Skip to main content
SpringerLink
Log in

Training a Neural Network for Cyberattack Classification Applications Using Hybridization of an Artificial Bee Colony and Monarch Butterfly Optimization

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Arguably the most recurring issue concerning network security is building an approach that is capable of detecting intrusions into network systems. This issue has been addressed in numerous works using various approaches, of which the most popular one is to consider intrusions as anomalies with respect to the normal traffic in the network and classify network packets as either normal or abnormal. Improving the accuracy and efficiency of this classification is still an open problem to be solved. The study carried out in this article is based on a new approach for intrusion detection that is mainly implemented using the Hybrid Artificial Bee Colony algorithm (ABC) and Monarch Butterfly optimization (MBO). This approach is implemented for preparing an artificial neural system (ANN) in order to increase the precision degree of classification for malicious and non-malicious traffic in systems. The suggestion taken into consideration was to place side-by-side nine other metaheuristic algorithms that are used to evaluate the proposed approach alongside the related works. In the beginning the system is prepared in such a way that it selects the suitable biases and weights utilizing a hybrid (ABC) and (MBO). Subsequently the artificial neural network is retrained by using the information gained from the ideal weights and biases which are obtained from the hybrid algorithm (HAM) to get the intrusion detection approach able to identify new attacks. Three types of intrusion detection evaluation datasets namely KDD Cup 99, ISCX 2012, and UNSW-NB15 were used to compare and evaluate the proposed technique against the other algorithms. The experiment clearly demonstrated that the proposed technique provided significant enhancement compared to the other nine classification algorithms, and that it is more efficient with regards to network intrusion detection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Anderson JP (1980) Computer security threat monitoring and surveillance. Technical report, James P. Anderson Company

  2. Denning DE (1987) An intrusion-detection model. IEEE Trans Software Eng 2:222–232

    Google Scholar 

  3. Ghanem WAH, Belaton B (2013) Improving accuracy of applications fingerprinting on local networks using NMAP-AMAP-ETTERCAP as a hybrid framework. In: 2013 IEEE international conference on control system, computing and engineering. IEEE, pp 403–407

  4. Inayat Z, Gani A, Anuar NB, Anwar S, Khan MK (2017) Cloud-based intrusion detection and response system: open research issues, and solutions. Arab J Sci Eng 42(2):399–423

    Google Scholar 

  5. Narayana GS, Vasumathi D (2018) An attributes similarity-based K-medoids clustering technique in data mining. Arab J Sci Eng 43(8):3979–3992

    Google Scholar 

  6. Fisch D, Hofmann A, Sick B (2010) On the versatility of radial basis function neural networks: a case study in the field of intrusion detection. Inf Sci 180(12):2421–2439

    Google Scholar 

  7. Ding S, Ma G, Shi Z (2014) A rough RBF neural network based on weighted regularized extreme learning machine. Neural Process Lett 40(3):245–260

    Google Scholar 

  8. Hajimirzaei B, Navimipour NJ (2019) Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm. ICT Exp 5(1):56–59

    Google Scholar 

  9. Li H (2016) Research on prediction of traffic flow based on dynamic fuzzy neural networks. Neural Comput Appl 27(7):1969–1980

    Google Scholar 

  10. Alauthaman M, Aslam N, Zhang L, Alasem R, Hossain MA (2018) A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks. Neural Comput Appl 29(11):991–1004

    Google Scholar 

  11. Pillutla H, Arjunan A (2019) Fuzzy self organizing maps-based DDoS mitigation mechanism for software defined networking in cloud computing. J Ambient Intell Humaniz Comput 10(4):1547–1559

    Google Scholar 

  12. Aguayo L, Barreto GA (2018) Novelty detection in time series using self-organizing neural networks: a comprehensive evaluation. Neural Process Lett 47(2):717–744

    Google Scholar 

  13. Pozi MSM, Sulaiman MN, Mustapha N, Perumal T (2016) Improving anomalous rare attack detection rate for intrusion detection system using support vector machine and genetic programming. Neural Process Lett 44(2):279–290

