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Intrusion Detection By Data Mining Algorithms: A Review

Year 2013, Volume: 2 Issue: 2, 76 - 91, 01.02.2013

Abstract

– With the increasing use of network-based services and sensitive information on networks, maintaining information security is essential. Intrusion Detection System is a security tool used to detect unauthorized activities of a computer system or network. Data mining is one of the technologies applied to intrusion detection. This article introduces various data mining techniques used to implement an intrusion detection system. Then reviews some of the related studies focusing on data mining algorithms

References

  • M.A. Maloof, Machine Learning and Data Mining for Computer Security, Springer- Verlag, 2006.
  • J.J. Davis and A.J. Clark, Data Preprocessing for Anomaly Based Network Intrusion Detection: A Review, Computers & Security 30 (2011) 353-375.
  • S.V.Shirbhate, V.M.Thakare and S.S.Sherekar, Data Mining Approaches for Network Intrusion Detection System, International Journal of Computer Technology and Electronics Engineering (2011) 41-44.
  • T. Lappas and K. Pelechrinis, Data Mining Techniques for (Network) Intrusion Detection Systems, svn.assembla.com/svn/odinIDS/Egio/artigos/datamining/dataIDS.pdf http://atl
  • E. Biermann, E. Cloete and L.M. Venter, A comparison of Intrusion Detection systems, Computers & Security 20 (2001) 676-683.
  • M. L. Shahreza, D. Moazzami, B. Moshiri and M.R. Delavar, Anomaly Detection Using a Self-Organizing Map and Particle Swarm Optimization, Scientia Iranica 18 (2011) 1460-1468
  • K.K. Bharti, S. Shukla and S. Jain, Intrusion Detection Using Clustering, Proceeding of the Association of Counseling Center Training Agencies (ACCTA), 2010, Volume: 1.
  • S.Y. Wua and E. Yen, Data mining-based intrusion detectors, Expert Systems with Applications 36 (2009) 5605-5612.
  • M. Govindarajan and R.M. Chandrasekaran, Intrusion Detection Using Neural Based Hybrid Classification Methods, Computer Networks 55 (2011) 1662-1671.
  • M. Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms, John Wiley & Sons, 2003.
  • R. Patel, A. Thakkar and A. Ganatra, A Survey and Comparative Analysis of Data Mining Techniques for Network Intrusion Detection Systems, International Journal of Soft Computing and Engineering 2 (2012).
  • G.V. Nadiammai, S. Krishnaveni and M. Hemalatha, A Comprehensive Analysis and Study in Intrusion Detection System using Data Mining Techniques, International Journal of Computer Applications 35 (2011).
  • S.T. BRUGGER, Data Mining Methods for Network Intrusion Detection, University of California, Jun. 2004.
  • C.F. Tsai, Y.F. Hsu, C. Y. Lin and W.Y. Lin, Intrusion Detection by Machine Learning: A Review, Expert Systems with Applications 36 (2009) 11994–12000.
  • C. Modi, D. Patel, B. Borisaniya, H. Patel, A. Patel and M. Rajarajan, A survey of Intrusion Detection Techniques in Cloud, Journal of Network and Computer Applications (2012).
  • V. Jyothsna, V.V. Rama Prasad and K. Munivara Prasad, A Review of Anomaly based Intrusion Detection Systems, International Journal of Computer Applications 28 (2011).
  • A. Jain, S. Sharma and M.S. Sisodia, Network Intrusion Detection by Using Supervised and Unsupervised Machine Learning Techniques: A Survey, International Journal of Computer Technology and Electronics Engineering 1 (2011).
  • S. Zhong, T.M. Khoshgoftaar and N. Seliya, Clustering-based Network Intrusion Detection, International Journal of Reliability, Quality and Safety Engineering 14 (2007) 169-187.
  • F. Ozturk and A. Subasi, Comparison of Decision Tree Methods for Intrusion Detection, Proceeding of the 2nd International Symposium on Sustainable Development, 2010, pp: 401-407.
  • J. Zhang and M. Zulkernine, Network Intrusion Detection using Random Forests, Proceeding of the Third Annual Conference on Privacy, Security and Trust, 2005.
  • C. Kolias, G. Kambourakis and M. Maragoudakis, Swarm Intelligence in Intrusion Detection: A Survey, Computers & security 30 (2011), 625-642.
