Evolutionary Intelligence

, Volume 12, Issue 2, pp 131–146 | Cite as

A new evolutionary neural networks based on intrusion detection systems using locust swarm optimization

  • Ilyas BenmessahelEmail author
  • Kun Xie
  • Mouna Chellal
  • Thabo Semong
Research Paper


The need to avoid computer system breaches is increasing. Many researchers have adopted different approaches, such as intrusion detection systems (IDSs), to handle various threats. Intrusion detection has become an imperative system to detect various security breaches. Until today, researchers face the problem of building reliable and effective IDSs that can handle numerous attacks with changing patterns. This paper deals with feed-forward neural network (FNN) training problems using the application of a recently invented meta-heuristic optimization algorithm locust swarm optimization (LSO) for the first time. FNN is combined with LSO (FNN-LSO) to build an advanced detection system and improve the performance of IDS. Our method is applied to a series of experiments to study the capability and performance of the proposed approach. Experimental studies began by using intrusion detection evaluation data, namely, NSL-KDD and UNSW-NB15, to benchmark the performance of the proposed approach. The most common evolutionary trainers, namely, particle swarm optimizer PSO-based trainer and genetic algorithm GA-based trainer, were implemented to verify the results. Compared with existing methods in the literature, our proposed approach provides to be more accurate to be an alternative solution for IDS. The experimental results show that our training algorithm not only attained a very good performance in terms of speed convergence but also achieved reliability due to the reduced likelihood of being trapped in local minima. Furthermore, our proposed model improves the detection rate.


Multilayer feed forward (MLFF) Training neural network Evolutionary algorithm (EA) Locust swarm optimization (LSO) Intrusion detection systems (IDSs) 



