Skip to main content
SpringerLink
Log in

DDoS attack detection in IoT environment using optimized Elman recurrent neural networks based on chaotic bacterial colony optimization

Cluster Computing Aims and scope Submit manuscript

Abstract

The Internet of Things (IoT) is made up of billions of interconnected devices that can transmit and receive data over the Internet. IoT devices have many vulnerabilities that attackers could use to compromise their security because of the heterogeneity of device connectivity. Distributed denial-of-service (DDoS) attacks against those applications become more common as IoT applications continue to expand and devolve. Identifying DDoS attacks is a difficult process due to the variety of IoT devices connected. The present article proposed a new method to detect DDoS attacks using an optimized Elman recurrent neural network (ERNN) based on chaotic bacterial colony optimization (CBCO) called CBCO-ERNN. The proposed method uses CBCO for obtaining optimal parameters (weights and biases) and structure (number of hidden neurons) of ERNN architecture. The chaos theory is applied to improve BCO’s exploration and exploitation capabilities by initializing the bacterial population and selecting the appropriate chemotaxis step size value. The CBCO approach is used to train the ERNN model to avoid local optima and enhance the convergence rate. The performance of the CBCO-ERNN is tested and evaluated using four benchmark attack datasets such as the BoT-IoT, CIC-IDS2017, CIC-DDoS2019, and IoTID20 datasets, and five performance metrics are considered: accuracy, sensitivity, specificity, precision, and F-Score. According to the experimental results, the CBCO-ERNN method provides a high detection and a faster convergence rate when compared to earlier algorithms.

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

Access this article

Log in via an institution

Price includes VAT (India)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Algorithm 2
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Data availability

The used datasets are available as follows.

BoT-IoT (“https://research.unsw.edu.au/projects/unsw-nb15-dataset).

CIC-IDS2017 (“https://www.unb.ca/cic/datasets/ids-2017.html”).

CIC-DDoS2019 (“https://www.unb.ca/cic/datasets/ddos-2019.html”).

IoTID20 (“https://sites.google.com/view/iot-network-intrusion-dataset”).

References

  1. Sinthuja, M., Suthendran, K.: DDoS attack detection using enhanced long-short term memory with hybrid machine learning algorithms. In: 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), pp. 1213–1218, IEEE (2022)

  2. Wani, A., Revathi, S.: DDoS detection and alleviation in IoT using SDN (SDIoT-DDoS-DA). J. Inst. Eng. 101(2), 117–128 (2020)

    Google Scholar 

  3. Roopak, M., Tian, G.Y., Chambers, J.: Multi-objective-based feature selection for DDoS attack detection in IoT networks. IET Netw. 9(3), 120–127 (2020)

    Article  Google Scholar 

  4. Vishwakarma, R., Jain, A.K.: A survey of DDoS attacking techniques and defence mechanisms in the IoT network. Telecommun. Syst. 73(1), 3–25 (2020)

    Article  Google Scholar 

  5. Jia, Y., Zhong, F., Alrawais, A., Gong, B., Cheng, X.: Flowguard: An intelligent edge defense mechanism against IoT DDoS attacks. IEEE Internet Things J. 7(10), 9552–9562 (2020)

    Article  Google Scholar 

  6. Dhahri, H.: Biogeography-based optimization for weight optimization in Elman neural network compared with meta-heuristics methods. BRAIN: Broad Res. Artif. Intell. Neurosci. 11(2), 82–103 (2020)

    Article  Google Scholar 

  7. Kumar, N.P., Vijayabaskar, S., Murali, L., Ramaswamy, K.: Design of optimal Elman recurrent neural network based prediction approach for biofuel production. Sci. Rep. 13(1), 8565 (2023)

    Article  Google Scholar 

  8. Sheela, K.G., Deepa, S.N.: Review on methods to fix number of hidden neurons in neural networks. Math. Prob. Eng. 2013, 1–13 (2013)

    Article  Google Scholar 

  9. Chandra, R.: Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction. IEEE Trans. Neural Netw. Learn. Syst. 26(12), 3123–3136 (2015)

    Article  MathSciNet  Google Scholar 

  10. Emambocus, B.A.S., Jasser, M.B., Amphawan, A.: A survey on the optimization of artificial neural networks using swarm intelligence algorithms. IEEE Access 11, 1280–1294 (2023)

