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A new intelligent intrusion detector based on ensemble of decision trees

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Abstract

Artificial intelligence and machine learning are in widespread use nowadays in order to develop automatic and precise models for different tasks especially in the Internet. In this paper, by the use of machine learning techniques, an intrusion detection system is proposed. An intrusion detection system is involved extensive mass of data; such data is naturally characterized with repetitions and noise which leads to the reduction in the stability and the accuracy of the intrusion detection system. Hence, the issue of reducing features dimensions for achieving a smaller subset of features which can precisely express the results and status of network observations has attracted a lot of researchers’ attention. In the proposed method, by using gradually feature removal method, 16 critical features were selected for representing various network visits. By combining ant colony algorithm and ensemble of decision trees, we proposed an efficient and stable classifier for judging a network visit to be normal or not. Despite the selection of 16 features, high accuracy, i.e. 99.92%, and the average value of Matthews correlation coefficient 0.91 are obtained.

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References

  • Aggarwal P, Sharma SK (2015) Analysis of KDD dataset attributes—class wise for intrusion detection. Procedia Comput Sci 57:842–851

    Google Scholar 

  • Alamiedy TA, Anbar M, Alqattan ZNM, Alzubi QM (2019) Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm. J Ambient Intell Humaniz Comput 2019:1–22

    Google Scholar 

  • Aloqaily M, Otoum S, Ridhawi I, Jararweh Y (2019) An intrusion detection system for connected vehicles in smart cities. Ad Hoc Netw 90:101842

    Google Scholar 

  • Alrawashdeh K, Purdy C (2016) Toward an online anomaly intrusion detection system based on deep learning. In: 15th IEEE international conference on machine learning and applications (ICMLA), IEEE, Anaheim, CA, USA, pp 195–200. https://doi.org/10.1109/ICMLA.2016.0040

  • Al-Yaseen WL, Othman ZA, Nazri MZA (2017) Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system. Expert Syst Appl 67:296–303

    Google Scholar 

  • Amor N, Benferhat S, Elouedi Z (2004) Naive bayes vs decision trees in intrusion detection systems. In: Proceedings of the 2004 ACM symposium on applied computing, ACM, Nicosia, Cyprus, pp 420–424. https://doi.org/10.1145/967900.967989

  • Aslam JA, Popa RA, Rivest RL (2007) On estimating the size and confidence of a statistical audit. In: Proceedings of the USENIX workshop on accurate electronic voting technology, USENIX Association, Boston, MA, p 8–8

  • Besharati E, Naderan M, Namjoo E (2019) LR-HIDS: logistic regression host-based intrusion detection system for cloud environments. J Ambient Intell Humaniz Comput 10(9):3669–3692

    Google Scholar 

  • Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Chapman and Hall, Wadsworth

    MATH  Google Scholar 

  • Chebrolu S, Abraham A, Thomas JP (2005) Feature deduction and ensemble design of intrusion detection systems. Comput Secur 24:295–307

    Google Scholar 

  • Chimphlee W, Abdullah AH, Sap MNM, Srinoy S, Chimphlee S (2006) Anomaly-based intrusion detection using fuzzy rough clustering. In: International conference on hybrid information technology, IEEE, Cheju Island, South Korea, pp 329–334. https://doi.org/10.1109/ICHIT.2006.253508

  • Dada EG (2017) A hybridized SVM-kNN-pdAPSO approach to intrusion detection system. Fac Semin Ser Univ Maid 8:48–54

    Google Scholar 

  • Denning DE (1987) An intrusion-detection model. IEEE Trans Softw Eng 13:222–232

    Google Scholar 

  • Depren O, Topallar M, Anarim E, Ciliz MK (2005) An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks. Expert Syst Appl 29:713–722

    Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1991) The ant system: An autocatalytic optimizing process. Technical Report No. 91-016, Politecnico di Milano, Italy, pp 1–21

  • Elhag S, Fernández A, Altalhi A, AlshomraniS Herrera F (2019) A multi-objective evolutionary fuzzy system to obtain a broad and accurate set of solutions in intrusion detection systems. Soft Comput 23:1321

    Google Scholar 

  • Fleuret F (2004) Fast binary feature selection with conditional mutual information. J Mach Learn Res 5:1531–1555

    MathSciNet  MATH  Google Scholar 

  • Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119–139

    MathSciNet  MATH  Google Scholar 

  • Hoque MS, Mukit MA, Bikas MAN, Sazzadul Hoque M (2012) An implementation of intrusion detection system using genetic algorithm. Int J Netw Secur Appl 4:109–120

    Google Scholar 

  • Hoque N, Bhuyan MH, Baishya RC, Bhattacharyya DK, Kalita JK (2014) Network attacks: taxonomy, tools and systems. J Netw Comput Appl 40:307–324

    Google Scholar 

  • Horng SJ, Su M-Y, Chen YH, Kao TK, Chen RJ, Lai JL, Perkasa CD (2011) A novel intrusion detection system based on hierarchical clustering and support vector machines. Expert Syst Appl 38:306–313

    Google Scholar 

  • Jo S, Sung H, Ahn B (2015) A comparative study on the performance of intrusion detection using decision tree and artificial neural network models. J Korea Soc Digit Ind Inf Manag 11:33–45

    Google Scholar 

  • Joseph JFC, Das A, Lee B-S, Seet B-C (2010) CARRADS: cross layer based adaptive real-time routing attack detection system for MANETS. Comput Netw 54:1126–1141

    MATH  Google Scholar 

  • Karimi Z, Harounabadi A (2013) Feature ranking in intrusion detection dataset using combination of filtering methods. Int J Comput Appl 78:21–27

    Google Scholar 

  • Kearns M (1988) Thoughts on hypothesis boosting. Unpublished manuscript, Machine Learning class project 45:105

