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Designing a modified feature aggregation model with hybrid sampling techniques for network intrusion detection

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Abstract

Cyber defense solutions that can adapt to new threats and learn to act independently of human guidance are necessary in light of the proliferation of so-called 'next-generation' cyberattacks. Multi-granularity feature aggregation is a method for detecting network intrusions, but its accuracy is often low due to class imbalance and various classifications of intrusions. To address this issue, this model employs a hybrid sampling algorithm composed of ADASYN and repeated edited nearest neighbors (RENN) for sample processing. The feature-discriminative ability of various assaults is improved by employing channel self-attention at the block level during classification. Finally, an enhanced reptile search algorithm (IRSA) is proposed, which uses a sine cosine algorithm and Levy flight to optimally select the weight of the proposed model. The Levy factor boosts the exploitation capabilities of the search agents, and an algorithm with improved global search capabilities prevents local minimal entrapment by undertaking a full-scale search space. To learn binary and multiclass classification, the model was trained on the CIC-IDS 2017, UNSW-NB15, and WSN-DS datasets. Accuracy and falsehood are just some of the evaluation criteria used in the confusion matrix to determine the system's efficacy. Experimental consequences demonstrate a high detection rate, good accuracy, and a relatively low false alarm rate (FAR), validating the efficacy of the suggested approach. Following that, K4 achieved an accuracy score of 81.99, the precision-recall (PR) was 82.69, the detection rate (D.R.) was 82.12, the F1-score was 80.33, and the FAR was 2.3, all in that order.

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The data that support the findings of this study are available upon reasonable request from the authors.

References

  1. Zhou, X., Liang, W., Li, W., Yan, K., Shimizu, S., Kevin, I., Wang, K.: Hierarchical adversarial attacks against graph-neural-network-based IoT network intrusion detection system. IEEE Internet Things J. 9(12), 9310–9319 (2021)

    Article  Google Scholar 

  2. Maseer, Z.K., Yusof, R., Mostafa, S.A., Bahaman, N., Musa, O., Al-rimy, B.A.S.: DeepIoT. IDS: hybrid deep learning for enhancing IoT network intrusion detection. Comput. Mater. Contin 69(3), 3945–3966 (2021)

    Google Scholar 

  3. Papadopoulos, P., Thornewill von Essen, O., Pitropakis, N., Chrysoulas, C., Mylonas, A., Buchanan, W.J.: Launching adversarial attacks against network intrusion detection systems for IoT. J. Cybersecur. Priv. 1(2), 252–273 (2021)

    Article  Google Scholar 

  4. Divya, N.S., Bobba, V., Vatambeti, R.: An adaptive cluster based vehicular routing protocol for secure communication. Wireless Pers. Commun. 127, 1717–1736 (2022). https://doi.org/10.1007/s11277-021-08717-4

    Article  Google Scholar 

  5. Khan, M.A., Khan, M.A., Jan, S.U., Ahmad, J., Jamal, S.S., Shah, A.A., Buchanan, W.J.: A deep learning-based intrusion detection system for MQTT-enabled IoT. Sensors 21(21), 7016 (2021)

    Article  Google Scholar 

  6. Nimbalkar, P., Kshirsagar, D.: Feature selection for intrusion detection system in Internet-of-Things (IoT). ICT Express 7(2), 177–181 (2021)

    Article  Google Scholar 

  7. Leevy, J. L., Khoshgoftaar, T. M., & Peterson, J. M. (2021, August). Mitigating class imbalance for iot network intrusion detection: a survey. In 2021 IEEE Seventh International Conference on Big Data Computing Service and Applications (BigDataService) (pp. 143–148). IEEE

  8. Islam, N., Farhin, F., Sultana, I., Kaiser, M.S., Rahman, M.S., Mahmud, M., Cho, G.H.: Towards machine learning based intrusion detection in IoT networks. Comput. Mater. Contin. 69(2), 1801 (2021)