    Google Scholar 

  14. Thaseen IS, Kumar CA, Ahmad A (2019) Integrated intrusion detection model using chi square feature selection and ensemble of classifiers. Arab J Sci Eng 44(4):3357–3368

    Google Scholar 

  15. Catania CA, Bromberg F, Garino CG (2012) An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection. Expert Syst Appl 39(2):1822–1829

    Google Scholar 

  16. Vijayanand R, Devaraj D, Kannapiran B (2018) Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection. Comput Secur 77:304–314

    Google Scholar 

  17. Zou X, Cao J, Guo Q, Wen T (2018) A novel network security algorithm based on improved support vector machine from smart city perspective. Comput Electr Eng 65:67–78

    Google Scholar 

  18. Shams EA, Rizaner A (2018) A novel support vector machine based intrusion detection system for mobile ad hoc networks. Wireless Netw 24(5):1821–1829

    Google Scholar 

  19. Asghari S, Navimipour NJ (2019) Resource discovery in the peer to peer networks using an inverted ant colony optimization algorithm. Peer-to-Peer Netw Appl 12(1):129–142

    Google Scholar 

  20. Kolias C, Kambourakis G, Maragoudakis M (2011) Swarm intelligence in intrusion detection: a survey. Comput Secur 30(8):625–642

    Google Scholar 

  21. Ozturk C, Karaboga D (2011) Hybrid artificial bee colony algorithm for neural network training. In: 2011 IEEE congress of evolutionary computation (CEC). IEEE, pp 84–88

  22. Garro BA, Sossa H, Vázquez RA (2011) Artificial neural network synthesis by means of artificial bee colony (abc) algorithm. In: 2011 IEEE congress of evolutionary computation (CEC). IEEE, pp 331–338

  23. Ojha VK, Abraham A, Snášel V (2017) Metaheuristic design of feedforward neural networks: a review of two decades of research. Eng Appl Artif Intell 60:97–116

    Google Scholar 

  24. Razmjooy N, Sheykhahmad FR, Ghadimi N (2018) A hybrid neural network–world cup optimization algorithm for melanoma detection. Open Med 13(1):9–16

    Google Scholar 

  25. Hagh MT, Ebrahimian H, Ghadimi N (2015) Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG. Front Energy 9(1):75–90

    Google Scholar 

  26. Abedinia O, Amjady N, Ghadimi N (2018) Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm. Comput Intell 34(1):241–260

    Google Scholar 

  27. Abusnaina AA, Abdullah R, Kattan A (2019) Supervised training of spiking neural network by adapting the E-MWO algorithm for pattern classification. Neural Process Lett 49(2):661–682

    Google Scholar 

  28. Karaboga D, Akay B, Ozturk C (2007) Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. In: International conference on modeling decisions for artificial intelligence. Springer, Berlin, pp 318–329

  29. Dang TL, Hoshino Y (2019) Hardware/software co-design for a neural network trained by particle swarm optimization algorithm. Neural Process Lett 49(2):481–505

    Google Scholar 

  30. Meissner M, Schmuker M, Schneider G (2006) Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training. BMC Bioinformatics 7(1):125

    Google Scholar 

  31. Li F (2010) Hybrid neural network intrusion detection system using genetic algorithm. In: 2010 International conference on multimedia technology. IEEE, pp 1–4

  32. Moradi M, Zulkernine M (2004) A neural network based system for intrusion detection and classification of attacks. In: Proceedings of the IEEE international conference on advances in intelligent systems-theory and applications, pp 15–18

  33. Liu C, Niu P, Li G, You X, Ma Y, Zhang W (2017) A hybrid heat rate forecasting model using optimized LSSVM based on improved GSA. Neural Process Lett 45(1):299–318

    Google Scholar 

  34. Ghanem WA, Jantan A (2018) Hybridizing artificial bee colony with monarch butterfly optimization for numerical optimization problems. Neural Comput Appl 30(1):163–181

    Google Scholar 

  35. Ghanem WAH, Jantan A (2018) A novel hybrid artificial bee colony with monarch butterfly optimization for global optimization problems. In: Vasant P, Litvinchev I, Marmolejo-Saucedo J (eds) Modeling, simulation, and optimization. Springer, Cham, pp 27–38

