Skip to main content
SpringerLink
Log in

Discovery of Botnet Activities in Internet-of-Things System Using Dynamic Evolutionary Mechanism

  • Published:
New Generation Computing Aims and scope Submit manuscript

Abstract

The rapid growth in numerous technological aspects of computer networks and various lightweight protocols have endorsed the concept of the Internet of things. Despite pervasive concern about cyber-attacks and privacy violation issues, nowadays most of the research scientists are using Internet of Things (IoT) devices in broad way like home automation, and smart mobility for malicious and intrusion traffic identification. For securing machines and connecting people to valuable resources in IoT networks, in this study, we have proposed dynamic multi-population teaching–learning-based optimization algorithm, called DMPTLBO to protect against malicious intruders in network system. This work utilizes dynamic scheme for dividing the learner into sub-population to balance the exploration and exploitation of the search process based on the problem landscape. Furthermore, search information is shared and diffused among different sub-population to maintain the diversity and enhance the exploration process to escape high false alarm rate and low detection rate. Moreover, purposeful detecting strategy is used for maintaining accessibility, and interoperability based on historical information of the search process. The performance of the proposed method is evaluated by series of comprehensive computational experiments and comparing it with state-of-the-art algorithms obtainable for identifying attacks on BoT-IoT and UNSW-NB15 datasets. Experimental results show that the proposed model is significantly achieving higher performance compared to other state-of-art techniques in terms of classifier accuracy, detection rate, false alarm rate, and CPU time.

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

Similar content being viewed by others

References

  1. Gu, J., Lu, S.: An effective intrusion detection approach using SVM with na ¨ ıve Bayes feature embedding. Comput. Secur. (2020). https://doi.org/10.1016/j.cose.2020.102158

    Article  Google Scholar 

  2. Spa, E.H., Zamboni, D.: Intrusion detection using autonomous agents. Comput. Netw. 34(4), 547–570 (2000)

    Article  Google Scholar 

  3. Meng, T., Jing, X., Yan, Z., Pedrycz, W.: A survey on machine learning for data fusion. Inf. Fus. 57, 115–129 (2020). https://doi.org/10.1016/j.inffus.2019.12.001

    Article  Google Scholar 

  4. Usha, M.: Anomaly based intrusion detection for 802.11 networks with optimal features using SVM classifier. Wirel Netw (2016). https://doi.org/10.1007/s11276-016-1300-5

    Article  Google Scholar 

  5. Barshandeh, S., Masdari, M., Dhiman, G., Hosseini, V., Singh, K.K.: A range-free localization algorithm for IoT networks. Int. J. Intell. Syst. (2021). https://doi.org/10.1002/int.22524

    Article  Google Scholar 

  6. Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., Faruki, P.: Network intrusion detection for IoT security based on learning techniques. IEEE Commun. Surv. Tutor. 21(3), 2671–2701 (2019). https://doi.org/10.1109/COMST.2019.2896380

    Article  Google Scholar 

  7. Eddine, D., Bouabdallah, A., Lakhlef, H.: Internet of things security: a top-down survey. Comput. Netw. (2018). https://doi.org/10.1016/j.comnet.2018.03.012

    Article  Google Scholar 

  8. Song, W., Dong, W., Kang, L.: Group anomaly detection based on bayesian framework with genetic algorithm. Inf. Sci. (NY) (2020). https://doi.org/10.1016/j.ins.2020.03.110

    Article  Google Scholar 

  9. Masdari, M., Khezri, H.: A survey and taxonomy of the fuzzy signature-based intrusion detection systems. Appl. Soft Comput. J. 92, 106301 (2020). https://doi.org/10.1016/j.asoc.2020.106301

    Article  Google Scholar 

  10. Zhou, C., et al.: Design and Analysis of Multimodel-Based Anomaly Intrusion Detection Systems in Industrial Process Automation. IEEE Trans. Syst. Man Cybern. Syst. 45(10), 1345–1360 (2015). https://doi.org/10.1109/TSMC.2015.2415763