    Google Scholar 

  9. Sahar, N., Mishra, R., & Kalam, S. (2021). Deep learning approach-based network intrusion detection system for fog-assisted iot. In Proceedings of international conference on big data, machine learning and their applications: ICBMA 2019 (pp. 39–50). Springer Singapore

  10. Kumar, V., Das, A.K., Sinha, D.: UIDS: a unified intrusion detection system for IoT environment. Evol. Intel. 14, 47–59 (2021)

    Article  Google Scholar 

  11. Siddiqui, A.J., Boukerche, A.: Adaptive ensembles of autoencoders for unsupervised IoT network intrusion detection. Computing 103(6), 1209–1232 (2021)

    Article  MathSciNet  Google Scholar 

  12. Vatambeti, R., Sanshi, S., Krishna, D.P.: An efficient clustering approach for optimized path selection and route maintenance in mobile ad hoc networks. J Ambient Intell Human Comput 14, 305–319 (2023). https://doi.org/10.1007/s12652-021-03298-3

    Article  Google Scholar 

  13. Shukla, A., Ahamad, S., Rao, G. N., Al-Asadi, A. J., Gupta, A., & Kumbhkar, M. (2021, December). Artificial intelligence assisted IoT data intrusion detection. In 2021 4th International Conference on Computing and Communications Technologies (ICCCT) (pp. 330–335). IEEE

  14. Keserwani, P.K., Govil, M.C., Pilli, E.S., Govil, P.: A smart anomaly-based intrusion detection system for the Internet of Things (IoT) network using GWO–PSO–R.F. model. J. Reliab. Intell. Environ. 7, 3–21 (2021)

    Article  Google Scholar 

  15. Choudhary, S., Dey, A., Kesswani, N.: CRIDS: correlation and regression-based network intrusion detection system for IoT. S.N. Comput. Sci. 2, 1–7 (2021)

    Google Scholar 

  16. Zhao, R., Gui, G., Xue, Z., Yin, J., Ohtsuki, T., Adebisi, B., Gacanin, H.: A novel intrusion detection method based on lightweight neural network for internet of things. IEEE Internet Things J. 9(12), 9960–9972 (2021)

    Article  Google Scholar 

  17. Liu, J., Yang, D., Lian, M., Li, M.: Research on intrusion detection based on particle swarm optimisation in IoT. IEEE Access 9, 38254–38268 (2021)

    Article  Google Scholar 

  18. Lee, J.D., Cha, H.S., Rathore, S., Park, J.H.: M-IDM: a multi-classification based intrusion detection model in healthcare IoT. Comput. Mater. Contin. (2021). https://doi.org/10.32604/cmc.2021.014774

    Article  Google Scholar 

  19. Gad, A.R., Nashat, A.A., Barkat, T.M.: Intrusion detection system using machine learning for vehicular ad hoc networks based on ToN-IoT dataset. IEEE Access 9, 142206–142217 (2021)

    Article  Google Scholar 

  20. Qaddoura, R., Al-Zoubi, M.A., Faris, H., Almomani, I.: A multi-layer classification approach for intrusion detection in IoT networks based on deep learning. Sensors 21(9), 2987 (2021)

    Article  Google Scholar 

  21. Zhong, M., Zhou, Y., Chen, G.: Sequential model-based intrusion detection system for IoT servers using deep learning methods. Sensors 21(4), 1113 (2021)

    Article  Google Scholar 

  22. Kumar, P., Gupta, G.P., Tripathi, R.: Design of anomaly-based intrusion detection system using fog computing for IoT network. Autom. Control. Comput. Sci. 55(2), 137–147 (2021)

    Article  Google Scholar 

  23. Aloul, F., Zualkernan, I., Abdalgawad, N., Hussain, L., & Sakhnini, D. (2021, July). Network intrusion detection on the IoT edge using adversarial autoencoders. In 2021 International Conference on Information Technology (ICIT) (pp. 120–125). IEEE

