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Stacking Enabled Ensemble Learning Based Intrusion Detection Scheme (SELIDS) for IoV

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Abstract

A revolutionary approach for enhancing driving efficiency and safety in intelligent transportation systems (ITS) is deploying autonomous vehicles. Vehicle-to-everything (V2X) technology facilitates interactions between vehicles and infrastructure. However, the Internet of Vehicles (IoV) is susceptible to many cyberattacks, encompassing impersonation, denial of service (DoS), and fuzzy assaults. This paper proposes an intelligent network intrusion detection system (NIDS) using machine learning algorithms. The usage of the ML approach in a Stacking-enabled Ensemble Learning-based Intrusion Detection Scheme (SELIDS) for IoV is proposed. We additionally examine each technique’s shortcomings and how they affect the NIDS efficiency. Deploying the proposed NIDS on the benchmark dataset demonstrates the capacity of the system to recognize different kinds of assaults. Finally, we explore the potential for NIDS to collaborate with additional security technologies in the future. Empirical results prove the efficacy of the proposed mechanism.

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Data Availability

The data set generated and/or analyzed during the current study is available upon reasonable request from the corresponding author. However, data sets are available as open source.

References

  1. He Q, Meng X, Qu R. Towards a severity assessment method for potential cyber attacks to connected and self-driving vehicles. J Adv Transp. 2020;1:1–15. https://doi.org/10.1155/2020/6873273.

    Article  Google Scholar 

  2. Al-Jarrah OY, Maple C, Dianati M, Oxtoby D, Mouzakitis A. Intrusion detection systems for intra-vehicle networks: a review. IEEE Access. 2029;7:21266–89. https://doi.org/10.1109/ACCESS.2019.2894183.

    Article  Google Scholar 

  3. Yang L, Moubayed A, Shami A. MTH-IDS: a multitiered hybrid intrusion detection system for internet of vehicles. IEEE Internet Things J. 2022;9(1):616–32. https://doi.org/10.1109/JIOT.2021.3084796.

    Article  Google Scholar 

  4. Alheeti KMA, Gruebler A, McDonald-Maier K. Intelligent intrusion detection of grey hole and rushing attacks in self-driving vehicular networks. Computers. 2016;5(3):1–18. https://doi.org/10.3390/computers5030016.

    Article  Google Scholar 

  5. Gad AR, Nashat AA, Barkat TM. Intrusion detection system using machine learning for vehicular ad hoc networks based on ToN-IoT dataset. IEEE Access. 2021;9:142206–17. https://doi.org/10.1109/ACCESS.2021.3120626.

    Article  Google Scholar 

  6. Kosmanos D, Pappas A, Maglaras L, Moschoyiannis S, Aparicio-Navarro FJ, Argyriou A, Janicke H. A novel intrusion detection system against spoofing attacks in connected electric vehicles. Array. 2020;2019(5):1–11. https://doi.org/10.1016/j.array.2019.100013.

    Article  Google Scholar 

  7. Maseer ZK, Yusof R, Bahaman N, Mostafa SA, Foozy CFM. Benchmarking of machine learning for anomaly-based intrusion detection systems in the CICIDS2017 dataset. In IEEE Access. 2021;9:22351–70. https://doi.org/10.1109/ACCESS.2021.3056614.

    Article  Google Scholar 

  8. Liao HJ, Lin CHR, Lin YC, Tung KY. Intrusion detection system: a comprehensive review. J Netw Comput Appl. 2013;36(1):16–24. https://doi.org/10.1016/j.jnca.2012.09.004.

    Article  Google Scholar 

  9. Bajpai S, Sharma K, Chaurasia BK. Intrusion detection framework in IoT networks. Springer Nat Comput Sci J, Special Issue on Machine Learning and Smart Systems. 2023;4(350):1–16. https://doi.org/10.1007/s42979-023-01770-9.

    Article  Google Scholar 

  10. Yulianto A, Sukarno P, Suwastika NA. Improving adaboost-based intrusion detection system (IDS) performance on CIC IDS 2017 dataset. J Phys: Conference Series, IOP Publishing. 2019;1192(1):1–11. https://doi.org/10.1088/1742-6596/1192/1/012018.

    Article  Google Scholar 

  11. Revathi S, Malathi A. A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. Int J Eng Res Technol (IJERT). 2013;2(12):1848–53.

    Google Scholar 

  12. Alazzam H, Sharieh A, Sabri KE. A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer. Expert Syst Appl. 2020;148(113249):1–14. https://doi.org/10.1016/j.eswa.2020.113249.

