C. Rodrigues, P. Gérard, C. Rouveirol, in On and Off-Policy Relational Reinforcement Learning. Late-Breaking Papers of the International Conference on Inductive Logic Programming (2008)

    Google Scholar 

  97. D.R. Lowne, S.J. Roberts, R. Garnett, Sequential non stationary dynamic classification. Pattern Recognit. 43, 897–905 (2010)

    Article  MATH  Google Scholar 

  98. S.M. Lee, S.J. Roberts, in Sequential Dynamic Classification Using Latent Variable Models. Advance Access publication on 27 Jan 2010, Published by Oxford University Press on behalf of The British Computer Society, 2010

    Google Scholar 

  99. S.M. Lee, S.J. Roberts, Sequential dynamic classification using latent variable models. Technical report, Technical Report PARG-08-02, University of Oxford, 2008

    Google Scholar 

  100. E. Even-Dar, Y. Mansour, Learning rates for Q-learning. J. Mach. Learn. Res. 5, 1–25 (2003)

    Google Scholar 

  101. R. Dearden, N. Friedman, S. Russell, in Bayesian Q-learning. Fifteenth National Conference on Artificial Intelligence (AAAI) (1998)

    Google Scholar 

  102. Á. Herrero, E. Corchado, Multiagent systems for network intrusion detection: a review, in Computational Intelligence in Security for Information Systems, AISC 63, ed. by Á. Herrero et al. (Springer, Berlin, Heidelberg, 2009) springerlink.com ©, pp. 143–154

    Google Scholar 

  103. W. Lee, in A Data Mining Framework for Constructing Features and Models for Intrusion Detection Systems. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences (Columbia University, 1999)

    Google Scholar 

  104. W. Lee, S.J. Stolfo, K.W. Mok, in A Data Mining Framework for Building Intrusion Detection Models. IEEE Symposium on Security and Privacy (1999), pp. 120–132

    Google Scholar 

  105. A.L. Servin, Multi-Agent Reinforcement Learning for Intrusion Detection, Ph.D. thesis, The University of York, 2009

    Google Scholar 

  106. J.P. Anderson, in Computer Security Threat Monitoring and Surveillance. Technical report (James P. Anderson Co., Fort Washington, PA., 1980)

    Google Scholar 

  107. D.E. Denning, Information Warfare and Security (Addison Wesley Reading, Ma, 1999)

    Google Scholar 

  108. D.E. Denning, An intrusion-detection model. IEEE Trans. Softw. Eng. SE-13, 222–232 (1987)

    Article  Google Scholar 

  109. S. Axelsson, in Intrusion Detection Systems: A Survey and Taxonomy. Technical Report 99-15 (Chalmers University, 2000)

    Google Scholar 

  110. K. Shafi, An Online and Adaptive Signature-based Approach for Intrusion Detection Using Learning Classifier Systems, PhD thesis, 2008

    Google Scholar 

  111. V. Chandola, A. Banerjee, V. Kumar, Anomaly detection: a survey. ACM Comput. Surv. 41(3) (2009)

    Article  Google Scholar 

  112. A. Lazarevic, L. Ertoz, V. Kumar, A. Ozgur, J. Srivastava, in A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection. Proceedings of the Third SIAM International Conference on Data Mining, 1–3 May 2003, San Francisco, CA, 2003

    Google Scholar 

  113. J.P. Early, Behavioral Feature Extraction For Network Anomaly Detection. CERIAS Tech Report 2005-55, Doctoral Dissertation, Purdue University West Lafayette, IN, USA (2005). ISBN:0-542-34849-7

    Google Scholar 

  114. R. Sekar, A. Gupta, J. Frullo, T. Hanbhag, A. Tiwari, H. Yang, S. Zhou, Specification-based anomaly detection: a new approach for detecting. Int. J. Netw. Secur. 1(2), 84–102 (2005)

    Google Scholar 

  115. K. Wang, J.J. Parekh, S.J. Stolfo, in Anagram: A Content Anomaly Detector Resistant To Mimicry Attack. Proceedings of the Ninth International Symposium on Recent Advances in Intrusion Detection (RAID), 2006

