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Original Article
Road surface Anomaly Detection using Edge and Deep Learning
Mohd. Jibran Parvez1
Dr. Khaja Mahabubullah2
1 Student, MCA Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Professor & HOD MCA Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: September-October 2025
Pages: 07-12
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250605002
References
1. W. Lee, S. J. Stolfo, and K. W. Mok, “A data mining framework for building intrusion detection models,” Proceedings of the IEEE Symposium on Security and Privacy, pp. 120–132, 1999.
2. M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), pp. 1–6, 2009.
3. S. Mukkamala, G. Janoski, and A. Sung, “Intrusion detection using neural networks and support vector machines,” Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 1702–1707, 2002.
4. M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, “Network anomaly detection: Methods, systems and tools,” IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 303–336, 2014.
5. K. Kim, “Anomaly detection using autoencoders for network security,” Applied Sciences, vol. 8, no. 6, pp. 1–16, 2018.
6. S. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A deep learning approach to network intrusion detection,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 1, pp. 41–50, 2018.
7. Y. Zhang, P. Patras, and H. Haddadi, “Deep learning in mobile and wireless networking: A survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2224–2287, 2019.
8. N. Gao, H. Wang, X. Yang, Y. Yang, X. Li, and Y. Xiang, “A survey of deep learning for network anomaly detection,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 120–144, 2019.
9. A. Javaid, Q. Niyaz, W. Sun, and M. Alam, “A deep learning approach for network intrusion detection system,” Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), pp. 21–26, 2016.
10. R. Vinayakumar, K. Soman, and P. Poornachandran, “Evaluating deep learning approaches to characterize and classify malicious network traffic,” Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1265–1276, 2018.
2. M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), pp. 1–6, 2009.
3. S. Mukkamala, G. Janoski, and A. Sung, “Intrusion detection using neural networks and support vector machines,” Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 1702–1707, 2002.
4. M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, “Network anomaly detection: Methods, systems and tools,” IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 303–336, 2014.
5. K. Kim, “Anomaly detection using autoencoders for network security,” Applied Sciences, vol. 8, no. 6, pp. 1–16, 2018.
6. S. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A deep learning approach to network intrusion detection,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 1, pp. 41–50, 2018.
7. Y. Zhang, P. Patras, and H. Haddadi, “Deep learning in mobile and wireless networking: A survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2224–2287, 2019.
8. N. Gao, H. Wang, X. Yang, Y. Yang, X. Li, and Y. Xiang, “A survey of deep learning for network anomaly detection,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 120–144, 2019.
9. A. Javaid, Q. Niyaz, W. Sun, and M. Alam, “A deep learning approach for network intrusion detection system,” Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), pp. 21–26, 2016.
10. R. Vinayakumar, K. Soman, and P. Poornachandran, “Evaluating deep learning approaches to characterize and classify malicious network traffic,” Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1265–1276, 2018.
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