On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on Intrusion Detection Systems

Abadeh, 2007, Intrusion detection using a fuzzy genetics-based learning algorithm, Journal of Network and Computer Applications, 30, 414, 10.1016/j.jnca.2005.05.002 Abadeh, 2011, Design and analysis of genetic fuzzy systems for intrusion detection in computer networks, Expert Systems with Applications, 38, 7067, 10.1016/j.eswa.2010.12.006 Alcalá;-Fdez, 2009, KEEL: A software tool to assess evolutionary algorithms for data mining problems, Soft Computing, 13, 307, 10.1007/s00500-008-0323-y Alcala, 2007, A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection, IEEE Transactions on Fuzzy Systems, 15, 616, 10.1109/TFUZZ.2006.889880 Alcalá-Fdez, 2011, A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning, IEEE Transactions on Fuzzy Systems, 19, 857, 10.1109/TFUZZ.2011.2147794 Alcalá, 2012, Special issue on evolutionary fuzzy systems, International Journal Of Computational Intelligence Systems, 5, 209, 10.1080/18756891.2012.685261 Allwein, 2000, Reducing multiclass to binary: A unifying approach for margin classifiers, Journal of Machine Learning Research, 1, 113 Axelsson, S. (1998). Research in intrusion-detection systems: A survey. Technical Report 98–17, Department of Computer Engineering, Chalmers University of Technology, Goteborg, Sweden. Axelsson, 2000, The base-rate fallacy and the difficulty of intrusion detection, ACM Transaction on Information Systems and Security, 3, 186, 10.1145/357830.357849 Benferhat, 2013, An intrusion detection and alert correlation approach based on revising probabilistic classifiers using expert knowledge, Applied Intelligence, 38, 520, 10.1007/s10489-012-0383-7 Casillas, 2005, Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction, IEEE Transactions on Fuzzy Systems, 13, 13, 10.1109/TFUZZ.2004.839670 Chang, 2011, LIBSVM: A library for support vector machines, ACM Transactions on Intelligent Systems and Technology, 2, 27:1, 10.1145/1961189.1961199 Chebrolu, 2005, Feature deduction and ensemble design of intrusion detection systems, Computers and Security, 24, 295, 10.1016/j.cose.2004.09.008 Chen, 2007, Hybrid flexible neural-tree-based intrusion detection systems, International Journal Intelligent Systems, 22, 337, 10.1002/int.20203 Chung, 2012, A hybrid network intrusion detection system using simplified swarm optimization (SSO), Applied Soft Computing, 12, 3014, 10.1016/j.asoc.2012.04.020 Cordón, 1999, A proposal on reasoning methods in fuzzy rule-based classification systems, International Journal of Approximate Reasoning, 20, 21, 10.1016/S0888-613X(00)88942-2 Cordón, 2004, Ten years of genetic fuzzy systems: Current framework and new trends, Fuzzy Sets and Systems, 141, 5, 10.1016/S0165-0114(03)00111-8 Debar, 1999, Towards a taxonomy of intrusion-detection systems, Computer Networks, 31, 805, 10.1016/S1389-1286(98)00017-6 Denning, 1987, An intrusion detection model, IEEE Transactions on Software Engineering, 13, 222, 10.1109/TSE.1987.232894 Dickerson, J., & Dickerson, J. (2000). Fuzzy network profiling for intrusion detection. In Proceedings of the 19th International Conference of the North American Fuzzy Information Society (NAFIPS’00), (pp. 301–306). Atlanta, GA, USA. IEEE Press. Dickerson, J., Juslin, J., Koukousoula, O., & Dickerson, J. (2001). Fuzzy intrusion detection. In Proceedings of the 20th International Conference of the North American Fuzzy Information Society (NAFIPS’01) and Joint the 9th IFSA World Congress, vol. 3, (pp. 1506–1510). Vancouver, Canada. IEEE Press. Dietterich, 2000, An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization, Machine Learning, 40, 139, 10.1023/A:1007607513941 Eiben, 2003, Introduction to evolutionary computation Fernández, 2010, Solving multi-class problems with linguistic fuzzy rule based classification systems based on pairwise learning and preference relations, Fuzzy Sets and Systems, 161, 3064, 10.1016/j.fss.2010.05.016 Florez, G., Bridges, S., & Vaughn, R. (2002). An improved algorithm for fuzzy data mining for intrusion detection. In Proceedings of the 21st North American Fuzzy Information Processing Society Conference (NAFIPS’02), (pp. 457–462). New Orleans, LA. Fürnkranz, 2002, Round robin classification, Journal of Machine Learning Research, 2, 721 Gacto, 2011, Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures, Information Sciences, 181, 4340, 10.1016/j.ins.2011.02.021 Galar, 2011, An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes, Pattern Recognition, 44, 1761, 10.1016/j.patcog.2011.01.017 Gomez, J. % Dasgupta, D. (2001). Evolving fuzzy classifiers for intrusion detection. In Proceedings of IEEE Workshop on Information Assurance, (pp. 68–75). United States Military Academy, West Point, New York. Greene, 1993, Competition-based induction of decision models from examples, Machine Learning, 13, 229, 10.1007/BF00993044 Guo, 2014, A distance sum-based hybrid method for intrusion detection, Applied