A Review of AI-Based Intrusion Detection Systems for Mobile Ad Hoc Networks (MANETs)

Year : 2026 | Volume : 13 | 03 | Page :
    By

    Deepa P. Vaidya,

  • Shrinivas P. Deshpande,

  1. Assistant Professor, Department of Computer Science, Degree College of Physical Education, Shree H. V. P. Mandal, Amravati, Maharashtra, India
  2. Professor, Principal, Department of Computer Science, Degree College of Physical Education, Shree H. V. P. Mandal, Amravati, Maharashtra, India

Abstract

Mobile Ad Hoc Networks (MANETs) comprise wireless networks that lack any conventional infrastructure . Their chief features include highly changing network topologies, lack of centralized administration, and open nature of communication, which collectively result in making MANETs of the wireless kind very susceptible to a diverse range of cyber-attacks like blackhole, greyhole, wormhole, flooding, Sybil and denial-of-service (DoS) among others. Conventionally, Intrusion Detection Systems (IDS) relying on static rule-based methods could hardly meet the requirements to handle continuously evolving attack patterns and changing network behaviors . Artificial Intelligence (AI) techniques, particularly Machine Learning (ML), Deep Learning (DL), and Federated Learning (FL), are increasingly being considered effective approaches for improving intrusion detection in MANET environments because of their ability to learn from data, identify complex patterns, and adapt to evolving attack behaviours .

This paper presents a concise review of AI-based intrusion detection methods for MANETs by classifying existing approaches into signature-based, anomaly-based, and hybrid techniques. It discusses machine learning algorithms such as Support Vector Machine (SVM), Random Forest, and clustering methods, along with deep learning models including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). It also highlights the growing importance of federated learning and hybrid intrusion detection systems as emerging research directions. Furthermore, it compares representative approaches based on detection capability, computational efficiency, strengths, and limitations, while identifying key research challenges and future directions such as explainable AI, lightweight deployment, real-time intrusion monitoring, and multi-layer IDS architectures. The review serves as a useful reference for researchers and practitioners working toward intelligent and secure MANET environments.

Keywords: MANET, Intrusion Detection System, Machine Learning, Deep Learning, Network Security, Artificial Intelligence

How to cite this article:
Deepa P. Vaidya, Shrinivas P. Deshpande. A Review of AI-Based Intrusion Detection Systems for Mobile Ad Hoc Networks (MANETs). Journal of Artificial Intelligence Research & Advances. 2026; 13(03):-.
How to cite this URL:
Deepa P. Vaidya, Shrinivas P. Deshpande. A Review of AI-Based Intrusion Detection Systems for Mobile Ad Hoc Networks (MANETs). Journal of Artificial Intelligence Research & Advances. 2026; 13(03):-. Available from: https://journals.stmjournals.com/joaira/article=2026/view=250290


