An improved man-in-the-middle (MITM) attack detections using convolutional neural networks

Mohammed Iddrisu; Kate Takyi; Rose-Mary Owusuaa Mensah Gyening; Kwame Ofosuhene Peasah; Linda Amoako Banning; Kwabena Owusu-Agyemang

doi:10.31893/multiscience.2025129

Keywords:

Abstract

The increasing reliance on digital communication networks has made information security a critical concern for individuals, organizations, and governments worldwide. Man-in-the-middle (MITM) attacks are significant, prevalent, and damaging concerning cyber-attacks. Detecting MitM attacks is complex due to their stealthy nature and the sophisticated methods employed by attackers. There is the need for researchers to address this issue using current and novel methods like artificial intelligence. In this paper, an improved MitM attack detection approach using the Convolutional Neural Network (CNN) deep learning algorithm is developed, resulting in an overall detection accuracy of 0.986%. The results confirms that the proposed model is very efficient in comparision to other proposed solutions by other authors.
References
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Iddrisu, M., Takyi, K., Owusuaa Mensah Gyening, R.-M., Ofosuhene Peasah, K., Amoako Banning, L., & Owusu-Agyemang, K. (2024). An improved man-in-the-middle (MITM) attack detections using convolutional neural networks. Multidisciplinary Science Journal, 7(3), 2025129. https://doi.org/10.31893/multiscience.2025129

[1] Ahmad, F., Kurugollu, F., Adnane, A., Hussain, R., & Hussain, F. (2020). MARINE: Man-in-the-Middle Attack Resistant Trust Model in Connected Vehicles. IEEE Internet of Things Journal, 7(4), 3310–3322.https://doi.org/10.1109/JIOT.2020.2967568

[2] Al-Juboori, S. A. M., Hazzaa, F., Jabbar, Z. S., Salih, S., & Gheni, H. M. (2023). Man-in-the-middle and denial of service attacks detection using machine learning algorithms. Bulletin of Electrical Engineering and Informatics, 12(1), 418-426. DOI: https://doi.org/10.11591/eei.v12i1.4555

[3] Chen, H., Chen, Y., & Summerville, D. H. (2011). A survey on the application of FPGAs for network infrastructure security. In IEEE Communications Surveys and Tutorials (p. 541–561). IEEE, 2011. https://doi.org/10.1109/SURV.2011.072210.00075

[4] Disha, R. A., & Waheed, S. (2022). Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique. Cybersecurity, 5(1),https://doi.org/10.1186/s42400-021-00103-8

[5] Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020). Machine Learning in IoT Security: Current Solutions and Future Challenges. IEEE Communications Surveys and Tutorials, 22(3), 1686–1721. https://doi.org/10.1109/COMST.2020.2986444

[6] Jemili, F., & Bouras, H. (2021). Intrusion detection based on big data fuzzy analytics. In Open Data. IntechOpen. DOI: 10.5772/intechopen.99636

[7] Lairab, B. I., & Azer, M. A. (2021). A Distributed Intrusion Detection System for Ad Hoc Networks. In Proceedings of the 2021 16th International Conference on Computer Engineering and Systems (ICCES) (p.1-4). IEEE, 2021.https://doi.org/10.1109/ICCES54031.2021.9686166

[8] Lan, H., Zhu, X., Sun, J., & Li, S. (2020). Traffic Data Classification to Detect Man-in-the-Middle Attacks in Industrial Control System. In Proceedings of 2019 6th International Conference on Dependable Systems and Their Applications (DSA 2019), (p. 430–434). https://doi.org/10.1109/DSA.2019.00067

[9] Ma, T., Xu, C., Yang, S., Huang, Y., An, Q., Kuang, X., & Grieco, L. A. (2023). A Mutation-Enabled Proactive Defense Against Service-Oriented Man-in-The-Middle Attack in Kubernetes. IEEE Transactions on Computers, 1–14. https://doi.org/10.1109/tc.2023.3238125

[10] Mirsky, Y., Kalbo, N., Elovici, Y., & Shabtai, A. (2018). Vesper: Using echo analysis to detect man-in-the-middle attacks in LANs. IEEE Transactions on Information Forensics and Security, 14(6), 1638-1653.

[11] Saed, M., & Aljuhani, A. (2022, January). Detection of man in the middle attack using machine learning. In 2022 2nd International Conference on Computing and Information Technology (ICCIT) (p. 388-393). IEEE, 2022.

[12] https://doi.org/10.1109/ICCIT52419.2022.9711555

[13] Saheed, Y. K., & Arowolo, M. O. (2021). Efficient cyber-attack detection on the internet of medical things-smart environment based on deep recurrent neural network and machine learning algorithms. IEEE Access, 9, 161546-161554.

[14] https://doi.org/10.1109/ACCESS.2021.3128837

[15] Sieh, W.-B., Leu, J.-S., & Takada, J.-I. (2022). Use chains to block DNS attacks: A trusty blockchain-based domain name system. Journal of Communications and Networks, 24(3), 347–356.https://doi.org/10.23919/jcn.2022.000009

[16] Sultan, A. B. M., Mehmood, S., & Zahid, H. (2022, March). Man in the Middle Attack Detection for MQTT based IoT devices using different Machine Learning Algorithms. In 2022 2nd International Conference on Artificial Intelligence (ICAI) (p. 118-121). IEEE, 2022.https://doi.org/10.1109/ICAI55435.2022.9773590

[17] Takyi, K., Bagga, A., & Goopta, P. (2018, August). Clustering techniques for traffic classification: A comprehensive review. In 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO) (pp. 224-230). IEEE. DOI: 10.1109/ICRITO.2018.8748772

[18] Zahara, L., Musa, P., Wibowo, E. P., Karim, I., & Musa, S. B. (2020). The facial emotion recognition (FER-2013) dataset for prediction system of micro-expressions face using the convolutional neural network (CNN) algorithm based Raspberry Pi. In 2020 Fifth international conference on informatics and computing (ICIC) (p. 1-9). IEEE. DOI: 10.1109/ICIC50835.2020.9288560

Abstract

References

How to cite