    Article  Google Scholar 

  11. Revathi, J., Eswaramurthy, V., Padmavathi, P.: Bacterial colony optimization for data clustering. In: 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–4, IEEE (2019)

  12. Syed, N.F., Baig, Z., Ibrahim, A., Valli, C.: Denial of service attack detection through machine learning for the IoT. J. Inform. Telecommun. 4(4), 482–503 (2020)

    Article  Google Scholar 

  13. Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., Alazab, A.: A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks. Electronics 8(11), 1210 (2019)

    Article  Google Scholar 

  14. Elsaeidy, A.A., Jamalipour, A., Munasinghe, K.S.: A hybrid deep learning approach for replay and DDoS attack detection in a smart city. IEEE Access 9, 154864–154875 (2021)

    Article  Google Scholar 

  15. Novaes, M.P., Carvalho, L.F., Lloret, J., Proenca, M.L.: Long short-term memory and fuzzy logic for anomaly detection and mitigation in software-defined network environment. IEEE Access 8, 83765–83781 (2020)

    Article  Google Scholar 

  16. Shieh, C.-S., Lin, W.-W., Nguyen, T.-T., Chen, C.-H., Horng, M.-F., Miu, D.: Detection of unknown ddos attacks with deep learning and gaussian mixture model. Appl. Sci. 11(11), 5213 (2021)

    Article  Google Scholar 

  17. Krishna, E., Thangavelu, A.: Attack detection in IoT devices using hybrid metaheuristic lion optimization algorithm and firefly optimization algorithm. Int. J. Syst. Assur. Eng. Manag. (2021). https://doi.org/10.1007/s13198-021-01150-7

    Article  Google Scholar 

  18. Rinish Reddy, R., Rachamalla, S., Yoosuf, M.S., Anil, G.R.: Convolutional neural network based intrusion detection system and predicting the DDoS attack. In: Data intelligence and cognitive informatics, pp. 81–94. Springer Nature Singapore, Singapore (2023)

    Chapter  Google Scholar 

  19. Aljebreen, M., Mengash, H.A., Arasi, M.A., Aljameel, S.S., Salama, A.S., Hamza, M.A.: Enhancing DDoS attack detection using snake optimizer with ensemble learning on internet of things environment. IEEE Access 11, 104745 (2023)

    Article  Google Scholar 

  20. Bakhsh, S.A., Khan, M.A., Ahmed, F., Alshehri, M.S., Ali, H., Ahmad, J.: Enhancing IoT network security through deep learning-powered intrusion detection system. Internet Things 24, 100936 (2023)

    Article  Google Scholar 

  21. Wang, S., Xu, W., Liu, Y.: Res-TranBiLSTM: an intelligent approach for intrusion detection in the internet of things. Comput. Netw. 235, 109982 (2023)

    Article  Google Scholar 

  22. Diro, A., Chilamkurti, N.: Leveraging LSTM networks for attack detection in fog-to-things communications. IEEE Commun. Mag. 56(9), 124–130 (2018)

    Article  Google Scholar 

  23. Awad, A.A., Ali, A.F., Gaber, T.: An improved long short term memory network for intrusion detection. PLoS ONE 18(8), e0284795 (2023)

    Article  Google Scholar 

  24. Thangasamy, A., Sundan, B., Govindaraj, L.: A novel framework for DDoS attacks detection using hybrid LSTM techniques. Comput. Syst. Sci. Eng. 45(3), 2553 (2023)

    Article  Google Scholar 

  25. Bhale, P., Chowdhury, D.R., Biswas, S., Nandi, S.: OPTIMIST: lightweight and transparent IDS with optimum placement strategy to mitigate mixed-rate DDoS attacks in IoT networks. IEEE Internet Things J. 10, 8357 (2023)

    Article  Google Scholar 

  26. Adefemi Alimi, K.O., Ouahada, K., Abu-Mahfouz, A.M., Rimer, S., Alimi, O.A.: Refined LSTM based intrusion detection for denial-of-service attack in internet of things. J. Sens. Actuator Netw. 11(3), 32 (2022)

    Article  Google Scholar 

  27. Katib, I., Ragab, M.: Blockchain-assisted hybrid harris hawks optimization based deep DDoS attack detection in the IoT environment. Mathematics 11(8), 1887 (2023)

    Article  Google Scholar 

  28. Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)