  • D.Y. Yeung and Y. Ding, Host-based Intrusion Detection Using Dynamic and Static Behavioral Models, Pattern Recognition 36 (2003) 229-243.
  • S.Y. Jiang, X. Song, H. Wang, J.J. Han and Q.H. Li, A Clustering-based Method for Unsupervised Intrusion Detections, Pattern Recognition Letters 27 (2006) 802-810.
  • J.A. Renjit and K.L. Shunmuganathan, Network Based Anomaly Intrusion Detection System Using SVM, Indian Journal of Science and Technology 4 (2011) 1105 -1108.
  • H. Altwaijry and S. Algarny, "Bayesian based intrusion detection system," Journal of King Saud University Computer and Information Sciences 24 (2012) 1-6.
  • C. Kruegel and T. Toth, Using Decision Trees to Improve Signature-Based Intrusion Detection, Springer-Verlag Berlin Heidelberg (2003) 173–191.
  • M. Panda and M.R. Patra, Network Intrusion Detection Using Naïve Bayes, International Journal of Computer Science and Network Security 7 (2007).
  • N. Yasmin, A.S. Nugroho and H. Widiputra, Optimized Sampling with Clustering Approach for Large Intrusion Detection Data, Proceeding of the International Conference on Rural Information and Communication Technology, 2009, pp: 56-60.
  • N. Devarakonda, S. Pamidi, V. Kumari and G. A, Intrusion Detection System using Bayesian Network and Hidden Markov Model, Procedia Technology, 2012, Volume: 4, pp: 506-514.
  • Y. Guan, A. Ghorbani and N. Belacel, Y-means: A Clustering Method for Intrusion Detection, Canadian Conference on Electrical and Computer Engineering, 2003.
  • Y. Liao and V.R. Vemuri, Use of K-Nearest Neighbor classifier for intrusion detection, Computers & Security 21 (2002) 439-448.
  • R. Naik, V. Kshirsagar and B. S. Sonawane, New Strategy for Detecting Intrusion by Using C4.5 Algorithm, Proceedings of the International Conference on Computational Intellegence Applicaitons (ICCIA), 2012.
  • Z. Chen and D. Zhu, Hierarchic Clustering Algorithm used for Anomaly Detecting, Procedia Engineering, 2011, Volume: 15, pp: 3401-3405.
  • L. Hanguang and N. Yu, Intrusion Detection Technology Research Based on Apriori Algorithm, Physics Procedia, 2011, Volume: 24, pp: 1615-1620.
  • W. li and Z. Liu, A method of SVM with Normalization in Intrusion Detection, Procedia Environmental Sciences, 2011, Volume: 11, pp: 256-262.
  • I. Kang, M. K. Jeong and D. Kong, A differentiated One-class Classification Method with Applications to Intrusion Detection, Expert Systems with Applications 39 (2012) 3899-3901.
  • D.M. Farid, N. Harbi and M.Z. Rahman, Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection, International Journal of Network Security & Its Applications 2 (2010).
  • A.P. Muniyandi, R. Rajeswari and R. Rajaram, Network Anomaly Detection by Cascading K-Means Clustering and C4.5 Decision Tree Algorithm, Procedia Engineering, 2012, Volume: 30, pp: 174 – 182.
  • G. Stein, B. Chen, A.S. Wu and K.A. Hua, Decision Tree Classifier for Network Intrusion Detection With GA-based Feature Selection, Proceedings of the 43rd annual Southeast regional conference, 2005, Volume: 2, pp: 136-141.
  • S.A. Mulay and P.R. Devale and G.V. Garje, Intrusion Detection System USING Support Vector Machine and Decision Tree, International Journal of Computer Applications 3 (2010).
  • S.R. Gaddam, V.V. Phoha and K.S. Balagani, K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods, IEEE Transactions on Knowledge and Data Engineering 19 (2007) 345-354.
  • G. Wang, J. Hao, J. Ma and L. Huang, A New Approach to Intrusion Detection Using Artificial Neural Networks and Fuzzy Clustering, Elsevier (2010) 6225-6232.
  • S.S. Sivatha Sindhu, S. Geetha and A. Kannan, Decision Tree Based Light Weight Intrusion Detection Using a Wrapper Approach, Expert Systems with Applications 39 (2012) 129-141.
  • S.J. Horng , M.Y. Su, Y.H. Chen, T.W. Kao, R.J. Chen, J.L. Lai and C.D. Perkasa, A Novel Intrusion Detection System Based on Hierarchical Clustering and Support Vector Machines, Expert Systems with Applications 38 (2011) 306-313.
Year 2013, Volume: 2 Issue: 2, 76 - 91, 01.02.2013