    Article  Google Scholar 

  11. Kaur, S., Singh, M.: Hybrid intrusion detection and signature generation using deep recurrent neural networks. Neural Comput. Appl. 9, 1–19 (2019). https://doi.org/10.1007/s00521-019-04187-9

    Article  Google Scholar 

  12. Rathore, M.M., Ahmad, A., Paul, A.: Real time intrusion detection system for ultra-high-speed big data environments. J. Supercomput. 72(9), 3489–3510 (2016). https://doi.org/10.1007/s11227-015-1615-5

    Article  Google Scholar 

  13. Abdollahzadeh, B., Soleimanian Gharehchopogh, F., Mirjalili, S.: Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems. Int. J. Intell. Syst. 36(10), 5887–5958 (2021)

    Article  Google Scholar 

  14. Garg, S., et al.: En-ABC: an ensemble artificial bee colony based anomaly detection scheme for cloud environment. J. Parallel Distrib. Comput. 135, 219–233 (2020). https://doi.org/10.1016/j.jpdc.2019.09.013

    Article  Google Scholar 

  15. Alhakami, W., Alharbi, A., Bourouis, S., Alroobaea, R.: Network anomaly intrusion detection using a nonparametric bayesian approach and feature selection. IEEE Access 7, 52181–52190 (2019). https://doi.org/10.1109/ACCESS.2019.2912115

    Article  Google Scholar 

  16. Sun, G., Li, J., Dai, J., Song, Z., Lang, F.: Feature selection for IoT based on maximal information coefficient. Fut. Gen. Comput. Syst. 89, 606–616 (2018). https://doi.org/10.1016/j.future.2018.05.060

    Article  Google Scholar 

  17. Abdollahzadeh, B., Gharehchopogh, F.S.: A multi-objective optimization algorithm for feature selection problems. Eng. Comput (2021). https://doi.org/10.1007/s00366-021-01369-9

    Article  Google Scholar 

  18. Moustafa, N., Turnbull, B., Choo, K.K.R.: An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of Internet of Things. IEEE Internet Things J. 6(3), 4815–4830 (2019). https://doi.org/10.1109/JIOT.2018.2871719

    Article  Google Scholar 

  19. Dwivedi, S., Vardhan, M., Tripathi, S.: Incorporating evolutionary computation for securing wireless network against cyberthreats. J. Supercomput. (2020). https://doi.org/10.1007/s11227-020-03161-w

    Article  Google Scholar 

  20. Guizani, M., Shafiq, M., Tian, Z., Bashir, A.K., Member, S.: IoT malicious traffic identification using wrapper-based feature selection mechanisms. Comput. Secur. (2020). https://doi.org/10.1016/j.cose.2020.101863

    Article  Google Scholar 

  21. Condomines, J.P., Zhang, R., Larrieu, N.: Network intrusion detection system for UAV ad-hoc communication: from methodology design to real test validation. Ad Hoc Netw. 90, 101759 (2018). https://doi.org/10.1016/j.adhoc.2018.09.004

    Article  Google Scholar 

  22. Zhou, Y., Cheng, G., Jiang, S., Dai, M.: Building an efficient intrusion detection system based on feature selection and ensemble classifier. Comput. Netw. 174, 107247 (2020). https://doi.org/10.1016/j.comnet.2020.107247

    Article  Google Scholar 

  23. Davahli, A., Shamsi, M., Abaei, G.: Hybridizing genetic algorithm and grey wolf optimizer to advance an intelligent and lightweight intrusion detection system for IoT wireless networks. J. Ambient Intell. Humaniz. Comput. (2020). https://doi.org/10.1007/s12652-020-01919-x

    Article  Google Scholar 

  24. Barshandeh, S., Piri, F., Sangani, S.R.: HMPA: an innovative hybrid multi-population algorithm based on artificial ecosystem-based and Harris Hawks optimization algorithms for engineering problems. Springer, London (2020)

    Google Scholar 

  25. H. Zhu, J. Cheng, C. Zhang, J. Wu, and X. Shao, “Detecting botnet by using particle swarm optimization algorithm based on voting system,” Appl. Soft Comput. J., p. 106060, 2020, doi: https://doi.org/10.1016/j.asoc.2019.106060.