    Article  Google Scholar 

  29. Niu, B., Wang, H.: Bacterial colony optimization: principles and foundations. In: Emerging Intelligent Computing Technology and Applications: 8th International Conference, ICIC 2012, Huangshan, China, July 25–29, 2012. Proceedings 8, pp. 501–506. Springer, (2012)

  30. Vijayakumari, K., Baby Deepa, V.: Fuzzy C-means hybrid with fuzzy bacterial colony optimization. In: Advances in Electrical and Computer Technologies: Select Proceedings of ICAECT 2020, pp. 75–87. Springer, Singapore (2021)

    Chapter  Google Scholar 

  31. Prakash, V., Vinothina, V., Kalaiselvi, K., Velusamy, K.: An improved bacterial colony optimization using opposition-based learning for data clustering. Clust. Comput. 25(6), 4009–4025 (2022)

    Article  Google Scholar 

  32. Revathi, J., Eswaramurthy, V., Padmavathi, P.: Hybrid data clustering approaches using bacterial colony optimization and k-means. IOP Conf. Ser.: Mater. Sci. Eng. 1070(1), 012064 (2021)

    Article  Google Scholar 

  33. Tamilarisi, K., Gogulkumar, M., Velusamy, K.: Data clustering using bacterial colony optimization with particle swarm optimization. In: 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–5. IEEE (2021)

  34. Babu, S.S., Jayasudha, K.: A simplex method-based bacterial colony optimization for data clustering. In: Smith, J. (ed.) Innovative Data Communication Technologies and Application: Proceedings of ICIDCA 2021, pp. 987–995. Springer, Singapore (2022)

    Chapter  Google Scholar 

  35. Babu, S.S., Jayasudha, K.: A simplex method-based bacterial colony optimization algorithm for data clustering analysis. Int. J. Pattern Recognit. Artif. Intell. 36(12), 2259027 (2022)

    Article  Google Scholar 

  36. Wang, H., Tan, L., Niu, B.: Feature selection for classification of microarray gene expression cancers using bacterial colony optimization with multi-dimensional population. Swarm Evol. Comput. 48, 172–181 (2019)

    Article  Google Scholar 

  37. İlkin, S., Gençtürk, T.H., Gülağız, F.K., Özcan, H., Altuncu, M.A., Şahin, S.: hybSVM: bacterial colony optimization algorithm based SVM for malignant melanoma detection. Eng. Sci. Technol. Int. J. 24(5), 1059–1071 (2021)

    Google Scholar 

  38. Niu, B., Xie, T., Bi, Y., Liu, J.: Bacterial colony optimization for integrated yard truck scheduling and storage allocation problem. In: Intelligent Computing in Bioinformatics: 10th International Conference, ICIC 2014, Taiyuan, China, August 3–6, 2014. Proceedings 10, pp. 431–437. Springer (2014)

  39. Boccaletti, S., Grebogi, C., Lai, Y.-C., Mancini, H., Maza, D.: The control of chaos: theory and applications. Phys. Rep. 329(3), 103–197 (2000)

    Article  MathSciNet  Google Scholar 

  40. Singh, T.: A chaotic sequence-guided Harris hawks optimizer for data clustering. Neural Comput. Appl. 32, 17789–17803 (2020)

    Article  Google Scholar 

  41. Bharti, K.K., Singh, P.K.: Chaotic gradient artificial bee colony for text clustering. Soft. Comput. 20, 1113–1126 (2016)

    Article  Google Scholar 

  42. Kumar, S., Singh, S.K.: Feature selection and recognition of face by using hybrid chaotic PSO-BFO and appearance-based recognition algorithms. Int. J. Nat. Comput. Res. (IJNCR) 5(3), 26–53 (2015)

    Article  Google Scholar 

  43. Parpinelli, R.S., Plichoski, G.F., Silva, R.S.D., Narloch, P.H.: A review of techniques for online control of parameters in swarm intelligence and evolutionary computation algorithms. Int. J. Bio-Inspir. Comput. 13(1), 1–20 (2019)

    Article  Google Scholar 

  44. Sivasakthi, B., Selvanayagi D.: Prediction of osteoporosis disease using enhanced Elman recurrent neural network with bacterial colony optimization. In Computational Vision and Bio-Inspired Computing: Proceedings of ICCVBIC, pp. 211–220. Springer (2022)