Abstract

References

  • M.A. Maloof, Machine Learning and Data Mining for Computer Security, Springer- Verlag, 2006.
  • J.J. Davis and A.J. Clark, Data Preprocessing for Anomaly Based Network Intrusion Detection: A Review, Computers & Security 30 (2011) 353-375.
  • S.V.Shirbhate, V.M.Thakare and S.S.Sherekar, Data Mining Approaches for Network Intrusion Detection System, International Journal of Computer Technology and Electronics Engineering (2011) 41-44.
  • T. Lappas and K. Pelechrinis, Data Mining Techniques for (Network) Intrusion Detection Systems, svn.assembla.com/svn/odinIDS/Egio/artigos/datamining/dataIDS.pdf http://atl
  • E. Biermann, E. Cloete and L.M. Venter, A comparison of Intrusion Detection systems, Computers & Security 20 (2001) 676-683.
  • M. L. Shahreza, D. Moazzami, B. Moshiri and M.R. Delavar, Anomaly Detection Using a Self-Organizing Map and Particle Swarm Optimization, Scientia Iranica 18 (2011) 1460-1468
  • K.K. Bharti, S. Shukla and S. Jain, Intrusion Detection Using Clustering, Proceeding of the Association of Counseling Center Training Agencies (ACCTA), 2010, Volume: 1.
  • S.Y. Wua and E. Yen, Data mining-based intrusion detectors, Expert Systems with Applications 36 (2009) 5605-5612.
  • M. Govindarajan and R.M. Chandrasekaran, Intrusion Detection Using Neural Based Hybrid Classification Methods, Computer Networks 55 (2011) 1662-1671.
  • M. Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms, John Wiley & Sons, 2003.
  • R. Patel, A. Thakkar and A. Ganatra, A Survey and Comparative Analysis of Data Mining Techniques for Network Intrusion Detection Systems, International Journal of Soft Computing and Engineering 2 (2012).
  • G.V. Nadiammai, S. Krishnaveni and M. Hemalatha, A Comprehensive Analysis and Study in Intrusion Detection System using Data Mining Techniques, International Journal of Computer Applications 35 (2011).
  • S.T. BRUGGER, Data Mining Methods for Network Intrusion Detection, University of California, Jun. 2004.
  • C.F. Tsai, Y.F. Hsu, C. Y. Lin and W.Y. Lin, Intrusion Detection by Machine Learning: A Review, Expert Systems with Applications 36 (2009) 11994–12000.
  • C. Modi, D. Patel, B. Borisaniya, H. Patel, A. Patel and M. Rajarajan, A survey of Intrusion Detection Techniques in Cloud, Journal of Network and Computer Applications (2012).
  • V. Jyothsna, V.V. Rama Prasad and K. Munivara Prasad, A Review of Anomaly based Intrusion Detection Systems, International Journal of Computer Applications 28 (2011).
  • A. Jain, S. Sharma and M.S. Sisodia, Network Intrusion Detection by Using Supervised and Unsupervised Machine Learning Techniques: A Survey, International Journal of Computer Technology and Electronics Engineering 1 (2011).
  • S. Zhong, T.M. Khoshgoftaar and N. Seliya, Clustering-based Network Intrusion Detection, International Journal of Reliability, Quality and Safety Engineering 14 (2007) 169-187.
  • F. Ozturk and A. Subasi, Comparison of Decision Tree Methods for Intrusion Detection, Proceeding of the 2nd International Symposium on Sustainable Development, 2010, pp: 401-407.
  • J. Zhang and M. Zulkernine, Network Intrusion Detection using Random Forests, Proceeding of the Third Annual Conference on Privacy, Security and Trust, 2005.
  • C. Kolias, G. Kambourakis and M. Maragoudakis, Swarm Intelligence in Intrusion Detection: A Survey, Computers & security 30 (2011), 625-642.
  • D.Y. Yeung and Y. Ding, Host-based Intrusion Detection Using Dynamic and Static Behavioral Models, Pattern Recognition 36 (2003) 229-243.
  • S.Y. Jiang, X. Song, H. Wang, J.J. Han and Q.H. Li, A Clustering-based Method for Unsupervised Intrusion Detections, Pattern Recognition Letters 27 (2006) 802-810.