  • Khan L, Awad M, Thuraisingham B (2007) A new intrusion detection system using support vector machines and hierarchical clustering. VLDB J 16:507–521

    Google Scholar 

  • Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97:273–324

    MATH  Google Scholar 

  • Langin C, Rahimi S (2010) Soft computing in intrusion detection: the state of the art. J Ambient Intell Humaniz Comput 1(2):133–145

    Google Scholar 

  • Li Y, Guo L (2007) An active learning based TCM-KNN algorithm for supervised network intrusion detection. Comput Secur 26:459–467

    Google Scholar 

  • Li Y, Xia J, Zhang S, Yan J, Ai X, Dai K (2012) An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Syst Appl 39:424–430

    Google Scholar 

  • Lytras M, Raghavan V, Damiani E (2017) Big data and data analytics research: from metaphores to value space for collective wisdom in human decision making and smart machines. Int J Semant Web Inf Syst 13(1):1–10

    Google Scholar 

  • Lytras M, Visvizi A, Sarirete A (2019) Clustering smart city services: perceptions, expectations responses. Sustainability 11(6):1–19

    Google Scholar 

  • Mukherjee S, Sharma N (2012) Intrusion detection using Naïve Bayes classifier with feature reduction. Procedia Technol 4:119–128

    Google Scholar 

  • Mukkamala S, Sung AH (2003) Feature selection for intrusion detection with neural networks and support vector machines. Transp Res Rec 1822(1):33–39

    Google Scholar 

  • Mukkamala S, Sung AH, Abraham A (2004) Modeling intrusion detection systems using linear genetic programming approach. Innov Appl Artif Intell 1:633–642

    Google Scholar 

  • Onik AR, Samad T (2017) A network intrusion detection framework based on Bayesian network using wrapper approach. Int J Comput Appl 166:13–17

    Google Scholar 

  • Park ST, Li G, Hong JC (2018) A study on smart factory-based ambient intelligence context-aware intrusion detection system using machine learning. J Ambient Intell Humaniz Comput 2018:1–8

    Google Scholar 

  • Revathi S, Malathi A (2013) A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. Int J Eng Res 2:1848–1854

    Google Scholar 

  • Sangkatsanee P, Wattanapongsakorn N, Charnsripinyo C (2011) Practical real-time intrusion detection using machine learning approaches. Comput Commun 34:2227–2235

    Google Scholar 

  • Selvakumar K, Karuppiah M, SaiRamesh L, Islam SKH, Hasan MM, Fortino G, Choo KKR (2019) Intelligent temporal classification and fuzzy rough set-based feature selection algorithm for intrusion detection system in WSNs. Inf Sci 497:77–90

    Google Scholar 

  • Shafi K, Abbass HA (2009) An adaptive genetic-based signature learning system for intrusion detection. Expert Syst Appl 36:12036–12043

    Google Scholar 

  • Sharifi AM, Amirgholipour SK, Pourebrahimi A (2015) Intrusion detection based on joint of K-means and KNN. J Converg Inf Technol 10:42–51

    Google Scholar 

  • Sheltami T, Basabaa A, Shakshuki E (2014) A3ACKs: adaptive three acknowledgments intrusion detection system for MANETs. J Ambient Intell Humaniz Comput 5(4):611–620

    Google Scholar 

  • Sruit M, Lytras M (2018) Applied data science in patient-centric healthcare. Telemat Inf 35(4):643–653

    Google Scholar 

  • Tsai CF, Lin CY (2010) A triangle area based nearest neighbors approach to intrusion detection. Pattern Recogn 43:222–229

    MATH  Google Scholar 

  • Tsang CH, Kwong S, Wang H (2007) Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Pattern Recogn 40:2373–2391

    MATH  Google Scholar 

  • Vapnik VN (1998) Statistical learning theory. Adapt Learn Syst Signal Process Commun Control 2:1–740

    MATH  Google Scholar 

  • Wang W, Battiti R (2006) Identifying intrusions in computer networks with principal component analysis. In: Proceedings of first international conference on availability, reliability and security. ARES06, IEEE, Vienna, Austria, pp 271–279. https://doi.org/10.1109/ARES.2006.73

  • Wang G, Hao J, Ma J, Huang L (2010) A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Expert Syst Appl 37:6225–6232

    Google Scholar 

  • Xiang C, Yong PC, Meng LS (2008) Design of multiple-level hybrid classifier for intrusion detection system using Bayesian clustering and decision trees. Pattern Recogn Lett 29:918–924

    Google Scholar 

  • Xiao L, Liu Y (2009) A two step feature selection algorithm adapting to intrusion detection. In: International joint conference on artificial intelligence, IEEE, Hainan Island, China, pp 618–622. https://doi.org/10.1109/JCAI.2009.214

  • Zhao G, Zhang C, Zheng L (2017) Intrusion detection using deep belief network and probabilistic neural network. In: IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC), IEEE, Guangzhou, China, pp 639–642. https://doi.org/10.1109/CSE-EUC.2017.119

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Acknowledgements

Thank you for all people who helped us with this research and its hard way.

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Authors and Affiliations

  1. Department of Computer Engineering, University College of Nabi Akram, Tabriz, Iran

    Seyed Morteza Mousavi & Avaz Naghipour

  2. Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran

    Vahid Majidnezhad

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  1. Seyed Morteza Mousavi
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  2. Vahid Majidnezhad
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  3. Avaz Naghipour
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Correspondence to Vahid Majidnezhad.

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Mousavi, S.M., Majidnezhad, V. & Naghipour, A. A new intelligent intrusion detector based on ensemble of decision trees. J Ambient Intell Human Comput 13, 3347–3359 (2022). https://doi.org/10.1007/s12652-019-01596-5

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