  1. 1.
    Xie G, Xie K, Huang J, Wang X, Chen Y, Wen J (2017) Fast low-rank matrix approximation with locality sensitive hashing for quick anomaly detection. In: 2017 IEEE Conference on computer communications (INFOCOM)Google Scholar
  2. 2.
    Bamakan SMH, Amiri B, Mirzabagheri M, Shi Y (2015) A new intrusion detection approach using pso based multiple criteria linear programming. Procedia Comput Sci 55:231–237CrossRefGoogle Scholar
  3. 3.
    Demertzis K, Iliadis L (2014) A hybrid network anomaly and intrusion detection approach based on evolving spiking neural network classification. Springer International Publishing, Cham, pp 11–23Google Scholar
  4. 4.
    Dash T (2017) A study on intrusion detection using neural networks trained with evolutionary algorithms. Soft Comput 21(10):2687–2700CrossRefGoogle Scholar
  5. 5.
    Tang A, Sethumadhavan S, Stolfo SJ (2014) Unsupervised anomaly-based malware detection using hardware features. Springer International Publishing, Cham, pp 109–129Google Scholar
  6. 6.
    Ghorbani AA, Lu W, Tavallaee M (2010) Detection approaches. Springer, Boston, pp 27–53Google Scholar
  7. 7.
    Rastegari S (2015) Intelligent network intrusion detection using an evolutionary computation approachGoogle Scholar
  8. 8.
    Tian WJ, Liu JC (2010) Network intrusion detection analysis with neural network and particle swarm optimization algorithm. In: 2010 Chinese control and decision conference, pp 1749–1752Google Scholar
  9. 9.
    Lotfi Shahreza M, Moazzami D, Moshiri B, Delavar MR (2011) Anomaly detection using a self-organizing map and particle swarm optimization. Sci Iran 18(6):1460–1468CrossRefGoogle Scholar
  10. 10.
    Gomathy A, Lakshmipathi B (2011) Network intrusion detection using genetic algorithm and neural network. Springer, Berlin Heidelberg, Berlin, pp 399–408Google Scholar
  11. 11.
    Pal B, Hasan MAM (2012) Neural network amp; genetic algorithm based approach to network intrusion detection amp; comparative analysis of performance. In: 2012 15th international conference on computer and information technology (ICCIT), pp 150–154Google Scholar
  12. 12.
    Ozturk C, Karaboga D (2011) Hybrid artificial bee colony algorithm for neural network training. In: 2011 IEEE congress of evolutionary computation (CEC), pp 84–88Google Scholar
  13. 13.
    Hassim YMM, Ghazali R (2014) Optimizing functional link neural network learning using modified bee colony on multi-class classifications. Springer, Berlin, pp 153–159Google Scholar
  14. 14.
    Chattopadhyay M (2015) Modelling of intrusion detection system using artificial intelligence—evaluation of performance measures. Springer International Publishing, Cham, pp 311–336Google Scholar
  15. 15.
    Akkar HA, Mahdi FR (2016) Evolutionary algorithms for neural networks binary and real data classification. Int J Sci Technol Res 5(7):55–60Google Scholar
  16. 16.
    Abdalla OA, Elfaki AO, Almurtadha YM (2014) Optimizing the multilayer feed-forward artificial neural networks architecture and training parameters using genetic algorithm. Int J Comput Appl 96(10):42–48 Full text availableGoogle Scholar
  17. 17.
    Garro BA, Vázquez RA (2015) Designing artificial neural networks using particle swarm optimization algorithms. Intell Neurosci 2015:61Google Scholar
  18. 18.
    Faris H, Aljarah I, Mirjalili S (2016) Training feedforward neural networks using multi-verse optimizer for binary classification problems. Appl Intell 45(2):322–332CrossRefGoogle Scholar
  19. 19.
    Bui NT, Hasegawa H (2015) Training artificial neural network using modification of differential evolution algorithm. Int J Mach Learn Comput 5(1):1–6CrossRefGoogle Scholar
  20. 20.
    Ding S, Li H, Chunyang S, Junzhao Y, Jin F (2013) Evolutionary artificial neural networks: a review. Artif Intell Rev 39(3):251–260CrossRefGoogle Scholar
  21. 21.
    Cuevas E, González A, Zaldívar D, Pérez-Cisneros M (2015) Multithreshold segmentation by using an algorithm based on the behavior of locust swarms. Math Probl Eng 2015(805357):25Google Scholar
  22. 22.
    González A, Cuevas E, Fausto F, Valdivia A, Rojas R (2017) A template matching approach based on the behavior of swarms of locust. Appl Intell 47(4):1087–1098CrossRefGoogle Scholar
  23. 23.
    Lee W, Stolfo SJ, Mok KW (2000) Adaptive intrusion detection: a data mining approach. Artif Intell Rev 14(6):533–567CrossRefzbMATHGoogle Scholar
  24. 24.
    Ahmad I, Abdullah AB, Alghamdi AS (2009) Application of artificial neural network in detection of probing attacks. In: 2009 IEEE symposium on industrial electronics applications, vol 2, pp 557–562Google Scholar
  25. 25.
    Li J, Zhang G-Y, Gu G-C (2004) The research and implementation of intelligent intrusion detection system based on artificial neural network. In: Proceedings of 2004 international conference on machine learning and cybernetics (IEEE Cat. No.04EX826), vol 5, pp 3178–3182Google Scholar