  24. Louk, M.H.L., Tama, B.A.: Dual-IDS: a bagging-based gradient boosting decision tree model for network anomaly intrusion detection system. Expert Syst. Appl. 213, 119030 (2023)

    Article  Google Scholar 

  25. Thakkar, A., Lohiya, R.: Fusion of statistical importance for feature selection in Deep Neural Network-based Intrusion Detection System. Inf. Fusion 90, 353–363 (2023)

    Article  Google Scholar 

  26. Logeswari, G., Bose, S., Anitha, T.: An intrusion detection system for sdn using machine learning. Intell. Autom. Soft Comput. 35(1), 867–880 (2023)

    Article  Google Scholar 

  27. Mahadik, S., Pawar, P.M., Muthalagu, R.: Efficient intelligent intrusion detection system for heterogeneous internet of things (HetIoT). J. Netw. Syst. Manage. 31(1), 2 (2023)

    Article  Google Scholar 

  28. Cui, J., Zong, L., Xie, J., Tang, M.: A novel multi-module integrated intrusion detection system for high-dimensional imbalanced data. Appl. Intell. 53(1), 272–288 (2023)

    Article  Google Scholar 

  29. Talukder, M.A., Hasan, K.F., Islam, M.M., Uddin, M.A., Akhter, A., Yousuf, M.A., Moni, M.A.: A dependable hybrid machine learning model for network intrusion detection. J. Inf. Secur. Appl. 72, 103405 (2023)

    Google Scholar 

  30. Venkatesan, S.: Design an intrusion detection system based on feature selection using ML algorithms. Math. Stat. Eng. Appl. 72(1), 702–710 (2023)

    Google Scholar 

  31. Kasongo, S.M.: A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework. Comput. Commun. 199, 113–125 (2023)

    Article  Google Scholar 

  32. Awajan, A.: A novel deep learning-based intrusion detection system for IOT networks. Computers 12(2), 34 (2023)

    Article  Google Scholar 

  33. Heidari, A., Navimipour, N.J., Unal, M.: A secure intrusion detection platform using blockchain and radial basis function neural networks for internet of drones. IEEE Internet Things J. (2023). https://doi.org/10.1109/JIOT.2023.3237661

    Article  Google Scholar 

  34. Yin, Y., Jang-Jaccard, J., Xu, W., Singh, A., Zhu, J., Sabrina, F., Kwak, J.: IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset. J. Big Data 10(1), 1–26 (2023)

    Article  Google Scholar 

  35. Alghanam, O.A., Almobaideen, W., Saadeh, M., Adwan, O.: An improved PIO feature selection algorithm for IoT network intrusion detection system based on ensemble learning. Expert Syst. Appl. 213, 118745 (2023)

    Article  Google Scholar 

  36. Mohamed, S., Ejbali, R.: Deep SARSA-based reinforcement learning approach for anomaly network intrusion detection system. Int. J. Inf. Secur. 22(1), 235–247 (2023)

    Article  Google Scholar 

  37. Jeyaselvi, M., Dhanaraj, R.K., Sathya, M., Memon, F.H., Krishnasamy, L., Dev, K., Qureshi, N.M.F.: A highly secured intrusion detection system for IoT using EXPSO-STFA feature selection for LAANN to detect attacks. Clust. Comput. 26(1), 559–574 (2023)

    Article  Google Scholar 

  38. Priyadharshini, M., Banu, A.F., Sharma, B., Chowdhury, S., Rabie, K., Shongwe, T.: Hybrid multi-label classification model for medical applications based on adaptive synthetic data and ensemble learning. Sensors 23(15), 6836 (2023)

    Article  Google Scholar 

  39. Abualigah, L., AbdElaziz, M., Sumari, P., Geem, Z.W., Gandomi, A.H.: Reptile search algorithm (RSA): a nature-inspired meta-heuristic optimiser. Expert Syst. Appl. 191, 116158 (2022)