  26. Abdollahzadeh, B., Barshandeh, S., Javadi, H., Epicoco, N.: An enhanced binary slime mould algorithm for solving the 0–1 knapsack problem. Eng. Comput. (2021). https://doi.org/10.1007/s00366-021-01470-z

    Article  Google Scholar 

  27. Shukla, A.K., Pippal, S.K., Chauhan, S.S.: An empirical evaluation of teaching—learning—based optimization, genetic algorithm and particle swarm optimization. An empirical evaluation of teaching—learning-based optimization, genetic algorithm. Int. J. Comput. Appl. (2019). https://doi.org/10.1080/1206212X.2019.1686562

    Article  Google Scholar 

  28. Ferrag, M.A., Maglaras, L.: DeepCoin: a novel deep learning and blockchain-based energy exchange framework for smart grids. IEEE Trans. Eng. Manag. (2019). https://doi.org/10.1109/TEM.2019.2922936

    Article  Google Scholar 

  29. Koroniotis, N., Moustafa, N., Sitnikova, E., Turnbull, B.: Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Fut. Gen. Comput. Syst. 100, 779–796 (2019). https://doi.org/10.1016/j.future.2019.05.041

    Article  Google Scholar 

  30. Benyamin, A., Farhad, S.G., Saeid, B.: Discrete farmland fertility optimization algorithm with metropolis acceptance criterion for traveling salesman problems. Int. J. Intell. Syst. 36(3), 1270–1303 (2021)

    Article  Google Scholar 

  31. Barshandeh, S., Haghzadeh, M.: A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems, vol. 37. Springer, London (2021)

    Google Scholar 

  32. Xia, X., Gui, L., Zhan, Z.H.: A multi-swarm particle swarm optimization algorithm based on dynamical topology and purposeful detecting. Appl. Soft Comput. J. 67, 126–140 (2018). https://doi.org/10.1016/j.asoc.2018.02.042

    Article  Google Scholar 

  33. Wang, M., Chen, H.: Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl. Soft Comput. J. 88, 105946 (2020). https://doi.org/10.1016/j.asoc.2019.105946

    Article  Google Scholar 

  34. Abdollahzadeh, B., Gharehchopogh, F.S., Mirjalili, S.: African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Comput. Ind. Eng. 158(January), 107408 (2021). https://doi.org/10.1016/j.cie.2021.107408

    Article  Google Scholar 

  35. Benkhelifa, E., Welsh, T., Hamouda, W., Member, S.: A critical review of practices and challenges in intrusion detection systems for IoT: toward universal and resilient systems. IEEE Commun. Surv. Tutor. 20(4), 3496–3509 (2018). https://doi.org/10.1109/COMST.2018.2844742

    Article  Google Scholar 

  36. Gothawal, D.B., Nagaraj, S.V.: Anomaly-based intrusion detection system in RPL by applying stochastic and evolutionary game models over IoT environment. Wirel. Pers. Commun. 110(3), 1323–1344 (2020). https://doi.org/10.1007/s11277-019-06789-x

    Article  Google Scholar 

  37. Garg, S., Kaur, K., Batra, S., Kaddoum, G., Kumar, N., Boukerche, A.: A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications. Fut. Gen. Comput. Syst. 104, 105–118 (2020). https://doi.org/10.1016/j.future.2019.09.038

    Article  Google Scholar 

  38. Nimbalkar, P., Kshirsagar, D.: Feature selection for intrusion detection system in Internet-of-Things (IoT). ICT Express 7(2), 177–181 (2021). https://doi.org/10.1016/j.icte.2021.04.012