  26. 26.
    Berlin H, Djionang L, Tindo G (2017) A new networks intrusion detection architecture based on neural networks. Glob J Comput Sci Technol Netw Web Secur 17(1):19–27Google Scholar
  27. 27.
    Lu C, Zhai L, Liu T, Li N (2016) Network intrusion detection based on neural networks and D-S evidence. Springer International Publishing, Cham, pp 332–343Google Scholar
  28. 28.
    Gonzalez F, Gomez J, Kaniganti M, Dasgupta D (2003) An evolutionary approach to generate fuzzy anomaly (attack) signatures. In: IEEE systems, man and cybernetics society information assurance workshop, 2003, pp 251–259Google Scholar
  29. 29.
    Qinzhen X, Yang L, Zhao Q, He Z (2006) A novel intrusion detection mode based on understandable neural network trees. J Electrons (China) 23(4):574–579CrossRefGoogle Scholar
  30. 30.
    Ke G, Hong YH (2014) The research of network intrusion detection technology based on genetic algorithm and bp neural network. In: Frontiers of manufacturing science and measuring technology IV, vol 599 of applied mechanics and materials. Trans Tech Publications, pp 726–730Google Scholar
  31. 31.
    Han S-J, Cho S-B (2005) Evolutionary neural networks for anomaly detection based on the behavior of a program. IEEE Trans Syst Man Cybern Part B (Cybernetics) 36(3):559–570MathSciNetCrossRefGoogle Scholar
  32. 32.
    Michailidis E, Katsikas SK, Georgopoulos E (2008) Intrusion detection using evolutionary neural networks. In: 2008 Panhellenic conference on informatics, pp 8–12Google Scholar
  33. 33.
    Qiu C, Shan J (2015) Research on intrusion detection algorithm based on bp neural network. Int J Secur Appl 9(6):247–259Google Scholar
  34. 34.
    Maarouf M, Sosa A, Galván B, Greiner D, Winter G, Mendez M, Aguasca R (2015) The role of artificial neural networks in evolutionary optimisation: a review. Springer International Publishing, Cham, pp 59–76Google Scholar
  35. 35.
    Zhu A-X (2017) Artificial neural networks. In: International encyclopedia of geography. American Cancer Society, Atlanta, Georgia, US, pp 1–6Google Scholar
  36. 36.
    Chen J-F, Do QH, Hsieh H-N (2015) Training artificial neural networks by a hybrid pso-cs algorithm. Algorithms 8(2):292–308MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    Chen S (2009) An analysis of locust swarms on large scale global optimization problems. Springer, Berlin, pp 211–220Google Scholar
  38. 38.
    Cuevas E, González A, Zaldívar D, Pérez-Cisneros M (2015) An optimisation algorithm based on the behaviour of locust swarms. Int J Bioinspired Comput 7(6):402–407CrossRefGoogle Scholar
  39. 39.
    Topaz CM, Bernoff AJ, Logan S, Toolson W (2008) A model for rolling swarms of locusts. Eur Phys J Spec Top 157:93–109CrossRefGoogle Scholar
  40. 40.
    Cortes C, Gonzalvo X, Kuznetsov V, Mohri M, Yang S (2016) Adanet: adaptive structural learning of artificial neural networks. CoRR. arXiv:abs/1607.01097
  41. 41.
    The Cyber Range Lab of the Australian Centre for Cyber Security (ACCS). Unsw-nb15 dataset. Accessed May 2015
  42. 42.
    Moustafa N, Slay J (Nov 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), pp 1–6Google Scholar
  43. 43.
    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 A Glob Perspect 25(1–3):18–31CrossRefGoogle Scholar
  44. 44.
    Khammassi C, Krichen S (2017) A ga-lr wrapper approach for feature selection in network intrusion detection. Comput Secur 70(Supplement C):255–277CrossRefGoogle Scholar
  45. 45.
    NSLKDD. Nsl-kdd dataset. Accesses Jun 2013
  46. 46.
    Feng W, Zhang Q, Hu G, Huang JX (2014) Mining network data for intrusion detection through combining svms with ant colony networks. Future Gener Comput Syst 37(Supplement C):127–140 (Special section: innovative methods and algorithms for advanced data-intensive computing special section: semantics, intelligent processing and services for big data special section: advances in data-intensive modelling and simulation special section: hybrid intelligence for growing internet and its applications)CrossRefGoogle Scholar
  47. 47.
    Chung YY, Wahid N (2012) A hybrid network intrusion detection system using simplified swarm optimization (sso). Appl Soft Comput 12(9):3014–3022CrossRefGoogle Scholar
  48. 48.
    Tang TA, Mhamdi L, McLernon D, Zaidi SAR, Ghogho M (2016) Deep learning approach for network intrusion detection in software defined networking. In: 2016 international conference on wireless networks and mobile communications (WINCOM), pp 258–263Google Scholar
  49. 49.
    Guo C, Ping Y, Liu N, Luo S-S (2016) A two-level hybrid approach for intrusion detection. Neurocomputing 214(Supplement C):391–400CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Computer Science and Electronics EngineeringHunan UniversityChangshaChina
  2. 2.School of Information Science and EngineeringCentral South UniversityChangshaChina

Personalised recommendations