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Cyber Intrusion Detection using Combined Feature Selection Algorithm

Varuchi Pareek, Anil Kumar

Abstract


Because of the broad dissemination of system availability, the interest for arrange security and insurance against digital assaults is ever expanding. Interruption identification frameworks (IDS) play out a basic job in the present system protection. This paper proposes an Intrusion Detection System dependent on include choice and bunching calculation utilizing channel and wrapper strategies. Channel and wrapper techniques are named highlight gathering dependent on direct relationship coefficient (FGLCC) calculation and cuttlefish calculation (CFA), individually. Choice is utilized as a classifier in the suggested technique. For execution check, the suggested technique has been applied on KDD Cup 99 enormous informational indexes. The outcomes checked a high precision (95%) and discovery rate (97%) with a lower bogus positive rate (1.7%) contrasted with the current strategies for writing.


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References


M. S. Abirami, Umaretiya Yash, Sonal Singh. "Chapter 55 Building an Ensemble Learning Based Algorithm for Improving Intrusion Detection System", Springer Science and Business Media LLC, 2020

Sara Mohammadi, Hamid Mirvaziri, Mostafa Ghazizadeh-Ahsaee, Hadis Karimipour. "Cyber intrusion detection by combined feature selection algorithm", Journal of Information Security and Applications, 2019

Hwang K, Cai M, Chen Y, Qin M. Hybrid intrusion detection with weighted signature generation over anomalous internet episodes. IEEE Trans Dependable Secure Comput 2007;4(1):41–55

Tavallaee M, Stakhanova N, Ghorbani AA. Toward credible evaluation of anomaly-based intrusion-detection methods. IEEE Trans Syst Man Cybern Part C (Appl Rev) 2010;40(5):516–24

Tapiador JE, Orfila A, Ribagorda A, Ramos B. Key-recovery attacks on KIDS, a keyed anomaly detection system. IEEE Trans Dependable Secure Comput 2015;12(3):312–25

Sandra Geris, Hadis Karimipour. "Joint State Estimation and Cyber-Attack Detection Based on Feature Grouping", 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE), 2019

Kabir E, Hu J, Wang H, Zhuo G. A novel statistical technique for intrusion de- tection systems. Future Gener Comput Syst 2018;79:303–18

Eesa AS, Orman Z, Brifcani AMA. A new feature selection model based on ID3 and bees algorithm for intrusion detection system. Turk J Electr Eng Comput Sci 2015;23:615–22

Maggi F, Matteucci M, Zanero S. Detecting intrusions through system call sequence and argument analysis. IEEE Trans Dependable Secure Comput 2010;7(4):381–95

Karimipour H, Dinavahi V. Robust massively parallel dynamic state estimation of power systems against cyber-attack. IEEE Access Dec. 2017;6:2984–95

Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 2005;27(8):1226–38

Mukkamala S, Sung AH. Significant feature selection using computational intel- ligent techniques for intrusion detection. Proc Adv Methods Knowl Discovery Complex Data 2005;5(4):285–306

Amiri F, YousefiMR, Lucas C, Shakery A, Yazdani N. Mutual informa- tion-based feature selection for intrusion detection systems. J Netw Comput Appl 2011;34(4):1184–99

Bolon-Canedo M, Sanchez-Marono N, Alonso-Betanzos A. Feature selection and classification in multiple class datasets: an application to KDD Cup 99 dataset. Expert Syst Appl 2011;38(5):5947–57

Horng S-J, Su M-Y, Chen Y-H, Kao T-W, Chen R-J, Lai J-L, Perkasa CD. A novel intrusion detection system based on hierarchical clustering and support vector machines. Expert Syst Appl 2011;38(1):306–313.

El-Khatib K. Impact of feature reduction on the efficiency of wireless intrusion detection systems. IEEE Trans Parallel Distrib Syst 2010;21(8):1143–9

Chandrashekar G, Sahin F. A survey on feature selection methods. Comput Electr Eng 2014;40(1):16–28

MIT Lincoln Laboratory. 1998 DARPA INTRUSION DETECTION EVALUATION DATASET [Online]. Available from https://www.ll.mit.edu/r-d/datasets/1998-darpa-intrusion-detection-evaluation-dataset

El-Alfy ESM, Alshammari MA. Towards scalable rough set based attribute sub- set selection for intrusion detection using parallel genetic algorithm in MapRe- duce,. Simul Modell Pract Theory 2016;64:18–29

Bostani H, Sheikhan M. Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems. Soft Comput 2017;21(9):2307–24

Ganapathy S, Kulothungan K, Muthurajkumar S, Vijayalakshmi M, Yogesh P, Kannan A. Intelligent feature selection and classification techniques for in- trusion detection in networks: a survey. EURASIP J Wirel Commun Netw 2013;2013(1):271.

Peng Y, Wu Z, Jiang J. A novel feature selection approach for biomedical data classification. J Biomed Inf 2010;43(1):15–23

Huang J, Cai Y, Xu X. A hybrid genetic algorithm for feature selection wrapper based on mutual information. Pattern Recognit Lett 2007;28(13):1825–44

R. Di Pietro and L. V. Mancini (Eds.),“Intrusion detection systems,”vol. 38, Springer Science & Business Media, 2008

Huang G-B, Zhou H, Ding X, Zhang R. Extreme learning machine for regres- sion and multiclass classification. IEEE Trans Syst Man Cybern Part B Apr. 2012;42(2):513–29

Eesa AS, Orman Z, Brifcani AMA. A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems. Expert Syst Appl 2015;42(5):2670–9

Wang J, Wen R, Li J, Yan F, Zhao B, Yu F. Detecting and mitigating tar- get link-flooding attacks using SDN. IEEE Trans Dependable Secure Comput 2018;1(1)

Kang SH, Kim KJ. A feature selection approach to find optimal fea- ture subsets for the network intrusion detection system. Cluster Comput 2016;19(1):325–33

Kuang F, Xu W, Zhuang S. A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl Soft Comput 2014;18:178–84

Mehmod T, Rais HBM. Ant Colony Optimization and Feature Selection for In- trusion Detection. In: Advances in machine learning and signal processing. Springer International Publishing; 2016. p. 305–12

Liu X, Lang B, Xu Y, Cheng B. Feature grouping and local soft match for mobile visual search. Pattern Recognit Lett 2012;33(3):239–46

Ambusaidi MA, He X, Nanda P, Tan Z. Building an intrusion detection sys- tem using a filter-based feature selection algorithm. IEEE Trans Comput 2016;65(10):2986–98

Mazumder S, Sharma T, Mitra R, Sengupta N, Sil J. Generation of sufficient cut points to discretize network traffic data sets. In: International conference on Swarm, Evolutionary, and memetic computing. Berlin, Heidelberg: Springer; 2012. p. 528–39

Ghorbani AA, Lu W, Tavallaee M. Network intrusion detection and prevention: concepts and techniques, vol. 47. Springer Science & Business Media; 2009

Kwak N, Choi CH. Input feature selection for classification problems. IEEE Trans Neural Netw 2002;13(1):143–59

Ravale U, Marathe N, Padiya P. Feature selection based hybrid anomaly intru- sion detection system using K means and RBF kernel function. Procedia Com- put Sci 2015;45:428–35

Song J, Zhu Z, Price C. Feature grouping for intrusion detection based on mu- tual. J Commun 2014;9(12):987–93

Battiti R. Using mutual information for selecting features in supervised neural net learning. IEEE Trans Neural Netw 1999;5(4):537–50.


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