    Article  Google Scholar 

  40. M. K. Putchala, ‘‘Deep learning approach for intrusion detection system 687 (IDS) in the Internet of Things (IoT) network using gated recurrent neural networks (GRU),’’ M.S. thesis, Dept. Comput. Sci. Eng., Wright State Univ., Dayton, OH, USA, 2017

  41. D. I. Edeh, ‘‘Network intrusion detection system using deep learning technique,’’ M.S. thesis, Dept. Comput., Univ. Turku, Turku, Finland, 2021

  42. Halbouni, A., Gunawan, T.S., Habaebi, M.H., Halbouni, M., Kartiwi, M., Ahmad, R.: ‘Machine learning and deep learning approaches for cybersecuriy: a review.’ IEEE Access 10, 19572–19585 (2022)

    Article  Google Scholar 

  43. I. Almomani, B. Al-Kasasbeh, and M. Al-Akhras, ‘‘WSN-DS: A dataset for intrusion detection systems in wireless sensor networks,’’ J. Sensors, vol. 2016, Aug. 2016, Art. no. 4731953

  44. Chhabra, M., & Kumar, R. (2022). A Smart Healthcare System Based on Classifier DenseNet 121 Model to Detect Multiple Diseases. In Mobile Radio Communications and 5G Networks: Proceedings of Second MRCN 2021 (pp. 297–312). Singapore: Springer Nature Singapore

  45. Dahou, A., AbdElaziz, M., Chelloug, S.A., Awadallah, M.A., Al-Betar, M.A., Al-qaness, M.A., Forestiero, A.: Intrusion detection system for IoT based on deep learning and modified reptile search algorithm. Comput. Intell. Neurosci. 2022, 6473507 (2022)

    Article  Google Scholar 

  46. Xiong, J., Peng, T., Tao, Z., Zhang, C., Song, S., Nazir, M.S.: A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction. Energy 266, 126419 (2023)

    Article  Google Scholar 

  47. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimisation problems. Knowl. Based Syst. 96, 120–133 (2016)

    Article  Google Scholar 

  48. Chechkin, A.; Metzler, R.; Klafter, J.; Gonchar, V.Y. Introduction to the Theory Lévy Flights. In Anomalous Transport: Foundations and Applications; Wiley-VCH: Weinheim, Germany, 2008

  49. Yang, X.-S., Deb, S.: Multiobjective cuckoo search for design optimisation. Comput. Oper. Res. 40, 1616–1624 (2013)

    Article  MathSciNet  Google Scholar 

  50. Kan, X., Fan, Y., Fang, Z., Cao, L., Xiong, N.N., Yang, D., Li, X.: A novel IoT network intrusion detection approach based on adaptive particle swarm optimisation convolutional neural network. Inf. Sci. 568, 147–162 (2021)

    Article  Google Scholar 

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, PVP Siddhartha Institute of Technology, Kanuru, Vijayawada, 520007, India

    NarasimhaSwamy Biyyapu & Phani Praveen Surapaneni

  2. Department of Computer Applications, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, 520007, India

    Esther Jyothi Veerapaneni

  3. Department of CSE-Data Science, Chalapathi Institute of Technology, Guntur, 522016, India

    Sai Srinivas Vellela

  4. School of Computer Science and Engineering, VIT-AP University, Vijayawada, India

    Ramesh Vatambeti

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  1. NarasimhaSwamy Biyyapu
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NB, EJV, PPS, SSV, RV all authors contributed equally.

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Correspondence to Ramesh Vatambeti.

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Biyyapu, N., Veerapaneni, E.J., Surapaneni, P.P. et al. Designing a modified feature aggregation model with hybrid sampling techniques for network intrusion detection. Cluster Comput 27, 5913–5931 (2024). https://doi.org/10.1007/s10586-024-04270-4

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