Soft Computing

, Volume 21, Issue 9, pp 2307–2324 | Cite as

Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems

  • Hamid Bostani
  • Mansour Sheikhan
Methodologies and Application


Intrusion detection systems (IDSs) play an important role in the security of computer networks. One of the main challenges in IDSs is the high-dimensional input data analysis. Feature selection is a solution to overcoming this problem. This paper presents a hybrid feature selection method using binary gravitational search algorithm (BGSA) and mutual information (MI) for improving the efficiency of standard BGSA as a feature selection algorithm. The proposed method, called MI-BGSA, used BGSA as a wrapper-based feature selection method for performing global search. Moreover, MI approach was integrated into the BGSA, as a filter-based method, to compute the feature–feature and the feature–class mutual information with the aim of pruning the subset of features. This strategy found the features considering the least redundancy to the selected features and also the most relevance to the target class. A two-objective function based on maximizing the detection rate and minimizing the false positive rate was defined as a fitness function to control the search direction of the standard BGSA. The experimental results on the NSL-KDD dataset showed that the proposed method can reduce the feature space dramatically. Moreover, the proposed algorithm found better subset of features and achieved higher accuracy and detection rate as compared to the some standard wrapper-based and filter-based feature selection methods.


Feature selection Intrusion detection system Anomaly detection Binary gravitational search algorithm Mutual information Hybrid model 


Compliance with ethical standards

Conflict of interest

The authors declare that there is no potential conflict of interest in this work.


