A Review on Machine Learning and Deep Learning Based Modern Intrusion Detection Techniques for Mobile AdHoc Networks


Authors : P. Sreenivasulu; Dr. M. Ussenaiah

Volume/Issue : Volume 10 - 2025, Issue 11 - November


Google Scholar : https://tinyurl.com/yb55f8cp

Scribd : https://tinyurl.com/26ennbff

DOI : https://doi.org/10.38124/ijisrt/25nov173

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.


Abstract : Intrusion detection is very essential to secure any network communication. Different intrusion detection methods for MANET using Machine Learning techniques are reviewed. The main goal is to present the progress developments in intrusion detection techniques from traditional methods to the recent advanced Machine learning techniques. Usage of Machine leaning approach in intrusion detection is significantly increasing in recent years. Review on various Machine leaning techniques, types of intrusion, role of classification methods and data sets used are presented in this paper.

Keywords : IDS, Attacks, Machine Learning, Classfication, Feature Selection, Data Sets.

References :

  1. Ali Basem, Edris Khezri, Sadegh Sarhani Moghadam Pooyan Azizi doost & Mohammad Trik-(2025)“A new intrusion detection method using ensemble classification and feature selection”-ScientificReports-| https://doi.org/10.1038/s41598-025-98604-w
  2. Malhotra, H., & Sharma, P. (2019). Intrusion Detection using Machine Learning and Feature Selection. International Journal of Computer Network & Information Security, 11(4).
  3. Belavagi, M. C., & Muniyal, B. (2016). Performance evaluation of supervised machine learning algorithms for intrusion detection. Procedia Computer Science, 89, 117-123.
  4. Taher, K. A., Jisan, B. M. Y., & Rahman, M. M. (2019, January). Network intrusion detection using supervised machine learning technique with feature selection. In 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) (pp. 643-646). IEEE.
  5. El Mourabit, Y., Bouirden, A., Toumanari, A., & Moussaid, N. E. (2015). Intrusion detection techniques in wireless sensor network using data mining algorithms: comparative evaluation based on attacks detection. International Journal of Advanced Computer Science and Applications, 6(9), 164-172.
  6. Li, Y., Xia, J., Zhang, S., Yan, J., Ai, X., & Dai, K. (2012). An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert systems with applications, 39(1), 424-430.
  7. Shah, B., & Trivedi, B. H. (2015, February). Reducing features of KDD CUP 1999 dataset for anomaly detection using back propagation neural network. In 2015 Fifth International Conference on Advanced Computing & Communication Technologies (pp. 247-251). IEEE.
  8. Yulianto, A., Sukarno, P., & Suwastika, N. A. (2019, March). Improving Adaboost-based intrusion detection system (IDS) performance on CIC IDS 2017 dataset. In Journal of Physics: Conference Series (Vol. 1192, No. 1, p. 012018). IOP Publishing.
  9. Abdulhammed, R., Faezipour, M., Musafer, H., & Abuzneid, A. (2019, June). Efficient network intrusion detection using pca-based dimensionality reduction of features. In 2019 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE.
  10. Pelletier, Z., & Abualkibash, M. (2020). Evaluating the CIC IDS-2017 Dataset Using Machine Learning Methods and Creating Multiple Predictive Models in the Statistical Computing Language R. Science, 5(2), 187-191.
  11. Hammad, M., El-medany, W., & Ismail, Y. (2020, December). Intrusion Detection System using Feature Selection With Clustering and Classification Machine Learning Algorithms on the UNSW-NB15 dataset. In 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT) (pp. 1-6). IEEE.
  12. Faker, O., & Dogdu, E. (2019, April). Intrusion detection using big data and deep learning techniques. In Proceedings of the 2019 ACM Southeast Conference (pp. 86-93).
  13. Sara A1-Emadi, Aisha A1-Mohannadi,Felwa A1-Senaid(2020).Using Deep Learning Techniques for Network Intrusion Detection-IEEE.
  14. Pratima Sree Muhuri,Prosenjit Chatterjee,Xiaohong,Kaushik Roy and Albert Esterline(2020,March) .Using a Long Short Memory Recurrent Neural Network to Classify Network Attacks.
  15. Asmaa Halbouni,Teddy Surya Gunawan,et.al(2022 August). CNN-LSTM:Hybrid Deep Neural Netork for Network Intrusion Detection System.-IEE Access,volume 10,2022.
  16. Daniela Pinto, Ivone Amorim , Eva Maia, Isabel Praça (2025,May) A review on intrusion detection datasets: tools, processes, and features-Volume 262, May 2025, 111177-computer networks, Elsevier.
  17. Emad E. Abdallah*, Wafa’ Eleisah, Ahmed Fawzi Otoom(2022) Procedia Computer Science 201 (2022) 205–212.
  18. https://www.ibm.com/think/topics/intrusion-detection-system.
  19. VinayKumar R,Soman KP and Prabaharan Poornachandran.”Applying Convolutional Neural Network for Network Intrusion Detection” .IEEE Xplore  December 2017.
  20. https://securityjournaluk.com/intrusion-detection/.
  21. Julian Jang-JaccardSurya Nepal-“ A survey of emerging threats in cybersecurity”. Journal of Computer and System Sciences 80 (2014) 973–993.
  22. Md. Alamin Talukder, Md. Manowarul Islam,etal-“Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction.”- Talukder et al. Journal of Big Data-Journal of Big Data(2024).11-33.
  23. Ankit Thakkar, Ritika Lohiya – “A Review of the Advancement in Intrusion Detection Datasets”.Science Direct- Procedia Computer Science 167 (2020) 636–645.

Intrusion detection is very essential to secure any network communication. Different intrusion detection methods for MANET using Machine Learning techniques are reviewed. The main goal is to present the progress developments in intrusion detection techniques from traditional methods to the recent advanced Machine learning techniques. Usage of Machine leaning approach in intrusion detection is significantly increasing in recent years. Review on various Machine leaning techniques, types of intrusion, role of classification methods and data sets used are presented in this paper.

Keywords : IDS, Attacks, Machine Learning, Classfication, Feature Selection, Data Sets.

CALL FOR PAPERS


Paper Submission Last Date
30 - November - 2025

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe