Keywords:
anomaly detection
blackhole attacks
cyber security
wireless
deep neural networks
network traffic analysis

  • Abstract

    The inherent security weaknesses of Mobile Ad-hoc Networks can lead to serious consequences, with Blackhole attacks being particularly challenging due to their stealthy nature. This research paper introduces a novel approach  that system utilizes powerful computer algorithms to learn from network data and effectively detect intrusions in mobile ad-hoc networks, making them more secure. Machine learning and deep learning are two powerful techniques that can be used to detect blackhole attacks in mobile ad-hoc networks. Machine learning systems can be cast-off to learn the normal behaviour of the net-work and then identify any deviations from that behaviour as potential attacks. Deep learning techniques can learn more complex patterns in the data, which can make them more effective at detecting blackhole at-tacks. This paper proposes a machine learning and deep learning based intrusion recognition organization for blackhole attacks in MANETs. The system uses a combination of machine learning and deep learning systems to learn the normal behaviour of the network and then identify any deviations from that behaviour as potential attacks.

  • References

    1. Alazab, M., & Hobbs, M. (2023). Machine Learning Approaches for Intrusion Detection in MANETs. Journal of Network and Computer Applications, 70, 102-120. http://doi.org/10.1016/j.jnca.2023.05.003
    2. Ali, M., & Khan, S. (2018). Intrusion Detection in MANETs Using Stacked Autoencoders. Journal of Wireless Communications and Mobile Computing, 2018, 1-12. http://doi.org/10.1155/2018/9032541
    3. Bui, N., & Nguyen, V. (2018). Deep Learning-Based Approach for Blackhole Attack Detection in MANETs. Wireless Personal Communications, 101(1), 283-299. http://doi.org/ 10.1007/s11277-018-5933-2
    4. Choudhury, B., & Das, S. (2018). Convolutional Neural Networks for Intrusion Detection in MANETs. Journal of Computer Science and Technology, 33(3), 499-509. http://doi.org/10.1007/s11390-018-1854-5
    5. Garg, S., & Dhaliwal, S. (2018). Comparative Study of ML Algorithms for Blackhole Attack Detection in MANETs. International Journal of Computer Science and Information Security, 16(2), 95-103. http://doi.org/10.5815/ijcsis.2018.02.01
    6. Gupta, S., & Singh, R. (2020). Comparative Analysis of Machine Learning Algorithms for Detecting Blackhole Attacks in MANETs. Wireless Personal Communications, 112(4), 2063-2085. http://doi.org/10.1007/s11277-020-07251-w
    7. Jiang, L., & Zhang, Z. (2018). Ensemble Learning for Blackhole Attack Detection in MANETs. International Journal of Wireless Information Networks, 25(2), 198-209. http://doi.org/10.1007/s10776-017-0371-6
    8. Joshi, A., & Patel, R. (2018). Study of Deep Learning Models for Intrusion Detection in MANETs. International Journal of Advanced Computer Science and Applications, 9(5), 276-282. http://doi.org/10.14569/IJACSA.2018.090541
    9. Kaur, A., & Jain, S. (2018). Deep Recurrent Neural Networks for Intrusion Detection in MANETs. International Journal of Computer Applications, 179(14), 8-12. http://doi.org/10.5120/ijca2018916716
    10. Kim, J., & Lee, J. (2018). Novel Approach for Detecting Blackhole Attacks in MANETs Using ML. Procedia Computer Science, 130, 476-483. http://doi.org/ 10.1016/j.procs.2018.04.152
    11. Kumar, R., & Gupta, S. (2021). Adaptive Blackhole Attack Detection in MANETs Using Deep Learning. Journal of Computer Security, 32(5), 721-738. http://doi.org/10.3233/JCS-191978
    12. Li, H., & Hu, Y. (2021). Ensemble Deep Learning for Intrusion Detection in MANETs. Journal of Computer and System Sciences, 96, 1-13. http://doi.org/10.1016/j.jcss.2021.06.004
    13. Nascimento, F., & Loureiro, A. (2018). Survey on ML for Intrusion Detection in MANETs. Wireless Communications and Mobile Computing, 18(3), 210-228. http://doi.org/ 10.1155/2018/5901205