  1. Amiri F, RezaeiYousefi M, Lucas C, Shakery A, Yazdani N (2011) Mutual information-based feature selection for intrusion detection systems. J Netw Comput Appl 34(4):1184–1199. doi: 10.1016/j.jnca.2011.01.002 CrossRefGoogle Scholar
  2. Battiti R (2002) Using mutual information for selecting features in supervised neural networks learning. IEEE Trans Neural Networ 5(4):537–550. doi: 10.1109/72.298224 CrossRefGoogle Scholar
  3. Bhuse V, Gupta A (2006) Anomaly intrusion detection in wireless sensor networks. J High Speed Netw 15(1):33–51Google Scholar
  4. Blake CL, Merz CJ (1998) UCI repository of machine learning databases. Department of Information and Computer Science, University of California, Irvine. Accessed 21 May 2008
  5. Bonev BI (2010) Feature selection based on information theory. Dissertation, University of AlicanteGoogle Scholar
  6. Cutillo L, Carissimo A, Figini S (2012) Network selection: a method for ranked lists selection. Plos One 7(8):e43678. doi: 10.1371/journal.pone.0043678 CrossRefGoogle Scholar
  7. Dash R, Paramguru RL, Dash R (2011) Comparative analysis of supervised and unsupervised discretization techniques. Int J Adv Sci Technol 2(3):29–37Google Scholar
  8. Deisy C, Baskar S, Ramraj N, Saravanan Koori J, Jeevanandam P (2010) A novel information theoretic-interact algorithm (IT-IN) for feature selection using three machine learning algorithms. Expert Syst Appl 37(12):7589–7597. doi: 10.1016/j.eswa.2010.04.084 CrossRefGoogle Scholar
  9. Enache AC, Patriciu VV (2014) Intrusions detection based on support vector machine optimized with swarm intelligence. In: 9th international symposium on applied computational intelligence and informatics, pp 153–158Google Scholar
  10. Fiore U, Palmieri F, Castiglione A, De Santis A (2013) Network anomaly detection with the restricted Boltzmann machine. Neurocomputing 122:13–23. doi: 10.1016/j.neucom.2012.11.050 CrossRefGoogle Scholar
  11. Hall MA (2000) Correlation-based feature selection for discrete and numeric class machine learning. In: 17th International Conference on Machine Learning, pp 359–366Google Scholar
  12. Hopkins M, Reeber E, Forman G, Suermondt J (1999) Spam dataset- machine learning repository, UCI. Accessed 1 August 2015
  13. Hoque N, Bhattacharyya DK, Kalita JK (2014) MIFS-ND: a mutual information-based feature selection method. Expert Syst Appl 41(14):6371–6385. doi: 10.1016/j.eswa.2014.04.019 CrossRefGoogle Scholar
  14. Jiang S, Wang Y, Ji Z (2014) Convergence analysis and performance of an improved gravitational search algorithm. Appl Soft Comput 24:363–384. doi: 10.1016/j.asoc.2014.07.016 CrossRefGoogle Scholar
  15. Kim G, Lee S, Kim S (2014) A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Syst Appl 41(4):1690–1700. doi: 10.1016/j.eswa.2013.08.066 CrossRefGoogle Scholar
  16. Kira K, Rendell LA (1992) Feature selection problem: Traditional methods and a new algorithm. In: 10th National Conference on artificial intelligence, pp 129–134Google Scholar
  17. Kuang F, Zhang S, Jin Z, Xu W (2015) A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection. Soft Comput 19(5):1187–1199. doi: 10.1007/s00500-014-1332-7 CrossRefGoogle Scholar
  18. Kudłacik P, Porwik P, Wesołowski T (2015) Fuzzy approach for intrusion detection based on user’s commands. Soft Comput. doi: 10.1007/s00500-015-1669-6 Google Scholar
  19. Kumar G, Kumar K (2012) An information theoretic approach for feature selection. Secur Commun Netw 5(2):178–185. doi: 10.1002/sec.303 CrossRefGoogle Scholar
  20. Kwak N, Choi CH (2003) Input feature selection by mutual information based on Parzen window. IEEE Trans Pattern Anal 24(12):1667–1671. doi: 10.1109/TPAMI.2002.1114861 CrossRefGoogle Scholar
  21. Liu H, Setiono R (1995) Chi2: Feature selection and discretization of numeric attributes. In: 7th international conference on tools with artificial intelligence, pp 388–391Google Scholar
  22. Liu H, Sun J, Liu L, Zhang H (2009) Feature selection with dynamic mutual information. Pattern Recogn 42(7):1330–1339. doi: 10.1016/j.patcog.2008.10.028 CrossRefzbMATHGoogle Scholar
  23. Liu H, Wu X, Zhang S (2014) A new supervised feature selection method for pattern classification. Comput Intell 30(2):342–361. doi: 10.1111/j.1467-8640.2012.00465.x MathSciNetCrossRefGoogle Scholar
  24. Migliardi M, Merlo A (2013) Improving energy efficiency in distributed intrusion detection systems. J High Speed Netw 19(3):251–264. doi: 10.3233/JHS-130476 Google Scholar
  25. Nezamabadi-pour H, Rostami-Shahrbabaki M, Maghfoori-Farsangi M (2008) Binary particle swarm optimization: challenges and new solutions. CSI J Comput Sci Eng 6(1-A):21–32Google Scholar