  45. Yang, L., Wang, F., Zhang, J., Ren, W.: Remaining useful life prediction of ultrasonic motor based on Elman neural network with improved particle swarm optimization. Measurement 143, 27–38 (2019)

    Article  Google Scholar 

  46. Wang, Y., Wang, L., Yang, F., Di, W., Chang, Q.: Advantages of direct input-to-output connections in neural networks: the Elman network for stock index forecasting. Inf. Sci. 547, 1066–1079 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  47. Sadeghi-Niaraki, A., Mirshafiei, P., Shakeri, M., Choi, S.-M.: Short-term traffic flow prediction using the modified Elman recurrent neural network optimized through a genetic algorithm. IEEE Access 8, 217526–217540 (2020)

    Article  Google Scholar 

  48. Chowdhury, N.: A comparative analysis of feed-forward neural network & recurrent neural network to detect intrusion. In: 2008 International Conference on Electrical and Computer Engineering, pp. 488–492. IEEE (2008)

  49. Chiba, Z., Abghour, N., Moussaid, K., El Omri, A., Rida, M.: A novel architecture combined with optimal parameters for back propagation neural networks applied to anomaly network intrusion detection. Comput. Secur. 75, 36–58 (2018)

    Article  Google Scholar 

  50. Tuan, T.A., Long, H.V., Son, L.H., Kumar, R., Priyadarshini, I., Son, N.T.K.: Performance evaluation of Botnet DDoS attack detection using machine learning. Evol. Intel. 13(2), 283–294 (2020)

    Article  Google Scholar 

  51. 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. Futur. Gener. Comput. Syst. 100, 779–796 (2019)

    Article  Google Scholar 

  52. Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp 1, 108–116 (2018)

    Google Scholar 

  53. Sharafaldin, I., Lashkari, A.H., Hakak, S., Ghorbani, A.A.: Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. In: 2019 International Carnahan Conference on Security Technology (ICCST), pp. 1–8. IEEE (2019)

  54. Alzahrani, R.J., Alzahrani, A.: Security analysis of ddos attacks using machine learning algorithms in networks traffic. Electronics 10(23), 2919 (2021)

    Article  Google Scholar 

  55. Ullah, I., Mahmoud, Q.H.: A scheme for generating a dataset for anomalous activity detection in iot networks. In: Canadian Conference on Artificial Intelligence, pp. 508–520. Springer, Cham (2020)

    Google Scholar 

  56. Velusamy, K., Amalraj, R.: Cascade correlation neural network with deterministic weight modification for predicting stock market price. IOP Conf. Ser.: Mater. Sci. Eng. 1110(1), 012005 (2021)

    Article  Google Scholar 

  57. Velusamy, K., Amalraj, R.: Performance of the cascade correlation neural network for predicting the stock price. In: 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–6. IEEE (2017)

  58. Bashaiwth, A., Binsalleeh, H., AsSadhan, B.: An explanation of the LSTM model used for DDoS attacks classification. Appl. Sci. 13(15), 8820 (2023)

    Article  Google Scholar 

Download references

Funding

No organizations or financial sources are supporting this research.

Author information

Authors and Affiliations

  1. Department of IT & CSE (IOT), Guru Nanak Institutions Technical Campus, Hyderabad, 501506, India

    M. I. Thariq Hussan

  2. Department of Computer Science and Engineering, CMR Technical Campus, Hyderabad, 501491, India

    G. Vinoda Reddy

  3. Department of Computer Science, College of Engineering and Technology, Wollega University, Nekemte, Ethiopia

    P. T. Anitha

  4. Department of Computer Science, Kristu Jayanti College, Bengaluru, India

    A. Kanagaraj

  5. Department of Information Technology, Vignan Institute of Technology and Science, Hyderabad, India

    P. Naresh

Authors
  1. M. I. Thariq Hussan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Vinoda Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. T. Anitha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Kanagaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. P. Naresh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the algorithms, development, and article. The final manuscript has been read and approved by all authors.

Corresponding author

Correspondence to M. I. Thariq Hussan.

Ethics declarations

Conflict of interest

There are no conflicts of interest stated by 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

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hussan, M.I.T., Reddy, G.V., Anitha, P.T. et al. DDoS attack detection in IoT environment using optimized Elman recurrent neural networks based on chaotic bacterial colony optimization. Cluster Comput (2023). https://doi.org/10.1007/s10586-023-04187-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10586-023-04187-4

Keywords

Search

Navigation