    Article  Google Scholar 

  39. Sharma, N.V., Yadav, N.S.: An optimal intrusion detection system using recursive feature elimination and ensemble of classifiers. Microprocess. Microsyst. 85(July 2020), 104293 (2021). https://doi.org/10.1016/j.micpro.2021.104293

    Article  Google Scholar 

  40. Alazzam, H., Sharieh, A., Sabri, K.E.: A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer. Expert Syst. Appl. (2020). https://doi.org/10.1016/j.eswa.2020.113249

    Article  Google Scholar 

  41. Manimurugan, S., Qdah Majdi, A., Mohmmed, M., Narmatha, C., Varatharajan, R.: Intrusion detection in networks using crow search optimization algorithm with adaptive neuro-fuzzy inference system. Microprocess. Microsyst. 79, 103261 (2020). https://doi.org/10.1016/j.micpro.2020.103261

    Article  Google Scholar 

  42. Selvakumar, B., Muneeswaran, K.: Firefly algorithm based feature selection for network intrusion detection. Comput. Secur. (2018). https://doi.org/10.1016/j.cose.2018.11.005

    Article  Google Scholar 

  43. Hajisalem, V., Babaie, S.: A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection. Comput. Networks 136, 37–50 (2018). https://doi.org/10.1016/j.comnet.2018.02.028

    Article  Google Scholar 

  44. Vijayanand, R., Devaraj, D., Kannapiran, B.: Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection. Comput. Secur. 77, 304–314 (2018). https://doi.org/10.1016/j.cose.2018.04.010

    Article  Google Scholar 

  45. Khammassi, C., Krichen, S.: A GA-LR wrapper approach for feature selection in network intrusion detection. Comput. Secur. (2017). https://doi.org/10.1016/j.cose.2017.06.005

    Article  Google Scholar 

  46. Kuang, F., Xu, W., Zhang, S.: A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl. Soft Comput. J. 18, 178–184 (2014). https://doi.org/10.1016/j.asoc.2014.01.028

    Article  Google Scholar 

  47. Li, J., Zhao, Z., Li, R., Zhang, H.: AI-based two-stage intrusion detection for software defined IoT networks. IEEE Internet Things J. 6(2), 2093–2102 (2019). https://doi.org/10.1109/JIOT.2018.2883344

    Article  Google Scholar 

  48. Kotenko, I., Saenko, I., Branitskiy, A.: Framework for mobile internet of things security monitoring based on big data processing and machine learning. IEEE Access 6, 72714–72723 (2018). https://doi.org/10.1109/ACCESS.2018.2881998

    Article  Google Scholar 

  49. Tao, M.H., Zolkipli, M.F.: Scalable machine learning-based intrusion detection system for IoT-enabled smart cities. Sustain. Cities. Soc. (2020). https://doi.org/10.1016/j.scs.2020.102324

    Article  Google Scholar 

  50. León, J., Dueñas, A., Makluf, C.A., Cabello, F.C.: An auto-configuring mesh protocol with proactive source routing for bluetooth low energy yuzo iano. Int. J. Internet Technol. Secur. Trans. 8(1), 25–47 (2018)

    Article  Google Scholar 

  51. Butun, I., Osterberg, P., Song, H.: Security of the internet of things: vulnerabilities, attacks, and countermeasures. IEEE Commun. Surv. Tutorials 22(1), 616–644 (2020). https://doi.org/10.1109/COMST.2019.2953364

    Article  Google Scholar 

  52. Anthi, E., Williams, L., Slowinska, M., Theodorakopoulos, G., Burnap, P.: A Supervised intrusion detection system for smart home IoT devices. IEEE Internet Things J. 6(5), 9042–9053 (2019). https://doi.org/10.1109/JIOT.2019.2926365

    Article  Google Scholar 

  53. Kazmi, S., Javaid, N., Mughal, M.J., Akbar, M., Ahmed, S.H., Alrajeh, N.: Towards optimization of metaheuristic algorithms for IoT enabled smart homes targeting balanced demand and supply of energy. IEEE Access 7, 24267–24281 (2017). https://doi.org/10.1109/ACCESS.2017.2763624