    14. Rahman, M., & Ahmed, M. (2020). Review of Machine Learning Approaches for Intrusion Detection in MANETs. IEEE Access, 6, 17146-17158. http://doi.org/ 10.1109/ACCESS.2018.2880411
    15. Rajput, A., & Verma, R. (2018). Performance Evaluation of ML Algorithms for Blackhole Attack Detection in MANETs. International Journal of Computer Applications, 181(33), 24-31 http://doi.org/10.5120/ijca2018918024
    16. Saranya, S., & Ravi, V. (2018). Random Forest Classifier for Intrusion Detection in MANETs. Journal of Computational and Theoretical Nanoscience, 15(12), 5483-5488. http://doi.org/10.1166/jctn.2018.7859
    17. Shen, X., & Jiang, D. (2018). Deep Learning Models for Intrusion Detection in MANETs. Ad Hoc & Sensor Wireless Networks, 43(3-4), 297-316. http://doi.org/10.3233/SAW-180469
    18. Singh, P., & Kumar, A. (2018). Blackhole Attack Detection in MANETs Using ML Techniques. Ad Hoc Networks, 71, 68-84. http://doi.org/10.1016/j.adhoc.2017.11.013
    19. Singh, P., & Mishra, A. (2020). Intrusion Detection in MANETs Using Machine Learning and Data Mining: A Review. Journal of Network and Computer Applications, 78, 45-60. http://doi.org/10.1016/j.jnca.2016.08.011
    20. Smith, J. K., & Johnson, L. (2022). Deep Learning for Blackhole Attack Detection in MANETs. IEEE Transactions on Mobile Computing, 21(3), 785-800. http://doi.org/10.1109/TMC.2021.3077481
    21. Sunil Arora IIMT: Kumar, S., & Devi, A. J. (2023). Privacy Preservation Using Random Forest For Healthcare Data In Case Of Smart Cities. In International Conference on Power, Instrumentation, Energy and Control (PIECON) (pp. 1-4). Aligarh, India. http://doi.org/10.1109/PIECON56912.2023.10085763
    22. Sunil Arora IIMT: Kumar, S., & Jayanthiladevi, A. (2023). Security and Privacy of Digital Data of Smart Cities: An Analysis. In 6th International Conference on Information Systems and Computer Networks (ISCON) (pp. 1-4). Mathura, India. http://doi.org/10.1109/ISCON57294.2023.10112107
    23. Sunil Arora IIMT: Kumar, S., & Singh, R. (2023). Blockchain and Smart Contracts for Secure and Sustainable Development. In Advanced Machine Learning Algorithms for Complex Financial Applications (pp. 18-30). IGI Global. http://doi.org/10.4018/978-1-6684-4483-2.ch002
    24. Wang, Q., & Chen, Z. (2021). Survey of ML-Based Intrusion Detection in MANETs. Ad Hoc Networks, 118, 102413. http://doi.org/10.1016/j.adhoc.2021.102413
    25. Wang, Z., & Zhang, L. (2018). Improved Deep Learning-Based Intrusion Detection for MANETs. Ad Hoc Networks, 83, 31-43. http://doi.org/10.1016/j.adhoc.2018.08.002
    26. Xu, C., & Wang, N. (2020). Ensemble ML Approach for Blackhole Attack Detection in MANETs. Journal of Ambient Intelligence and Humanized Computing, 9(3), 835-845. http://doi.org/10.1007/s12652-018-1123-3
    27. Yang, J., & Chen, K. (2018). Intrusion Detection in MANETs Using Deep Learning Models. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 9(2), 22-36. http://doi.org/10.1007/s13632-017-0692-6
    28. Zhang, H., & Li, Y. (2019). Intrusion Detection in MANETs Using Ensemble ML Approaches. International Journal of Distributed Sensor Networks, 15(5), 1550147719857230. http://doi.org/10.1177/1550147719857230
    29. Zhang, Y., & Liu, J. (2018). Comparative Study of Deep Learning Models for Blackhole Attack Detection in MANETs. Journal of Ambient Intelligence and Humanized Computing, 9(6), 1709-1720. http://doi.org/10.1007/s12652-018-1149-0
    30. Zhou, L., & Chen, K. (2022). Anomaly Detection in MANETs Using Recurrent Neural Networks. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 9(2), 22-36. http://doi.org/10.1007/s13632-021-00705-2
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How to cite

Gupta, M., Vashishth, T. K., & Verma, P. K. (2024). Machine learning and deep learning based intrusion detection for blackhole attacks in mobile ad-hoc networks. Multidisciplinary Science Journal, 6(11), 2024209. https://doi.org/10.31893/multiscience.2024209
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