  • J.A. Renjit and K.L. Shunmuganathan, Network Based Anomaly Intrusion Detection System Using SVM, Indian Journal of Science and Technology 4 (2011) 1105 -1108.
  • H. Altwaijry and S. Algarny, "Bayesian based intrusion detection system," Journal of King Saud University Computer and Information Sciences 24 (2012) 1-6.
  • C. Kruegel and T. Toth, Using Decision Trees to Improve Signature-Based Intrusion Detection, Springer-Verlag Berlin Heidelberg (2003) 173–191.
  • M. Panda and M.R. Patra, Network Intrusion Detection Using Naïve Bayes, International Journal of Computer Science and Network Security 7 (2007).
  • N. Yasmin, A.S. Nugroho and H. Widiputra, Optimized Sampling with Clustering Approach for Large Intrusion Detection Data, Proceeding of the International Conference on Rural Information and Communication Technology, 2009, pp: 56-60.
  • N. Devarakonda, S. Pamidi, V. Kumari and G. A, Intrusion Detection System using Bayesian Network and Hidden Markov Model, Procedia Technology, 2012, Volume: 4, pp: 506-514.
  • Y. Guan, A. Ghorbani and N. Belacel, Y-means: A Clustering Method for Intrusion Detection, Canadian Conference on Electrical and Computer Engineering, 2003.
  • Y. Liao and V.R. Vemuri, Use of K-Nearest Neighbor classifier for intrusion detection, Computers & Security 21 (2002) 439-448.
  • R. Naik, V. Kshirsagar and B. S. Sonawane, New Strategy for Detecting Intrusion by Using C4.5 Algorithm, Proceedings of the International Conference on Computational Intellegence Applicaitons (ICCIA), 2012.
  • Z. Chen and D. Zhu, Hierarchic Clustering Algorithm used for Anomaly Detecting, Procedia Engineering, 2011, Volume: 15, pp: 3401-3405.
  • L. Hanguang and N. Yu, Intrusion Detection Technology Research Based on Apriori Algorithm, Physics Procedia, 2011, Volume: 24, pp: 1615-1620.
  • W. li and Z. Liu, A method of SVM with Normalization in Intrusion Detection, Procedia Environmental Sciences, 2011, Volume: 11, pp: 256-262.
  • I. Kang, M. K. Jeong and D. Kong, A differentiated One-class Classification Method with Applications to Intrusion Detection, Expert Systems with Applications 39 (2012) 3899-3901.
  • D.M. Farid, N. Harbi and M.Z. Rahman, Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection, International Journal of Network Security & Its Applications 2 (2010).
  • A.P. Muniyandi, R. Rajeswari and R. Rajaram, Network Anomaly Detection by Cascading K-Means Clustering and C4.5 Decision Tree Algorithm, Procedia Engineering, 2012, Volume: 30, pp: 174 – 182.
  • G. Stein, B. Chen, A.S. Wu and K.A. Hua, Decision Tree Classifier for Network Intrusion Detection With GA-based Feature Selection, Proceedings of the 43rd annual Southeast regional conference, 2005, Volume: 2, pp: 136-141.
  • S.A. Mulay and P.R. Devale and G.V. Garje, Intrusion Detection System USING Support Vector Machine and Decision Tree, International Journal of Computer Applications 3 (2010).
  • S.R. Gaddam, V.V. Phoha and K.S. Balagani, K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods, IEEE Transactions on Knowledge and Data Engineering 19 (2007) 345-354.
  • G. Wang, J. Hao, J. Ma and L. Huang, A New Approach to Intrusion Detection Using Artificial Neural Networks and Fuzzy Clustering, Elsevier (2010) 6225-6232.
  • S.S. Sivatha Sindhu, S. Geetha and A. Kannan, Decision Tree Based Light Weight Intrusion Detection Using a Wrapper Approach, Expert Systems with Applications 39 (2012) 129-141.
  • S.J. Horng , M.Y. Su, Y.H. Chen, T.W. Kao, R.J. Chen, J.L. Lai and C.D. Perkasa, A Novel Intrusion Detection System Based on Hierarchical Clustering and Support Vector Machines, Expert Systems with Applications 38 (2011) 306-313.
There are 44 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Marjan Kuchaki Rafsanjani This is me