    Article  Google Scholar 

  13. Gopalan SS, Ravikumar D, Linekar D, Raza A, Hasib M. Balancing approaches towards ML for IDS: a survey for the CSE-CIC IDS dataset. In: International Conference on Communications, Signal Processing, and their Applications (ICCSPA). 2021; https://doi.org/10.1109/ICCSPA49915.2021.9385742

  14. Kasongo SM, Sun Y. Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset. J Big Data. 2020;7:1–20. https://doi.org/10.1186/s40537-020-00379-6.

    Article  Google Scholar 

  15. Husain A, Salem A, Jim C, Dimitoglou G. Development of an efficient network intrusion detection model using extreme gradient boosting (XGBoost) on the UNSW-NB15 dataset. In: IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). 2019: 1–7. https://doi.org/10.1109/ISSPIT47144.2019.9001867

  16. Bajpai S, Sharma K, Chaurasia BK. Intrusion detection system in IoT network using ML. NeuroQuantology. 2022;20(13):3597–601. https://doi.org/10.14704/nq.2022.20.13.NQ88441.

    Article  Google Scholar 

  17. Zhang Y, Ren X, Zhang J. Intrusion detection method based on information gain and Relief feature selection. In: 2019 International Joint Conference on Neural Networks (IJCNN). 2019; https://doi.org/10.1109/IJCNN.2019.8851756

  18. Ustebay S, Turgut Z, Aydin MA. Intrusion detection system with recursive feature elimination by using random forest and deep learning classifier. In: International congress on big data, deep learning and fighting cyber terrorism (IBIGDELFT). 2018: 71–76. https://doi.org/10.1109/IBIGDELFT.2018.8625318

  19. Zhou Y, Cheng G, Jiang S, Dai M. Building an efficient intrusion detection system based on feature selection and ensemble classifier. Comput Netw. 2020;174(107247):1–17. https://doi.org/10.1016/j.comnet.2020.107247.

    Article  Google Scholar 

  20. Zong W, Chow YW, Susilo W. A two-stage classifier approach for network intrusion detection. In: Information Security Practice and Experience: 14th International Conference, ISPEC. 2018; 14: 329- 340. https://doi.org/10.1007/978-3-319-99807-7_20

  21. Yulianto A, Sukarno P, Suwastika NA. Improving adaboost-based intrusion detection system (IDS) performance on CIC IDS 2017 dataset. In: Journal of Physics: Conference Series. IOP Publishing. 2019; 1192(1): 012018, 1–9. https://doi.org/10.1088/1742-6596/1192/1/012018

  22. Patil S, Varadarajan V, Mazhar SM, Sahibzada A, Ahmed N, Sinha O, Kumar S, Shaw K, Kotecha K. Explainable artificial intelligence for intrusion detection system. Electronics. 2022;11(3079):1–23. https://doi.org/10.3390/electronics11193079.

    Article  Google Scholar 

  23. Ullah S, Khan MA, Ahmad J, Jamal SS, Huma ZE, Hassan MT, Pitropakis N, Arshad BWJ. HDL-IDS: a hybrid deep learning architecture for intrusion detection in the internet of vehicles. Sensors. 2022;22(1340):1–20. https://doi.org/10.3390/s22041340.

    Article  Google Scholar 

  24. Sousa B, Magaia N, Silva S. An intelligent intrusion detection system for 5G-enabled internet of vehicles. Electronics. 2023;12(1757):1–15. https://doi.org/10.3390/electronics12081757.

    Article  Google Scholar 

  25. Agbaje P, Anjum A, Mitra A, Hounsinou S, Nwafor E, Olufowobi H. Privacy-Preserving Intrusion Detection System for Internet of Vehicles using Split Learning. In: Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies (BDCAT '23). 2024; 07, 1–8. https://doi.org/10.1145/3632366.3632388

  26. Wang Z, Ren H, Lu R, Huang L. Stacking Based LightGBM- CatBoost-Random Forest Algorithm and Its Application in Big Data Modeling. In: 4th International Conference on Data-driven Optimization of Complex Systems (DOCS). 2022: 1- 6. https://doi.org/10.1109/DOCS55193.2022.9967714

  27. Lee H, Jeong SH, Kim H K. OTIDS: A novel intrusion detection system for in-vehicle network by using remote frame. In: 2017 15th Annual Conference on Privacy, Security and Trust (PST). 2018:57–66. https://doi.org/10.1109/PST.2017.00017

  28. Panigrahi R, Borah S. A detailed analysis of CICIDS2017 dataset for designing Intrusion Detection Systems. International Journal of Engineering & Technology. 2018; 7(3.24), 479–482. https://doi.org/10.14419/ijet.v7i3.24.22797

  29. Pelletier Z, Abualkibash M. Evaluating the CIC IDS-2017 dataset using machine learning methods and creating multiple predictive models in the statistical computing language R. Science. 2020;5(2):187–91.