    Google Scholar 

  116. T. Won, C. Alaettinoglu, in Internet Routing Anomaly Detection and Visualization. Proceedings of International Conference on Dependable Systems and Networks (IEEE, 2005), pp. 172–181

    Google Scholar 

  117. Uci: Machine Learning Repository. http://archive.ics.uci.edu/ml/

  118. R.L. Kruse, A.J. Ryba, in Data Structures and Program Design in C++ (Prentice Hall, 1998). ISBN-13:9780137689958

    Google Scholar 

  119. J.F. Peters (ed.), in Transactions on Rough Sets XI. Lecture Notes in Computer Science/Transactions on Rough Sets (Springer, Berlin, 2010)

    MATH  Google Scholar 

  120. J.F. Peters, A. Skowron, C.-C. Chan, J.W. Grzymala-Busse, W.P. Ziarko (eds.), in Transactions on Rough Sets XIII. Lecture Notes in Computer Science/Transactions on Rough Sets (Springer, 2011)

    Google Scholar 

  121. X. Ren, Intrusion detection method using protocol classification and rough set based support vector machine. Comput. Inf. Sci. 2(4), 100–108 (2009)

    Google Scholar 

  122. Y. Caballero, R. Bello, Y. Salgado, M.M. García, A method to edit training set based on rough sets. Int. J. Comput. Intell. Res. 3(3), 219–229. ISSN 0973-1873 (2007)

    Google Scholar 

  123. D. Slezak, G. Wang, M.S. Szczuka, I. Düntsch, Y. Yao, in Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. 10th International Conference, RSFDGrC 2005, Regina, Canada, Aug 31—Sept 3, 2005, Proceedings, Part I (Springer, 2005)

    Google Scholar 

  124. D. Deng, H. Huang, Dynamic reduction based on rough sets in incomplete decision systems. Rough Sets Knowl Technol LNCS 4481(2007), 76–83 (2007)

    Article  Google Scholar 

  125. A. Skowron, C. Rauszer, The discernibility matrices and functions in information systems, in Intelligent Decision Support-Handbook of Applications and advances of the Rough Sets Theory, ed. by Slowinski (1991), pp. 331–362

    Chapter  Google Scholar 

  126. C. Velayutham, K. Thangavel, Unsupervised quick reduct algorithm using rough set theory. J. Electron. Sci. Technol. 9(3) (2011)

    Google Scholar 

  127. S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy, A fast iterative nearest point algorithm for support vector machine classifier design. IEEE Trans. Neural Netw. 11, 124–136 (2000)

    Article  Google Scholar 

  128. C. Cortes, V.N. Vapnik, Support vector network. Mach. Learn. 20, 273–297 (1995)

    MATH  Google Scholar 

  129. V.N. Vapnik, Statistical Learning Theory (Wiley Inc., New York, 1998)

    MATH  Google Scholar 

  130. B.E. Boser, I. Guyon, V. Vapnik, in A Training Algorithm for Optimal Margin Classifier. Proceedings of Fifth Annual Conference on Computational Learning Theory (COLT-92), USA, 1992, pp. 144–152

    Google Scholar 

  131. B. Zadrozny, in Learning and Evaluating Classifiers under Sample Selection Bias. International Conference on Machine Learning ICML’04, 2004

    Google Scholar 

  132. R. Jensen, Combining Rough and Fuzzy Sets for Feature Selection. Ph.D. thesis (2005)

    Google Scholar 

  133. S.K. Pal, P. Mitra, Case generation using rough sets with fuzzy representation. IEEE Trans. Knowl. Data Eng. 16(3) (2004)

    Article  Google Scholar 

  134. Richard Jensen, Qiang Shen, Fuzzy-rough sets assisted attribute selection. IEEE Trans. Fuzzy Syst. 15, 73–89 (2007)

    Article  Google Scholar 

  135. R. Jensen, Q. Shen, in Fuzzy-Rough Sets for Descriptive Dimensionality Reduction. Proceedings of the 11th International Conference on Fuzzy Systems (2002), pp. 29–34

    Google Scholar 

  136. M. Yang, S. Chen, X. Yang, in A Novel Approach of Rough Set-Based Attribute Reduction Using Fuzzy Discernibility Matrix. Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery, vol. 03 (IEEE Computer Society, 2007), pp. 96–101