Zahra Asghari Varzaneha This is me

Publication Date February 1, 2013
Published in Issue Year 2013 Volume: 2 Issue: 2

Cite

APA Rafsanjani, M. K., & Varzaneha, Z. A. (2013). Intrusion Detection By Data Mining Algorithms: A Review. Journal of New Results in Science, 2(2), 76-91.
AMA Rafsanjani MK, Varzaneha ZA. Intrusion Detection By Data Mining Algorithms: A Review. JNRS. February 2013;2(2):76-91.
Chicago Rafsanjani, Marjan Kuchaki, and Zahra Asghari Varzaneha. “Intrusion Detection By Data Mining Algorithms: A Review”. Journal of New Results in Science 2, no. 2 (February 2013): 76-91.
EndNote Rafsanjani MK, Varzaneha ZA (February 1, 2013) Intrusion Detection By Data Mining Algorithms: A Review. Journal of New Results in Science 2 2 76–91.
IEEE M. K. Rafsanjani and Z. A. Varzaneha, “Intrusion Detection By Data Mining Algorithms: A Review”, JNRS, vol. 2, no. 2, pp. 76–91, 2013.
ISNAD Rafsanjani, Marjan Kuchaki - Varzaneha, Zahra Asghari. “Intrusion Detection By Data Mining Algorithms: A Review”. Journal of New Results in Science 2/2 (February 2013), 76-91.
JAMA Rafsanjani MK, Varzaneha ZA. Intrusion Detection By Data Mining Algorithms: A Review. JNRS. 2013;2:76–91.
MLA Rafsanjani, Marjan Kuchaki and Zahra Asghari Varzaneha. “Intrusion Detection By Data Mining Algorithms: A Review”. Journal of New Results in Science, vol. 2, no. 2, 2013, pp. 76-91.
Vancouver Rafsanjani MK, Varzaneha ZA. Intrusion Detection By Data Mining Algorithms: A Review. JNRS. 2013;2(2):76-91.


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