  26. Noto K, Brodley C, Slonim D (2012) FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection. Data Min Knowl Disc 25(1):109–133. doi: 10.1007/s10618-011-0234-x MathSciNetCrossRefGoogle Scholar
  27. Palmieri F, Fiore U (2010) Network anomaly detection through nonlinear analysis. Comput Secur 29(7):737–755. doi: 10.1016/j.cose.2010.05.002 CrossRefGoogle Scholar
  28. Palmieri F, Fiore U, Castiglione A, De Santis A (2013) On the detection of card-sharing traffic through wavelet analysis and support vector machines. Appl Soft Comput 13(1):615–627. doi: 10.1016/j.asoc.2012.08.045 CrossRefGoogle Scholar
  29. Pang S, Ban T, Kadobayashi Y, Kasabov N (2011) Personalized mode transductive spanning SVM classification tree. Inf Sci 181(11):2071–2085. doi: 10.1016/j.ins.2011.01.008 CrossRefGoogle Scholar
  30. Pei M, Goodman ED, Punch WF (1998) Feature extraction using genetic algorithms. In: International symposium on intelligent data engineering and learning, pp 371–384Google Scholar
  31. Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal 27(8):1226–1238. doi: 10.1109/TPAMI.2005.159 CrossRefGoogle Scholar
  32. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248. doi: 10.1016/j.ins.2009.03.004 CrossRefzbMATHGoogle Scholar
  33. Rashedi E, Nezamabadi-pour H, Saryazdi S (2010) BGSA: binary gravitational search algorithm. Nat Comput 9(3):727–745. doi: 10.1007/s11047-009-9175-3 MathSciNetCrossRefzbMATHGoogle Scholar
  34. Robnik-Šikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 53(1–2):23–69. doi: 10.1023/A:1025667309714 CrossRefzbMATHGoogle Scholar
  35. Ruiz R, Riquelme JC, Aguilar-Ruiz JS (2005) Heuristic search over a ranking for feature selection. Lect Notes Comput Sci 3512:742–749. doi: 10.1007/11494669_91
  36. Sheikhan M (2014) Generation of suprasegmental information for speech using a recurrent neural network and binary gravitational search algorithm for feature selection. Appl Intell 40(4):772–790. doi: 10.1007/s10489-013-0505-x
  37. Sheikhan M, Jadidi Z, Farrokhi A (2012) Intrusion detection using reduced-size RNN based on feature grouping. Neural Comput Appl 21(6):1185–1190. doi: 10.1007/s00521-010-0487-0 CrossRefGoogle Scholar
  38. Sheikhan M, Mohammadi N (2012) Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection. Neural Comput Appl 21(8):1961–1970. doi: 10.1007/s00521-011-0599-1 CrossRefGoogle Scholar
  39. Sigillito VG (1989) Ionosphere dataset- machine learning repository, UCI. Accessed 1 August 2015
  40. Stakhanova N, Basu S, Wong J (2010) On the symbiosis of specification-based and anomaly-based detection. Comput Secur 29(2):253–268. doi: 10.1016/j.cose.2009.08.007 CrossRefGoogle Scholar
  41. Tavallaee M, Bagheri E, Wei L Ghorbani A (2009a) NSL-KDD Data Set. Accessed 21 November 2014
  42. Tavallaee M, Bagheri E, Wei L, Ghorbani A (2009b) A detailed analysis of the KDD CUP 99 data set. In: 2nd international symposium on computational intelligence for security and defense applications, pp 53–58Google Scholar
  43. Unler A, Murat A, Chinnam RB (2011) mr\(^{2}\)PSO: a maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification. Inf Sci 181(20):4625–4641. doi: 10.1016/j.ins.2010.05.037 CrossRefGoogle Scholar
  44. 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(9):6225–6232. doi: 10.1016/j.eswa.2010.02.102 CrossRefGoogle Scholar
  45. Wang W, Zhang X, Gombault S, Knapskog SJ (2009) Attribute normalization in network intrusion detection. In: 10th international symposium on pervasive systems, algorithms, and networks, pp 448–453Google Scholar
  46. Wolberg WH (1992) Original Wisconsin Breast Cancer Dataset- Machine Learning Repository, UCI. Accessed 1 August 2015
  47. Wu S, Yen E (2009) Data mining-based intrusion detectors. Expert Syst Appl 36(3):5605–5612. doi: 10.1016/j.eswa.2008.06.138 CrossRefGoogle Scholar
  48. Wu SX, Banzhaf W (2010) The use of computational intelligence in intrusion detection systems: a review. Appl Soft Comput 10(1):1–35. doi: 10.1016/j.asoc.2009.06.019 CrossRefGoogle Scholar
  49. Zhang Z, Hancock ER (2012) Hypergraph based information-theoretic feature selection. Pattern Recogn Lett 33(15):1991–1999. doi: 10.1016/j.patrec.2012.03.021 CrossRefGoogle Scholar
  50. Zhao Z, Liu H (2007) Searching for interacting features. In: 20th international joint conference on artificial intelligence, pp 1156–1161Google Scholar
  51. Zheng Y, Kwoh CK (2011) A feature subset selection method based on high-dimensional mutual information. Entropy 13(4):860–901. doi: 10.3390/e13040860 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Computer EngineeringIslamic Azad UniversityTehranIran
  2. 2.Department of Communication EngineeringIslamic Azad UniversityTehranIran

Personalised recommendations