    Article  Google Scholar 

  54. Buczak, A., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutor. 18(2), 1153–1175 (2015). https://doi.org/10.1109/COMST.2015.2494502

    Article  Google Scholar 

  55. Rao, R.V., Savsani, V.J., Balic, J.: Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Eng. Optim. 44(12), 1447–1462 (2012). https://doi.org/10.1080/0305215X.2011.652103

    Article  Google Scholar 

  56. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science (80–) 220(4598), 671–680 (1983). https://doi.org/10.1126/science.220.4598.671

    Article  MathSciNet  MATH  Google Scholar 

  57. Nseef, S.K., Abdullah, S., Turky, A., Kendall, G.: An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems. Knowl Based Syst. 104, 14–23 (2016). https://doi.org/10.1016/j.knosys.2016.04.005

    Article  Google Scholar 

  58. Moustafa, N., Slay, J., Creech, G.: Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks. IEEE Trans. Big Data 7790, 1–1 (2017). https://doi.org/10.1109/tbdata.2017.2715166

    Article  Google Scholar 

  59. Kumar, V., Sinha, D., Das, A.K., Pandey, S.C., Goswami, R.T.: An integrated rule based intrusion detection system: analysis on UNSW-NB15 data set and the real time online dataset. Cluster Comput. 23(2), 1397–1418 (2020). https://doi.org/10.1007/s10586-019-03008-x

    Article  Google Scholar 

  60. Hassan, M.M., Gumaei, A., Alsanad, A., Alrubaian, M., Fortino, G.: A hybrid deep learning model for efficient intrusion detection in big data environment. Inf. Sci. (NY) 513, 386–396 (2020). https://doi.org/10.1016/j.ins.2019.10.069

    Article  Google Scholar 

  61. Ahsan, M., Mashuri, M., Lee, M.H., Kuswanto, H., Prastyo, D.D.: Robust adaptive multivariate Hotelling’s T2 control chart based on kernel density estimation for intrusion detection system. Expert Syst. Appl. 145, 113105 (2020). https://doi.org/10.1016/j.eswa.2019.113105

    Article  Google Scholar 

  62. Lv, L., Wang, W., Zhang, Z., Liu, X.: A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine. Knowl Based Syst (2020). https://doi.org/10.1016/j.knosys.2020.105648

    Article  Google Scholar 

  63. Koroniotis, N., Moustafa, N., Sitnikova, E.: A new network forensic framework based on deep learning for Internet of Things networks: a particle deep framework. Fut. Gen. Comput. Syst. 110, 91–106 (2020). https://doi.org/10.1016/j.future.2020.03.042

    Article  Google Scholar 

  64. Lawal, M.A., Shaikh, R.A., Hassan, S.R.: Security analysis of network anomalies mitigation schemes in IoT networks. IEEE Access 8, 43355–43374 (2020). https://doi.org/10.1109/ACCESS.2020.2976624

    Article  Google Scholar 

  65. Guizani, M.: Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city. Futur. Gener. Comput. Syst. (2020). https://doi.org/10.1016/j.future.2020.02.017

    Article  Google Scholar 

  66. Ibitoye, O., Shafiq, O., Matrawy, A.: Analyzing adversarial attacks against deep learning for intrusion detection in IoT networks. arXiv Prepr. arXiv, 2019.

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India

    Alok Kumar Shukla & Shubhra Dwivedi

Authors
  1. Alok Kumar Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shubhra Dwivedi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alok Kumar Shukla.

Additional information

Publisher's Note

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

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shukla, A.K., Dwivedi, S. Discovery of Botnet Activities in Internet-of-Things System Using Dynamic Evolutionary Mechanism. New Gener. Comput. 40, 255–283 (2022). https://doi.org/10.1007/s00354-022-00158-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00354-022-00158-2

Keywords

Search

Navigation