Skip to main content
Log in

An enhanced detection system against routing attacks in mobile ad-hoc network

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Mobile ad-hoc network is a dynamic wireless network that transfers information through neighbor nodes with a temporary configuration. Due to its dynamic nature, it is exposed to attacks and intrusions. Routing disruption attack is the main problem of this network where intermediate nodes act maliciously. An encryption-based security mechanism is a first-line defense system that is efficient. It is still not compatible with the mobile ad-hoc network environment. Malicious nodes can drop encrypted data packets in this network. The lightweight technique analyzes a few parameters that consume few resources and provide comparatively low detection rates. However, an intrusion detection system is a reliable second-line security mechanism. In this paper, we have proposed a detection method that classifies malicious and benign information. The proposed intrusion detection method is based on learning techniques that initially require a dataset to determine mobile nodes’ behavior. Subsequently, we perform this work in an order such as mobile ad-hoc network simulation with some malicious nodes, features selection, and data collection using packet captured files. This work is executed through extensive simulations in the NS-3. The proposed method learns the system for information classification, and experimental results that show the proposed method performs better than existing schemes. Moreover, the obtained performance confirms that the suggested feature set is suitable for the intrusion detection system in mobile ad-hoc networks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Bouhaddi, M., Radjef, M. S., & Adi, K. (2018). An efficient intrusion detection in resource-constrained mobile ad-hoc networks. Computers and Security, 76, 156–177.

    Article  Google Scholar 

  2. Mitrokotsa, A., & Dimitrakakis, C. (2013). Intrusion detection in manet using classification algorithms: The effects of cost and model selection. Ad Hoc Networks, 11(1), 226–237.

    Article  Google Scholar 

  3. Feng, F., Liu, X., Yong, B., Zhou, R., & Zhou, Q. (2019). Anomaly detection in ad-hoc networks based on deep learning model: A plug and play device. Ad Hoc Networks, 84, 82–89.

    Article  Google Scholar 

  4. Papakostas, D., Eshghi, S., Katsaros, D., & Tassiulas, L. (2018). Energy-aware backbone formation in military multilayer ad hoc networks. Ad Hoc Networks, 81, 17–44.

    Article  Google Scholar 

  5. Akilarasu, G., & Shalinie, S. M. (2017). Wormhole-free routing and dos attack defense in wireless mesh networks. Wireless Networks, 23(6), 1709–1718.

    Article  Google Scholar 

  6. Alappatt, V., & Prathap, P. J. (2020). Hybrid cryptographic algorithm based key management scheme in manet. Materials Today: Proceedings

  7. Kumar, G., Rai, M. K., & Saha, R. (2017). Securing range free localization against wormhole attack using distance estimation and maximum likelihood estimation in wireless sensor networks. Journal of Network and Computer Applications, 99, 10–16.

    Article  Google Scholar 

  8. Prasad, M., Tripathi, S., & Dahal, K. (2019). Intrusion detection in ad hoc network using machine learning technique. In International Conference on Big Data, Machine Learning, and Applications, pages 60–71. Springer

  9. Prasad, M., Tripathi, S., & Dahal, K. (2019). Wormhole attack detection in ad hoc network using machine learning technique. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pages 1–7. IEEE

  10. Qazi, S., Raad, R., Mu, Y., & Susilo, W. (2018). Multirate delphi to secure multirate ad hoc networks against wormhole attacks. Journal of Information Security and Applications, 39, 31–40.

    Article  Google Scholar 

  11. Jamali, S., & Fotohi, R. (2017). Dawa: Defending against wormhole attack in manets by using fuzzy logic and artificial immune system. The Journal of Supercomputing, 73(12), 5173–5196.

    Article  Google Scholar 

  12. Wazid, M., & Das, A. K. (2017). A secure group-based blackhole node detection scheme for hierarchical wireless sensor networks. Wireless Personal Communications, 94(3), 1165–1191.

    Article  Google Scholar 

  13. Prasad, M., Tripathi, S., & Dahal, K. (2020). An efficient feature selection based bayesian and rough set approach for intrusion detection. Applied Soft Computing, 87, 105980.

    Article  Google Scholar 

  14. Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2020). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50, 102419.

    Article  Google Scholar 

  15. Masdari, M., & Khezri, H. (2020). A survey and taxonomy of the fuzzy signature-based intrusion detection systems. Applied Soft Computing, 92, 106301.

    Article  Google Scholar 

  16. Hammamouche, A., Omar, M., Djebari, N., & Tari, A. (2018). Lightweight reputation-based approach against simple and cooperative black-hole attacks for manet. Journal of Information Security and Applications, 43, 12–20.