    Google Scholar 

  30. Leevy JL. Khoshgoftaar TM A survey and analysis of intrusion detection models based on cse-cic-ids2018 big data. Journal of Big Data. 2020;7(1):1–19. https://doi.org/10.1186/s40537-020-00382.

    Article  Google Scholar 

  31. Sun P, Liu P, Li Q, Liu C, Lu X, Hao R, Chen J. DL-IDS: Extracting features using CNN-LSTM hybrid network for intrusion detection system. Security Commun Netw. 2020. https://doi.org/10.1155/2020/8890306.

    Article  Google Scholar 

  32. Hindy H, Brosset D, Bayne E, Seeam AK, Tachtatzis C, Atkinson R, Bellekens X. A taxonomy of network threats and the effect of current datasets on intrusion detection systems. IEEE Access. 2020;8:104650–75. https://doi.org/10.1109/ACCESS.2020.3000179.

    Article  Google Scholar 

  33. Tripathi A, Misra A, Kumar K and Chaurasia BK. Optimized Machine Learning for classifying colorectal tissues. In: Springer Nature Computer Science Journal. Special Issue on Machine Learning and Smart Systems., 2023; 4(461): 1–14. https://doi.org/10.1007/s42979-023-01882-2

  34. Tripathi A, Misra A, Kumar K and Chaurasia BK. Colon Cancer Tissue Classification Using ML. In: 6th International Conference on Information Systems and Computer Networks (ISCON). 2023: 1–6. https://doi.org/10.1109/ISCON57294.2023.10112181

  35. Sahu S, Mehtre B M. Network intrusion detection system using J48 Decision Tree. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2015: 2023–2026. https://doi.org/10.1109/ICACCI.2015.7275914

  36. Louk MHL, Tama BA. Dual-IDS: A bagging-based gradient boosting decision tree model for network anomaly intrusion detection system. Expert Syst Appl. 2023. https://doi.org/10.1016/j.eswa.2022.119030.

    Article  Google Scholar 

  37. Bansal A, Kaur S. Extreme gradient boosting based tuning for classification in intrusion detection systems. In: Advances in Computing and Data Sciences: Second International Conference, ICACDS 2018; 372–380. https://doi.org/10.1007/978-981-13-1810-8_37

  38. Farnaaz N, Jabbar MA. Random forest modeling for network intrusion detection system. Procedia Computer Science. 2016;89:213–7. https://doi.org/10.1016/j.procs.2016.06.047.

    Article  Google Scholar 

  39. Jin D, Lu Y, Qin J, Cheng Z, Mao Z. SwiftIDS: real-time intrusion detection system based on lightGBM and parallel intrusion detection mechanism. Comput Secur. 2020;97(101984):1–12. https://doi.org/10.1016/j.cose.2020.101984.

    Article  Google Scholar 

  40. Dhaliwal SS, Nahid AA, Abbas R. Effective intrusion detection system using XGBoost. Information. 2018;9(7):1–24. https://doi.org/10.3390/info9070149.

    Article  Google Scholar 

  41. Jumabek A, Yang S, Noh Y. CatBoost-based network intrusion detection on imbalanced CIC-IDS-2018 dataset. KICS. 2021;46(12):2191–7. https://doi.org/10.7840/kics.2021.46.12.2191.

    Article  Google Scholar 

  42. Hasan M, Mehedi A, Nasser M, Pal B, Ahmad S. Support vector machine and random forest modeling for intrusion detection system (IDS). J Intell Learn Syst Appl. 2014;6(01):45–52. https://doi.org/10.4236/jilsa.2014.61005.

    Article  Google Scholar 

  43. Li X, Chen W, Zhang Q, Wu L. Building auto-encoder intrusion detection system based on random forest feature selection. Comput Secur. 2020;95(101851):1–15. https://doi.org/10.1016/j.cose.2020.101851.

    Article  Google Scholar 

  44. Waskle S, Parashar L, Singh U. Intrusion detection system using PCA with random forest approach. In: International Conference on Electronics and Sustainable Communication Systems (ICESC). 2020: 803–808. https://doi.org/10.1109/ICESC48915.2020.9155656

  45. Hua Y. An efficient traffic classification scheme using embedded feature selection and lightgbm. In: Information Communication Technologies Conference (ICTC). 2020: 125–130. https://doi.org/10.1109/ICTC49638.2020.9123302

  46. Yao R, Wang N, Liu Z, Chen P, Ma D, Sheng X. Intrusion detection system in the smart distribution network: a feature engineering-based AE-lightGBM approach. Energy Rep. 2021;7:353–61. https://doi.org/10.1016/j.egyr.2021.10.024.