References

  1. Saad M. Hassan, Mohd Murtadha Mohamad, and Farkhana Binti Muchtar, Advanced Intrusion Detection in MANETs: A Survey of Machine Learning and Optimization Techniques for Mitigating Black/Gray Hole Attacks, IEEE Access, vol. 12, pp. 150046–150090, 2024, doi: 10.1109/ACCESS.2024.3457682.
  2. S. Laqtib, K. El Yassini, and M. L. Hasnaoui, A Technical Review and Comparative Analysis of Machine Learning Techniques for Intrusion Detection Systems in MANET, International Journal of Electrical and Computer Engineering (IJECE), vol. 10, no. 3, pp. 2701–2709, 2020.
  3. L. Nishani and M. Biba, Machine learning for intrusion detection in MANET: A state-of-the-art survey, J. Intell. Inf. Syst., vol. 46, no. 2, pp. 391-407, 2016.
  4. A. Abdelhamid, M. S. Elsayed, A. D. Jurcut, and M. A. Azer, A Lightweight Anomaly Detection System for Black Hole Attack, Electronics, vol. 12, no. 6, Art. no. 1294, 2023.
  5. H. Moudni, M. Er-Rouidi, H. Mouncif, and B. El Hadadi, Black Hole Attack Detection using Fuzzy based Intrusion Detection Systems in MANET, Procedia Computer Science, vol. 151, pp. 1176–1181, 2019.
  6. M. Alikhany and M. Abadi, A Dynamic Clustering-Based Approach for Anomaly Detection in AODV-Based MANETs, Proceedings of the International Symposium on Computer Networks and Distributed Systems (CNDS), Tehran, Iran, February 23–24, 2011.
  7. A. Yasin and M. Abu Zant, Detecting and Isolating Black-Hole Attacks in MANET Using Timer Based Baited Technique, Wireless Communications and Mobile Computing, Volume 2018, Article ID 9812135, 10 pages, 2018.
  8. Paulo Angelo Alves Resende and André Costa Drummond, A Survey of Random Forest Based Methods for Intrusion Detection Systems, ACM Computing Surveys, Volume 51, Issue 3, Article 48, Pages 1–36, May 2018.
  9. N. Farnaaz and M. A. Jabbar, Random forest modeling for network intrusion detection system, Procedia Comput. Sci., vol. 89, pp. 213-217, 2016.
  10. M. Choubisa, R. Doshi, Narendra Khatri, and Kamal Kant Hiran, A Simple and Robust Approach of Random Forest for Intrusion Detection System in Cyber Security, Proceedings of the 2022 International Conference on IoT and Blockchain Technology (ICIBT), Ranchi, India, 2022, pp. 1–5.
  11. J. Zhang, M. Zulkernine, and A. Haque, Random-Forests-based network intrusion detection systems, IEEE Trans. Syst., Man, Cybern. C, vol. 38, no. 5, pp. 649-659, 2008.
  12. S. R. Waskle, L. Parashar, and V. Singh, Intrusion detection system using PCA with Random Forest approach, in Proc. Int. Conf. Comput. Commun. Autom. (ICCCA), pp. 1065-1070, 2017.
  13. T. Poongothai and K. Duraiswamy, Cross Layer Intrusion Detection System of Mobile Ad Hoc Networks Using Feature Selection Approach, WSEAS Transactions on Communications, Volume 13, Pages 71–79, 2014.
  14. F. Barani and S. Gerami, ManetSVM: Dynamic Anomaly Detection Using One-Class Support Vector Machine in MANETs, Proceedings of the 10th International ISC Conference on Information Security and Cryptology (ISCISC), 2013, Pages 1–6.
  15. V. Justin, N. Marathe, and N. Dongre, Hybrid IDS Using SVM Classifier for Detecting DoS Attack in MANET Application, Proceedings of the International Conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC), Palladam, India, 2017, Pages 775–778.
  16. S. Ghosh, A. Banerjee, A. Sufian, Sachin Kumar Gupta, S. H. Alsamhi, and Abdu Saif, Efficient Selfish Node Detection Using SVM in IoT-MANET Environment, Transactions on Emerging Telecommunications Technologies, Volume 34, Issue 12, Article e4858, 2023.