    Google Scholar 

  137. Hu Qinghua, Yu. Daren, Zongxia Xie, Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recogn. Lett. 27, 414–423 (2006)

    Article  Google Scholar 

  138. R. Jensen, Q. Shen, Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. IEEE Trans. Knowl. Data Eng. 17(1) (2005)

    Google Scholar 

  139. R. Jensen, Q. Shen, Fuzzy-rough attribute reduction with application to web categorization, fuzzy sets and systems 141(3), 469–485 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  140. H. Guohua, S. Yuemei, in An Attribute Reduction Method Based on Fuzzy-Rough Sets Theories. First International Workshop on Education Technology and Computer Science (2009)

    Google Scholar 

  141. R. Jensen, Q. Shen, in Rough and Fuzzy Sets for Dimensionality Reduction (2001)

    Google Scholar 

  142. R. Jensen, A. Tuson, Q. Shen, in Extending Propositional Satisfiability to Determine Minimal Fuzzy-Rough Reducts. Proceedings of FUZZ-IEEE (2010)

    Google Scholar 

  143. R. Jensen, C. Cornelis, in Fuzzy-Rough Instance Selection. Proceedings of FUZZ-IEEE (2010)

    Google Scholar 

  144. E.P. Ephzibah, B. Sarojini, J. Emerald Sheela, A study on the analysis of genetic algorithms with various classification techniques for feature selection. Int. J. Comput. Appl. 8(8) (2010)

    Article  Google Scholar 

  145. M. Mitchell, An Introduction to Genetic Algorithms (MIT Press, Cambridge, MA, 1996)

    MATH  Google Scholar 

  146. Y. Rama Devi, P. Venu Gopal, P.S.V.S. Sai Prasad, Fuzzy rough data reduction using SVD. Int. J. Comput. Electr. Eng. 3(3) (2011)

    Google Scholar 

  147. J.R. Anaraki, M. Eftekhari, in Improving Fuzzy-Rough Quick Reduct for Feature Selection. IEEE 19th Iranian Conference on Electrical Engineering (ICEE, 2011), pp. 1–6

    Google Scholar 

  148. S. Kirkpatrik, C. Gelatt, M. Vecchi, Optimization by simulated annealing. Science 220, 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  149. S. Bandyopadhyay, Simulated annealing using a reversible jump markov chain monte carlo algorithm for fuzzy clustering. IEEE Trans. Knowl. Data Eng. 17(4) (2005)

    Article  Google Scholar 

  150. X.Y. Wang, G. Whitwell, J.M. Garibaldi, in The Application Of A Simulated Annealing Fuzzy Clustering Algorithm For Cancer Diagnosis. Proceedings of IEEE 4th International Conference on Intelligent Systems Design and Application, Budapest, Hungary, 26–28 Aug 2004, pp. 467–472

    Google Scholar 

  151. N. Sengupta, A. Srivastava, J. Sil, in Chapter 14 Reduction of Data Size in Intrusion Domain Using Modified Simulated Annealing Fuzzy Clustering Algorithm (Springer Science and Business Media LLC, 2013)

    Google Scholar 

  152. C.J.C.H. Watkins, P. Dayan, Q-learning. Mach. Learn. 8(3–4), 279–292 (1992)

    Google Scholar 

  153. S. Manju, M. Punithavalli, An analysis of Q-learning algorithms with strategies of reward function. Int. J. Comput. Sci. Eng. (IJCSE), 3(2) (2011). ISSN: 0975-3397

    Google Scholar 

  154. A.G. Barto, S. Mahadevan, Recent advances in hierarchical reinforcement learning. Discrete Event Dyn. Syst. 13, 341–379 (2003)

    Google Scholar 

  155. T.G. Dietterich, Hierarchical reinforcement learning with the maxq value function decomposition. J. Artif. Intell. Res. 13, 227–303 (2000)

    Article  MathSciNet  MATH  Google Scholar 

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Sengupta, N., Sil, J. (2020). Introduction. In: Intrusion Detection. Cognitive Intelligence and Robotics. Springer, Singapore. https://doi.org/10.1007/978-981-15-2716-6_1

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