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An optimal and secure environment for intrusion detection using hybrid optimization based ResNet 101-C model

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Abstract

A monitoring system that can identify and assess abnormal activity is known as an intrusion detection system (IDS), and it is crucial in protecting the network against attacks. In an imbalanced dataset, the classification accuracy of the predictive model will decrease and the training time will be relatively long. The Hybrid Seagull Optimized ResNet 101-C (HSO-ResNet 101-C) technique is proposed in this study to precisely detect various attack types to overcome these issues. The computational complexity of the ResNet101-C architecture, which requires more time and resources when handling big data, is solved via the Hybrid Seagull Optimizer. To improve the performance of intrusion detection systems in big data environments some performance metrics such as accuracy, precision, recall, and F1-score are employed. In this paper, the two types of datasets CICIDS2017 and UNSW-NB15 can be utilized for detecting the intrusion detection rate. In the CICIDS2017 dataset, the performance rates of 100%, 99.88%, 98.9%, 99.2%, and 6.38% are obtained from the parameters of accuracy, precision, recall, and F1-score FAR respectively. The performance rate of accuracy, precision, recall, and F1-score FAR is 98.9%, 96.4%, 95%, and 97.8%, obtained from the UNSW-NB15 dataset.

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References

  1. Lee J, Park K (2021) GAN-based imbalanced data intrusion detection system. Pers Ubiquitous Comput 25(1):121–128

    Article  Google Scholar 

  2. Panigrahi R, Borah S, Bhoi AK, Ijaz MF, Pramanik M, Kumar Y, Jhaveri RH (2021) A consolidated decision tree-based intrusion detection system for binary and multiclass imbalanced datasets. Mathematics 9(7):751

    Article  Google Scholar 

  3. Huang S, Lei K (2020) IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks. Ad Hoc Netw 105:102177

    Article  Google Scholar 

  4. Bagui S, Li K (2021) Resampling imbalanced data for network intrusion detection datasets. J Big Data 8(1):1–41

    Article  Google Scholar 

  5. RM SP, Maddikunta PKR, Parimala M, Koppu S, Gadekallu TR, Chowdhary CL, Alazab M (2020) An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture. Comput Commun 160:139–149

    Article  Google Scholar 

  6. Liu L, Wang P, Lin J, Liu L (2020) Intrusion detection of imbalanced network traffic based on machine learning and deep learning. Ieee Access 9:7550–7563

    Article  Google Scholar 

  7. Manoranjini J, Chandrasekar A, Jothi S (2019) Improved QoS and avoidance of black hole attacks in MANET using trust detection framework. Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije, 60(3):274–284

  8. Otoum S, Kantarci B, Mouftah H (2019) Empowering reinforcement learning on big sensed data for intrusion detection. IEEE Int Conf Commun (ICC) IEEE 1–7

  9. Ding W, Nayak J, Naik B, Pelusi D, Mishra M (2020) Fuzzy and real-coded chemical reaction optimization for intrusion detection in industrial big data environment. IEEE Trans Industr Inf 17(6):4298–4307

    Article  Google Scholar 

  10. Peng K, Leung VC, Huang Q (2018) Clustering approach based on mini batch kmeans for intrusion detection system over big data. IEEE Access 6:11897–11906

    Article  Google Scholar 

  11. Gifty R, Bharathi R, Krishnakumar P (2019) Privacy and security of big data in cyber physical systems using Weibull distribution-based intrusion detection. Neural Comput Appl 31(1):23–34

    Article  Google Scholar 

  12. Csubák D, Szücs K, Vörös P, Kiss A (2016) Big data testbed for network attack detection. Acta Polytech Hungarica 13(2):47–57

    Google Scholar 

  13. Hassan MM, Gumaei A, Alsanad A, Alrubaian M, Fortino G (2020) A hybrid deep learning model for efficient intrusion detection in a big data environment. Inf Sci 513:386–396

    Article  Google Scholar 

  14. Karatas G, Demir O, Sahingoz OK (2020) Increasing the performance of machine learning-based IDSs on an imbalanced and up-to-date dataset. IEEE Access 8:32150–32162

    Article  Google Scholar 

  15. Al S, Dener M (2021) STL-HDL: A new hybrid network intrusion detection system for imbalanced dataset on big data environment. Comput Secur 110:102435

    Article  Google Scholar 

  16. Zhang H, Huang L, Wu CQ, Li Z (2020) An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset. Comput Netw 177:107315

    Article  Google Scholar 

  17. Yang L, Li J, Yin L, Sun Z, Zhao Y, Li Z (2020) Real-time intrusion detection in wireless network: A deep learning-based intelligent mechanism. IEEE Access 8:170128–170139