  17. M. Patel and Sanjay Sharma, Detection of Malicious Attack in MANET: A Behavioral Approach, Proceedings of the 2013 IEEE 3rd International Advance Computing Conference (IACC), Ghaziabad, India, February 22–23, 2013, Pages 388–393.
  18. D. Djenouri and N. Badache, Struggling against Selfishness and Black Hole Attacks in MANETs, Wireless Communications and Mobile Computing, Volume 8, Issue 6, Pages 689–704, July 2008.
  19. Safaa Laqtib, Khalid El Yassini, and Moulay Lahcen Hasnaoui, Evaluation of Deep Learning Approaches for Intrusion Detection System in MANET, in Innovations in Smart Cities Applications Edition 3, Springer, 2020, Pages 986–998.
  20. Zainab Ali Abbood, Doğu Çağdaş Atilla, and Çağatay Aydin, Intrusion Detection System through Deep Learning in Routing MANET Networks, Intelligent Automation & Soft Computing, Volume 37, Issue 1, Pages 269–281, 2023.
  21. O. Sbai and M. El. boukhari, Data Flooding Intrusion Detection System for MANETs Using Deep Learning Approach, Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications (SITA 2020), November 2020, Pages 1–5.
  22. FH Tseng, HP Chiang, and HC Chao, Black Hole along with Other Attacks in MANETs: A Survey, Journal of Information Processing Systems, Volume 14, Issue 1, Pages 56–78, 2018.
  23. Ahmed M. Eltahlawy, Heba K. Aslan, Eslam G. Abdallah, Mahmoud Said Elsayed, Anca D. Jurcut, and Marianne A. Azer, A Survey on Parameters Affecting MANET Performance, Electronics, Volume 12, Issue 9, Article 1956, 2023
  24. K. S. Nirmala Bai and M. V. Subramanyam, Integrated Intrusion Detection Design with Discretion of Leading Agent Using Machine Learning for Efficient MANET System, Scientific Reports, Volume 15, Article 30849, 2025.
  25. Hwanseok Yang, Adaptive Clustering and Machine-Learning-Based DoS Intrusion Detection in MANETs, Applied Sciences, Volume 16, Issue 6, Article 2723, 2026.
  26. Ashraf Abdelhamid Abdallah, Mahmoud S. El Sayed Abdallah, Heba Aslan, Marianne A. Azer, Young-Im Cho, and Mohamed S. Abdallah, Enhancing Mobile Ad Hoc Network Security: An Anomaly Detection Approach Using Support Vector Machine for Black-Hole Attack Detection, International Journal of Safety and Security Engineering, Volume 14, Issue 4, Pages 1015–1028, 2024.
  27. Y. Alotaibi and M. Ilyas, “Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things’ Devices Security”, Sensors, Volume 23, Issue 12, Article 5568, 2023.
  28. S. Muruganandam, N. Srinivasan, and Anantha Sivaprakasam, “An Intelligent Method for Intrusion Detection and Prevention in Mobile AdHoc Networks”, International Journal of Intelligent Systems and Applications in Engineering, Volume 10, Issue 3, Pages 154–160, 2022.
  29. M. Sahaya Sheela, A. Gnana Soundari, Aditya Mudigonda, C. Kalpana, and co-authors, “Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security”, Intelligent and Converged Networks, Volume 5, Issue 1, Pages 1–18, March 2024.
  30. Shaik Shafi, S. Mounika, and S. Velliangiri, “Machine Learning and Trust Based AODV Routing Protocol to Mitigate Flooding and Blackhole Attacks in MANET”, Procedia Computer Science, Volume 218, Pages 2309–2318, 2023.
  31. S. Hemalatha, K. V. S. V. Trinadh Reddy, Ramaswamy T., R. V. V. Krishna, P. Supriya, and S. N. Ananthi, “Enhancing MANET Security Using AI-Driven Intrusion Detection Systems”, Journal of Communications, Volume 20, Number 4, Pages 371–383, 2025.
  32. Zainab Bashar Ibrahim and Mayada Faris Ghanim, “A Review of AI-Based Approaches against Wormhole and Blackhole Attacks in AODV Protocol”, International Journal of Advanced Natural Sciences and Engineering Researches, Volume 8, Pages 60–75, 2024.