    Article  Google Scholar 

  47. Devan P, Khare N. An efficient XGBoost–DNN-based classification model for network intrusion detection system. Neural Comput Appl. 2020;32:12499–514. https://doi.org/10.1007/s00521-020-04708.

    Article  Google Scholar 

  48. Alzahrani AO, Alenazi MJ. Designing a network intrusion detection system based on machine learning for software defined networks. Future Internet. 2021;13(5):1–18. https://doi.org/10.3390/fi13050111.

    Article  Google Scholar 

  49. Jin D, Lu Y, Qin J, Cheng Z, Mao Z. KC-IDS: Multi-layer intrusion detection system. In: International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS). 2020: 1–5. https://doi.org/10.1109/HPBDIS49115.2020.9130573

  50. Dong X, Yu Z, Cao W, Shi Y, Ma Q. A survey on ensemble learning. Front Comput Sci. 2020;14:241–58. https://doi.org/10.1007/s11704-019-8208-z.

    Article  Google Scholar 

  51. Sagi O, Rokach L. Ensemble learning: a survey. Wiley. 2018;8(4): e1249. https://doi.org/10.1002/widm.1249.

    Article  Google Scholar 

  52. Verma A, Ranga V. ELNIDS: Ensemble learning based network intrusion detection system for RPL based Internet of Things. In: 4th International conference on Internet of Things: Smart innovation and usages (IoT-SIU). 2019: 1–6. https://doi.org/10.1109/IoT-SIU.2019.8777504

  53. Wang Y, Wang D, Geng N, Wang Y, Yin Y, Jin Y. Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection. Appl Soft Comput. 2019;77:188–204. https://doi.org/10.1016/j.asoc.2019.01.015.

    Article  Google Scholar 

  54. Tama BA, Lim S. Ensemble learning for intrusion detection systems: a systematic mapping study and cross-benchmark evaluation. Comput Sci Rev. 2021;39(100357):1–27. https://doi.org/10.1016/j.cosrev.2020.100357.

    Article  MathSciNet  Google Scholar 

  55. Bajpai S, Sharma K, Chaurasia BK. A hybrid meta-heuristics algorithm—XGBoost based approach for IDS in IoT. Springer Nat Comput Sci J, Special Issue on Machine Learning and Smart Systems. 2024;5(537):1–16. https://doi.org/10.1007/s42979-024-02913-2.

    Article  Google Scholar 

  56. Upadhyaya S, Mehrotra D. Benchmarking the bagging and boosting (B & B) algorithms for modeling optimized autonomous intrusion detection systems (AIDS). SN Comput Sci. 2023. https://doi.org/10.1007/s42979-023-01914-x.

    Article  Google Scholar 

  57. Lampe B, Meng W. Intrusion detection in the automotive domain: a comprehensive review. IEEE Communications Surveys & Tutorials. 2023;25(4):2356–426. https://doi.org/10.1109/COMST.2023.3309864.

    Article  Google Scholar 

  58. Mahajan P, Uddin S, Hajati F, Moni MA. Ensemble learning for disease prediction: a review. Healthcare. 1808;2023(11):1–21. https://doi.org/10.3390/healthcare11121808.

    Article  Google Scholar 

  59. Chaurasia BK, Raj H, Rathour SS, Singh PB. Transfer learning driven ensemble model for detection of diabetic retinopathy disease. Med Biol Eng Comput, Springer. 2023;61:2033–49. https://doi.org/10.1007/s11517-023-02863-6.

    Article  Google Scholar 

  60. Shukla MM, Tripathi BK, Dwvedi T, Tripathi A, Chaurasia BK. A Hybrid CNN with Transfer Learning for Skin Cancer Disease Detection. In: Medical and Biological Engineering and Computing. 2024: 1–15. https://doi.org/10.1007/s11517-024-03115-x

  61. Sharma A, Goyal D, Mohana R. An ensemble learning-based framework for breast cancer prediction. Decision Anal J. 2024;10(100372):1–15. https://doi.org/10.1016/j.dajour.2023.100372.

    Article  Google Scholar 

  62. Kacker RN, Lagergren ES, Filliben JJ. Taguch vs orthogonal arrays are classical designs of experiments. J Res Nat Inst Stand Technol. 1991;96(5):577–91. https://doi.org/10.6028/jres.096.034.

    Article  Google Scholar 

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Singh, A.P., Chaurasia, B.K. & Tripathi, A. Stacking Enabled Ensemble Learning Based Intrusion Detection Scheme (SELIDS) for IoV. SN COMPUT. SCI. 5, 1000 (2024). https://doi.org/10.1007/s42979-024-03376-1

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