    Article  Google Scholar 

  17. Pragya, M., Arya, K. V., & Pal, S. H. (2018). Intrusion detection system against colluding misbehavior in manets. Wireless Personal Communications, 100(2), 491–503.

    Article  Google Scholar 

  18. Panos, C., Ntantogian, C., Malliaros, S., & Xenakis, C. (2017). Analyzing, quantifying, and detecting the blackhole attack in infrastructure-less networks. Computer Networks, 113, 94–110.

    Article  Google Scholar 

  19. Geetha, S. B., & Patil, V. C. (2017). Graph-based energy supportive routing protocol to resist wormhole attack in mobile adhoc network. Wireless Personal Communications, 97(1), 859–880.

    Article  Google Scholar 

  20. Kaur, P., Kaur, D., & Mahajan, R. (2017). Wormhole attack detection technique in mobile ad hoc networks. Wireless Personal Communications, 97(2), 2939–2950.

    Article  Google Scholar 

  21. Sankara Narayanan, S., & Murugaboopathi, G. (2020). Modified secure aodv protocol to prevent wormhole attack in manet. Concurrency and Computation: Practice and Experience, 32(4), e5017.

    Article  Google Scholar 

  22. Arthur, M. P., & Kannan, K. (2016). Cross-layer based multiclass intrusion detection system for secure multicast communication of manet in military networks. Wireless Networks, 22(3), 1035–1059.

    Article  Google Scholar 

  23. Subba, B., Biswas, S., & Karmakar, S. (2016). Intrusion detection in mobile ad-hoc networks: Bayesian game formulation. An International Journal Engineering Science and Technology, 19(2), 782–799.

    Article  Google Scholar 

  24. Elwahsh, H., Gamal, M., Salama, A. A., & El-Henawy, I. M. (2018). A novel approach for classifying manets attacks with a neutrosophic intelligent system based on genetic algorithm. Security and Communication Networks

  25. Islabudeen, M., & Kavitha Devi, M. K. (2020). A smart approach for intrusion detection and prevention system in mobile ad hoc networks against security attacks. Wireless Personal Communications, 112(1), 193–224.

    Article  Google Scholar 

  26. Muralidharan, V., & Sugumaran, V. (2012). A comparative study of naïve bayes classifier and bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Applied Soft Computing, 12(8), 2023–2029.

    Article  Google Scholar 

  27. Li, H., Wang, Y., Xu, X., Qin, L., & Zhang, H. (2019). Short-term passenger flow prediction under passenger flow control using a dynamic radial basis function network. Applied Soft Computing, 83, 105620.

    Article  Google Scholar 

  28. Masih, N., Naz, H., & Ahuja, S. (2021). Multilayer perceptron based deep neural network for early detection of coronary heart disease. Health and Technology, 11(1), 127–138.

    Article  Google Scholar 

  29. Nishani, L., & Biba, M. (2016). Machine learning for intrusion detection in manet: A state-of-the-art survey. Journal of Intelligent Information Systems, 46(2), 391–407.

    Article  Google Scholar 

  30. Murugan, A., Nair, S. A. H., & Kumar, K. P. (2019). Detection of skin cancer using svm, random forest and knn classifiers. Journal of Medical Systems, 43(8), 269.

    Article  Google Scholar 

  31. Mohanapriya, M., & Krishnamurthi, I. (2014). Modified dsr protocol for detection and removal of selective black hole attack in manet. Computers and Electrical Engineering, 40(2), 530–538.

    Article  Google Scholar 

  32. Prasad, M., Tripathi, S., & Dahal, K. (2020). Unsupervised feature selection and cluster center initialization based arbitrary shaped clusters for intrusion detection. Computers and Security, 99, 102062.

    Article  Google Scholar 

  33. Singh, R., Kumar, H., & Singla, R. K. (2015). An intrusion detection system using network traffic profiling and online sequential extreme learning machine. Expert Systems with Applications, 42(22), 8609–8624.

    Article  Google Scholar 

  34. Tomar, A., Nitesh, K., & Jana, P. K. (2020). An efficient scheme for trajectory design of mobile chargers in wireless sensor networks. Wireless Networks, 26(2), 897–912.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mahendra Prasad.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prasad, M., Tripathi, S. & Dahal, K. An enhanced detection system against routing attacks in mobile ad-hoc network. Wireless Netw 28, 1411–1428 (2022). https://doi.org/10.1007/s11276-022-02913-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-022-02913-1

Keywords

Navigation