  33. A. Hozouri, A. Mirzaei, and M. Effatparvar, “A Comprehensive Survey on Intrusion Detection Systems with Advances in Machine Learning, Deep Learning and Emerging Cybersecurity Challenges”, Discover Artificial Intelligence, Volume 5, Article 314, 2025.
  34. Jan Lansky, S. Ali, M. Mohammadi, Mohammed Kamal Majeed, Sarkhel H. Taher Karim, Shima Rashidi, Mehdi Hosseinzadeh, and Amir Masoud Rahmani, “Deep Learning-Based Intrusion Detection Systems: A Systematic Review”, IEEE Access, Volume 9, Pages 101574–101599, 2021.
  35. R. Kimanzi, P. Kimanga, D. Cherori, and Patrick K. Gikunda, “Deep Learning Algorithms Used in Intrusion Detection Systems – A Review”, arXiv preprint arXiv:2402.17020, February 2024.
  36. Saad M. Hassan, Mohd Murtadha Mohamad, Farkhana Binti Muchtar, and Firoz Bin Yusuf Patel Dawoodi, “Enhancing MANET Security Through Federated Learning and Multiobjective Optimization: A Trust-aware Routing Framework”, IEEE Access, Volume 12, Pages 181149–181178, 2024.
  37. S. Faizal Mukthar Hussain and S. M. H. Sithi Shameem Fathima, “Federated Learning-Assisted Coati Deep Learning-Based Model for Intrusion Detection in MANET”, International Journal of Computational Intelligence Systems, Volume 17, Article 285, 2024.
  38. M. Arya, H. Sastry, B. K. Dewangan, Mohammad Khalid Imam Rahmani, S. Bhatia, Abdul Wahab Muzaffar, and Mariyam Aysha Bivi, “Intruder Detection in VANET Data Streams Using Federated Learning for Smart City Environments”, Electronics, Volume 12, Issue 4, Article 894, 2023.
  39. M. M. Rashid, S. U.  Khan, F. Eusufzai, Md. Azharuddin Redwan, Saifur Rahman Sabuj, and Mahmoud Elsharief, “A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks”, Network, Volume 3, Issue 1, Pages 158–179, 2023.
  40. X. Chen, W. Qiu, L. Chen, Y. Ma, and J. Ma, “Fast and Practical Intrusion Detection System Based on Federated Learning for VANET”, Computers & Security, Volume 142, Article 103881, July 2024.
  41. D. C. Attota, V. Mothukuri, R. M. Parizi, Seyedamin Pouriyeh, Gautam Srivastava, and Ali Dehghantanha, “An Ensemble Multi-View Federated Learning Intrusion Detection for IoT”, IEEE Access, Volume 9, Pages 117734–117745, 2021.
  42. V. Chandola, A. Banerjee, and V. Kumar, “Anomaly Detection: A Survey”, ACM Computing Surveys, Volume 41, Issue 3, Article 15, 2009. DOI: 10.1145/1541880.1541882.
  43. H. Deng, Qing-An Zeng, and D. P. Agrawal, “SVM-Based Intrusion Detection System for Wireless Ad Hoc Networks”, Proceedings of the IEEE 58th Vehicular Technology Conference (VTC 2003-Fall), Orlando, Florida, USA, Volume 3, Pages 2147–2151, October 2003.
  44. A. S. Raj and S. Swamynathan, “A Survey of Intrusion Detection Systems in Mobile Ad Hoc Networks,” Wireless Personal Communications, vol. 109, no. 4, pp. 2521–2548, 2019.
  45. Y. Zhang and W. Lee, “Intrusion Detection in Wireless Ad-Hoc Networks”, in Proceedings of the 6th Annual ACM International Conference on Mobile Computing and Networking (MobiCom ’00), Boston, MA, USA, Aug. 2000, pp. 275–283, doi: 10.1145/345910.345958.
  46. S. Marti, T. J. Giuli, K. Lai, and M. Baker, “Mitigating Routing Misbehaviour in Mobile Ad Hoc Networks”, in Proceedings of the 6th Annual ACM International Conference on Mobile Computing and Networking (MobiCom ’00), Boston, MA, USA, Aug. 2000, pp. 255–265, doi: 10.1145/345910.345955.
Ahead of Print Subscription Original Research
Volume 13
03
Received 08/07/2026
Accepted 11/07/2026
Published 20/07/2026
Publication Time 12 Days
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