Intelligence, 40, 178, 10.1007/s10489-013-0452-6 Hall, 2009, The WEKA data mining software: An update, SIGKDD Explorations, 11, 10, 10.1145/1656274.1656278 Han, 2006, Data mining Hastie, 1998, Classification by pairwise coupling, The Annals of Statistics, 26, 451, 10.1214/aos/1028144844 Herrera, 2008, Genetic fuzzy systems: Taxonomy, current research trends and prospects, Evolutionary Intelligence, 1, 27, 10.1007/s12065-007-0001-5 Hüllermeier, 2008, Learning valued preference structures for solving classification problems, Fuzzy Sets and Systems, 159, 2337, 10.1016/j.fss.2008.01.021 Hüllermeier, 2010, Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting, Pattern Recognition, 43, 128, 10.1016/j.patcog.2009.06.013 Ishibuchi, 2004 Ishibuchi, 2005, Rule weight specification in fuzzy rule–based classification systems, IEEE Transactions on Fuzzy Systems, 13, 428, 10.1109/TFUZZ.2004.841738 Kavsek, 2006, Apriori-sd: Adapting association rule learning to subgroup discovery, Applied Artificial Intelligence, 20, 543, 10.1080/08839510600779688 Khor, 2012, A cascaded classifier approach for improving detection rates on rare attack categories in network intrusion detection, Applied Intelligence, 36, 320, 10.1007/s10489-010-0263-y Kuok, 1998, Mining fuzzy association rules in databases, SIGMOD Record, 27, 41, 10.1145/273244.273257 Lee, 2000, A framework for constructing features and models for intrusion detection systems, ACM Transactions on Information Systems and Security, 3, 227, 10.1145/382912.382914 Lee, 1999, A data mining framework for building intrusion detection models, 120 Lee, 2000, Adaptive intrusion detection: A data mining approach, Artificial Intelligence Review, 16, 533, 10.1023/A:1006624031083 López, 2013, An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics, Information Sciences, 250, 113, 10.1016/j.ins.2013.07.007 López, 2013, Addressing covariate shift for genetic fuzzy systems classifiers: A case of study with farc-hd for imbalanced datasets, 1 Lorena, 2008, A review on the combination of binary classifiers in multiclass problems, Artificial Intelligence Review, 30, 19, 10.1007/s10462-009-9114-9 Muda, 2011, A k-means and naive bayes learning approach for better intrusion detection, Information Technology Journal, 10, 648, 10.3923/itj.2011.648.655 Orlovsky, 1978, Decision-making with a fuzzy preference relation, Fuzzy Sets and Systems, 1, 155, 10.1016/0165-0114(78)90001-5 Özyer, 2007, Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening, Journal of Network and Computer Applications, 30, 99, 10.1016/j.jnca.2005.06.002 Patcha, 2007, An overview of anomaly detection techniques: Existing solutions and latest technological trends, Computer Networks, 51, 3448, 10.1016/j.comnet.2007.02.001 Peddabachigari, 2007, Modeling intrusion detection system using hybrid intelligent systems, Journal of Network and Computer Applications, 30, 114, 10.1016/j.jnca.2005.06.003 Quinlan, 1993 Sáez, 2014, Analyzing the presence of noise in multi-class problems: Alleviating its influence with the one-vs-one decomposition, Knowledge and Information Systems, 38, 179, 10.1007/s10115-012-0570-1 Sanz, 2013, Ivturs: A linguistic fuzzy rule-based classification system based on a new interval-valued fuzzy reasoning method with tuning and rule selection, IEEE Transactions on Fuzzy Systems, 21, 399, 10.1109/TFUZZ.2013.2243153 Smith, S. (1980). A learning system based on genetic algorithms. (Ph.D. thesis), University of Pittsburgh, Pittsburgh, PA. Smith, S. (1983). Flexible learning of problem solving heuristics through adaptive search. In 8th International Joint Conference on Artificial Intelligence, (pp. 422–425). Tajbakhsh, 2009, Intrusion detection using fuzzy association rules, Applied Soft Computing, 9, 462, 10.1016/j.asoc.2008.06.001 Tsang, 2007, Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection, Pattern Recognition, 40, 2373, 10.1016/j.patcog.2006.12.009 Venturini, 1993, SIA: A supervised inductive algorithm with genetic search for learning attributes based concepts, vol. 667, 280 Victorie, 2012, A local search guided differential evolution algorithm based fuzzy classifier for intrusion detection in computer networks, International Journal of Soft Computing, 6, 158 Wang, 2005, Agent-based evolutionary approach for interpretable rule-based knowledge extraction, IEEE Transactions On Systems, Man, And Cybernetics - Part C: Applications And Reviews, 35, 143, 10.1109/TSMCC.2004.841910 Wu, 2010, The use of computational intelligence in intrusion detection systems: A review, Applied Soft Computing, 10, 1, 10.1016/j.asoc.2009.06.019 Yi, 2011, Incremental SVM based on reserved set for network intrusion detection, Expert Systems with Applications, 38, 7698, 10.1016/j.eswa.2010.12.141 Zhang, 2002, Association rule mining, models and algorithms, vol. 2307 Zhu, 2001, Data mining for network intrusion detection: A comparison of alternative methods, Decision Sciences, 32, 635, 10.1111/j.1540-5915.2001.tb00975.x