    Google Scholar 

  36. Yu J, Xi L, Wang S (2007) An improved particle swarm optimization for evolving feedforward artificial neural networks. Neural Process Lett 26(3):217–231

    Google Scholar 

  37. Mizuta S, Sato T, Lao D, Ikeda M, Shimizu T (2001) Structure design of neural networks using genetic algorithms. Complex Syst 13(2):161–176

    Google Scholar 

  38. Lam HK, Ling SH, Leung FH, Tam PKS (2001) Tuning of the structure and parameters of neural network using an improved genetic algorithm. In: IECON’01. 27th Annual conference of the IEEE industrial electronics society (Cat. No. 37243), vol 1. IEEE, pp 25–30

  39. Ghanem WAH, Jantan A (2014) Using hybrid artificial bee colony algorithm and particle swarm optimization for training feed-forward neural networks. J Theor Appl Inf Technol 67(3):664–674

    Google Scholar 

  40. Ghanem WAH, Jantan A (2014) Swarm intelligence and neural network for data classification. In: 2014 IEEE international conference on control system, computing and engineering (ICCSCE 2014). IEEE, pp 196–201

  41. Mirjalili S, Hashim SZM, Sardroudi HM (2012) Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl Math Comput 218(22):11125–11137

    Google Scholar 

  42. Ghanem WAH, Jantan A (2018) New approach to improve anomaly detection using a neural network optimized by hybrid ABC and PSO Algorithms. Pak J Stat 34(1):1–14

    Google Scholar 

  43. Mirjalili S, Mirjalili SM, Lewis A (2014) Let a biogeography-based optimizer train your multi-layer perceptron. Inf Sci 269:188–209

    Google Scholar 

  44. Mirjalili S (2015) How effective is the Grey Wolf optimizer in training multi-layer perceptrons. Appl Intell 43(1):150–161

    Google Scholar 

  45. Ghanem WA, Jantan A (2018) A cognitively inspired hybridization of artificial bee colony and dragonfly algorithms for training multi-layer perceptrons. Cognit Comput 10(6):1096–1134

    Google Scholar 

  46. Özgür A, Erdem H (2016) A review of KDD99 dataset usage in intrusion detection and machine learning between 2010 and 2015. PeerJ Preprints 4:e19541

    Google Scholar 

  47. Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE symposium on computational intelligence for security and defense applications. IEEE, pp 1–6

  48. Lee W, Stolfo SJ (2000) A framework for constructing features and models for intrusion detection systems. ACM Trans Inf Syst Secur (TiSSEC) 3(4):227–261

    Google Scholar 

  49. Siddiqui MK, Naahid S (2013) Analysis of KDD CUP 99 dataset using clustering based data mining. Int J Database Theory Appl 6(5):23–34

    Google Scholar 

  50. Zainal A, Maarof MA, Shamsuddin SM (2007) Feature selection using rough-DPSO in anomaly intrusion detection. In: International conference on computational science and its applications. Springer, Berlin, pp 512–524

  51. Alomari O, Othman ZA (2012) Bee’s algorithm for feature selection in network anomaly detection. J Appl Sci Res 8(3):1748–1756

    Google Scholar 

  52. Jebur HH, Maarof MA, Zainal A (2015) Identifying generic features of KDD Cup 1999 for intrusion detection. JurnalTeknologi 74(1):1–9

    Google Scholar 

  53. Othman ZA, Muda Z, Theng LM, Othman MR (2014) Record to record feature selection algorithm for network intrusion detection. Int J Adv Comput Technol 6(2):163

    Google Scholar 

  54. Yassin W, Udzir NI, Muda Z, Sulaiman MN (2013) Anomaly-based intrusion detection through k-means clustering and Naives Bayes classification. In: Proceedings of 4th international conference on computing informatics, ICOCI, vol 49, pp 298–303

  55. Rufai KI, Muniyandi RC, Othman ZA (2014) Improving bee algorithm based feature selection in intrusion detection system using membrane computing. J Netw 9(3):523

    Google Scholar 

  56. Shiravi A, Shiravi H, Tavallaee M, Ghorbani AA (2012) Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput Secur 31(3):357–374