    Article  Google Scholar 

  18. Wu K, Chen Z, Li W (2018) A novel intrusion detection model for a massive network using convolutional neural networks. Ieee Access 6:50850–50859

    Article  Google Scholar 

  19. Uhm Y, Pak W (2021) Service-aware two-level partitioning for machine learning-based network intrusion detection with high performance and high scalability. IEEE Access 9:6608–6622

    Article  Google Scholar 

  20. Lin YD, Liu ZQ, Hwang RH, Nguyen VL, Lin PC, Lai YC (2022) Machine learning with variational Auto Encoder for imbalanced datasets in intrusion detection. IEEE Access 0:15247–15260

    Article  Google Scholar 

  21. Liu J, Gao Y, Hu F (2022) A fast network intrusion detection system using adaptive synthetic oversampling and LightGBM. Comput Secur 106:102289

    Article  Google Scholar 

  22. Ponmalar A, Dhanakoti V (2022) An intrusion detection approach using ensemble Support Vector Machine based Chaos Game Optimization algorithm in big data platform. Appl Soft Comput 116:108295

    Article  Google Scholar 

  23. Rashid M, Kamruzzaman J, Imam T, Wibowo S, Gordon S (2022) A tree-based stacking ensemble technique with feature selection for network intrusion detection. Appl Intell 1–14

  24. Ramkumar MP, Reddy PB, Thirukrishna JT, Vidyadhari C (2022) Intrusion detection in big data using hybrid feature fusion and optimization enabled deep learning based on spark architecture. Comput Secur 116:102668

    Article  Google Scholar 

  25. Mighan SN, Kahani M (2021) A novel scalable intrusion detection system based on deep learning. Int J Inf Secur 20(3):387–403

    Article  Google Scholar 

  26. Ahmad M, Riaz Q, Zeeshan M, Tahir H, Haider SA, Khan MS (2021) Intrusion detection in internet of things using supervised machine learning based on application and transport layer features using UNSW-NB15 data-set. EURASIP J Wirel Commun Netw 2021(1):1–23

    Article  Google Scholar 

  27. Dhiman G, Singh KK, Slowik A, Chang V, Yildiz AR, Kaur A, Garg M (2021) EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization. Int J Mach Learn Cybern 12(2):571–596

    Article  Google Scholar 

  28. Wang J, Li Y, Hu G (2021) Hybrid seagull optimization algorithm and its engineering application integrating Yin-Yang Pair idea. Eng Comput 38(3):2821–2857

    Article  Google Scholar 

  29. Dhiman G, Kumar V (2019) Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowl-Based Syst 165:169–196

    Article  Google Scholar 

  30. Yu X, Lu S, Guo L, Wang SH, Zhang YD (2021) ResGNet-C: A graph convolutional neural network for detection of COVID-19. Neurocomputing 452:592–605

    Article  Google Scholar 

  31. Farag HH, Said LA, Rizk MR (2021) Ahmed MAE. Hyperparameters optimization for ResNet and Xception in the purpose of diagnosing COVID-19. J Intell Fuzzy Syst (Preprint) 1–17

  32. Moustafa N (2019) UNSW_NB15 dataset. IEEE DataPort. Retrieved 4 Oct 2022, from https://ieee-dataport.org/documents/unswnb15-dataset

  33. CICDataset (2020) CICIDS2017. Kaggle. Retrieved 4 Oct 2022, from https://www.kaggle.com/datasets/cicdataset/cicids2017

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Author information

Authors and Affiliations

  1. Assistant professor, Department of Computing Technologies, SRM Institute of Science and Technologies, Kattankulathur, Chennai, 603203, India

    S. Nikkath Bushra

  2. Associate Professor/IT, Rajalakshmi Engineering College, Thandalam, Tamil Nadu, India

    Nalini Subramanian

  3. Department of Computer Science and Engineering, St. Joseph’s College of Engineering, Chennai, 600119, India

    A. Chandrasekar

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  1. S. Nikkath Bushra
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  2. Nalini Subramanian
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  3. A. Chandrasekar
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Contributions

All authors agreed on the content of the study. SNB, NS and AC collected all the data for analysis. SNB agreed on the methodology. SNB, NS and AC completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.

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Correspondence to S. Nikkath Bushra.

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Bushra, S.N., Subramanian, N. & Chandrasekar, A. An optimal and secure environment for intrusion detection using hybrid optimization based ResNet 101-C model. Peer-to-Peer Netw. Appl. 16, 2307–2324 (2023). https://doi.org/10.1007/s12083-023-01500-1

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  • DOI: https://doi.org/10.1007/s12083-023-01500-1

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