    Google Scholar 

  57. Moustafa N, Slay J (2015) UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 Military communications and information systems conference (MilCIS). IEEE, pp 1–6

  58. Moustafa N, Slay J (2016) The evaluation of network anomaly detection systems: statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set. Inf Secur J Glob Perspect 25(1–3):18–31

    Google Scholar 

  59. Moustafa N, Slay J (2015) The significant features of the UNSW-NB15 and the KDD99 data sets for network intrusion detection systems. In: 2015 4th international workshop on building analysis datasets and gathering experience returns for security (BADGERS). IEEE, pp 25–31

  60. Sindhu SSS, Geetha S, Kannan A (2012) Decision tree based light weight intrusion detection using a wrapper approach. Expert Syst Appl 39(1):129–141

    Google Scholar 

  61. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, vol 200. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department

  62. Rojas I, Cabestany J, Catala A (2015) Advances in artificial neural networks and computational intelligence. Neural Process Lett 42(1):1–3

    Google Scholar 

  63. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Google Scholar 

  64. Wang GG, Deb S, Coelho LDS (2015) Elephant herding optimization. In: 2015 3rd International symposium on computational and business intelligence (ISCBI). IEEE, pp 1–5

  65. Beyer H-G (2013) The theory of evolution strategies. Springer, New York

    Google Scholar 

  66. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Google Scholar 

  67. Wang GG, Deb S, Cui Z (2015) Monarch butterfly optimization. Neural Comput Appl 31:1–20

    Google Scholar 

  68. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Google Scholar 

  69. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  70. Bamakan SMH, Wang H, Shi Y (2017) Ramp loss K-support vector classification-regression; a robust and sparse multi-class approach to the intrusion detection problem. Knowl-Based Syst 126:113–126

    Google Scholar 

  71. Khammassi C, Krichen S (2017) A GA-LR wrapper approach for feature selection in network intrusion detection. Comput Secur 70:255–277

    Google Scholar 

  72. Papamartzivanos D, Mármol FG, Kambourakis G (2018) Dendron: genetic trees driven rule induction for network intrusion detection systems. Future Gener Comput Syst 79:558–574

    Google Scholar 

  73. Kumar G, Kumar K (2015) A multi-objective genetic algorithm based approach for effective intrusion detection using neural networks. In: Yager R, Reformat M, Alajlan N (eds) Intelligent methods for cyber warfare. Springer, Cham, pp 173–200

    Google Scholar 

  74. Hamed T, Dara R, Kremer SC (2018) Network intrusion detection system based on recursive feature addition and bigram technique. Comput Secur 73:137–155

    Google Scholar 

  75. Yassin W, Udzir NI, Muda Z, Sulaiman MN (2013) Anomaly-based intrusion detection through k-means clustering and Naives Bayes classification. In: Proceedings of 4th international conference on computing informatics, ICOCI, vol 49, pp 298–303

Download references

Acknowledgements

This research has been funded by Universiti Sains Malaysia under USM Fellowship [APEX (308/AIPS/415401) (1002/CIPS/ATSG4001)]. And by the RUI Grant, Account No. [1001/PKOMP/8014017] also under the Universiti Sains Malaysia.

Author information

Authors and Affiliations

  1. School of Computer Science, Universiti Sains Malaysia, Penang, Malaysia

    Waheed A. H. M. Ghanem & Aman Jantan

  2. Faculty of Engineering, University of Aden, Aden, Yemen

    Waheed A. H. M. Ghanem

  3. Faculty of Education-Saber, University of Aden, Aden, Yemen

    Waheed A. H. M. Ghanem

Authors
  1. Waheed A. H. M. Ghanem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aman Jantan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Waheed A. H. M. Ghanem.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghanem, W.A.H.M., Jantan, A. Training a Neural Network for Cyberattack Classification Applications Using Hybridization of an Artificial Bee Colony and Monarch Butterfly Optimization. Neural Process Lett 51, 905–946 (2020). https://doi.org/10.1007/s11063-019-10120-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-019-10120-x

Keywords

Search

Navigation