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Reseach Article

Enhanced Network Anomaly Detection using Convolutional Neural Networks in Cybersecurity Operations

by Khaled Bin Showkot Tanim, Mahadi Hasam Parash, MD Shadman Soumik, Mohammed Shakib
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 50
Year of Publication: 2024
Authors: Khaled Bin Showkot Tanim, Mahadi Hasam Parash, MD Shadman Soumik, Mohammed Shakib
10.5120/ijca2024924224

Khaled Bin Showkot Tanim, Mahadi Hasam Parash, MD Shadman Soumik, Mohammed Shakib . Enhanced Network Anomaly Detection using Convolutional Neural Networks in Cybersecurity Operations. International Journal of Computer Applications. 186, 50 ( Nov 2024), 13-25. DOI=10.5120/ijca2024924224

@article{ 10.5120/ijca2024924224,
author = { Khaled Bin Showkot Tanim, Mahadi Hasam Parash, MD Shadman Soumik, Mohammed Shakib },
title = { Enhanced Network Anomaly Detection using Convolutional Neural Networks in Cybersecurity Operations },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2024 },
volume = { 186 },
number = { 50 },
month = { Nov },
year = { 2024 },
issn = { 0975-8887 },
pages = { 13-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number50/enhanced-network-anomaly-detection-using-convolutional-neural-networks-in-cybersecurity-operations/ },
doi = { 10.5120/ijca2024924224 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-11-27T00:39:39.477370+05:30
%A Khaled Bin Showkot Tanim
%A Mahadi Hasam Parash
%A MD Shadman Soumik
%A Mohammed Shakib
%T Enhanced Network Anomaly Detection using Convolutional Neural Networks in Cybersecurity Operations
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 50
%P 13-25
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Network anomaly detection is critical for preserving cybersecurity and safeguarding sensitive data. Traditional approaches sometimes struggle with the complexity and amount of current network traffic. This research provides an upgraded network anomaly detection method utilizing convolutional neural networks (CNNs). Leveraging the BoT-IoT dataset, this paper utilize feature selection strategies based on entropy and correlation to develop a robust CNN feature matrix. The model showed considerable gains in identifying abnormalities, with a high accuracy rate of 96%. The application of the system in both offline and online modes illustrates its relevance in real-world cybersecurity operations. Detailed assessments, including training and testing timeframes, indicate the system's efficiency and efficacy. Future work will concentrate on increasing the dataset, incorporating additional deep learning models, and boosting real-time detection capabilities.

References
  1. Kwon, D., Natarajan, K., Suh, S. C., Kim, H., & Kim, J. (2018, July). An empirical study on network anomaly detection using convolutional neural networks. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) (pp. 1595-1598). IEEE.. (references)
  2. Alabadi, M., & Celik, Y. (2020, June). Anomaly detection for cyber-security based on convolution neural network: A survey. In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-14). IEEE.
  3. Al-Turaiki, I., & Altwaijry, N. (2021). A convolutional neural network for improved anomaly-based network intrusion detection. Big Data, 9(3), 233-252.
  4. Kravchik, M., & Shabtai, A. (2018, January). Detecting cyber-attacks in industrial control systems using convolutional neural networks. In Proceedings of the 2018 workshop on cyber-physical systems security and privacy (pp. 72-83).
  5. Khan, A. S., Ahmad, Z., Abdullah, J., & Ahmad, F. (2021). A spectrogram image-based network anomaly detection system using deep convolutional neural network. IEEE access, 9, 87079-87093.
  6. Radford, B. J., Apolonio, L. M., Trias, A. J., & Simpson, J. A. (2018). Network traffic anomaly detection using recurrent neural networks. arXiv preprint arXiv:1803.10769.
  7. Lai, Y., Zhang, J., & Liu, Z. (2019). Industrial anomaly detection and attack classification method based on convolutional neural network. Security and Communication Networks, 2019, 1-11.
  8. Moustafa, N., Hu, J., & Slay, J. (2019). A holistic review of network anomaly detection systems: A comprehensive survey. Journal of Network and Computer Applications, 128, 33-55.
  9. Fernandes, G., Rodrigues, J. J., Carvalho, L. F., Al-Muhtadi, J. F., & Proença, M. L. (2019). A comprehensive survey on network anomaly detection. Telecommunication Systems, 70, 447-489.
  10. Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021). Machine learning for anomaly detection: A systematic review. Ieee Access, 9, 78658-78700.
  11. Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2013). Network anomaly detection: methods, systems and tools. Ieee communications surveys & tutorials, 16(1), 303-336.
  12. Yang, Z., Liu, X., Li, T., Wu, D., Wang, J., Zhao, Y., & Han, H. (2022). A systematic literature review of methods and datasets for anomaly-based network intrusion detection. Computers & Security, 116, 102675.
  13. Patcha, A., & Park, J. M. (2007). An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer networks, 51(12), 3448-3470.
  14. Erhan, L., Ndubuaku, M., Di Mauro, M., Song, W., Chen, M., Fortino, G., ... & Liotta, A. (2021). Smart anomaly detection in sensor systems: A multi-perspective review. Information Fusion, 67, 64-79.
  15. Ali, W. A., Manasa, K. N., Bendechache, M., Fadhel Aljunaid, M., & Sandhya, P. (2020). A review of current machine learning approaches for anomaly detection in network traffic. Journal of Telecommunications and the Digital Economy, 8(4), 64-95.
  16. Bodström, T., & Hämäläinen, T. (2018). State of the art literature review on network anomaly detection with deep learning. In Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 18th International Conference, NEW2AN 2018, and 11th Conference, ruSMART 2018, St. Petersburg, Russia, August 27–29, 2018, Proceedings 18 (pp. 64-76). Springer International Publishing.
  17. Haji, S. H., & Ameen, S. Y. (2021). Attack and anomaly detection in iot networks using machine learning techniques: A review. Asian J. Res. Comput. Sci, 9(2), 30-46.
  18. Fahim, M., & Sillitti, A. (2019). Anomaly detection, analysis and prediction techniques in iot environment: A systematic literature review. IEEE Access, 7, 81664-81681.
  19. Ford, V., & Siraj, A. (2014, October). Applications of machine learning in cyber security. In Proceedings of the 27th international conference on computer applications in industry and engineering (Vol. 118). Kota Kinabalu, Malaysia: IEEE Xplore.
  20. Martínez Torres, J., Iglesias Comesaña, C., & García-Nieto, P. J. (2019). Machine learning techniques applied to cybersecurity. International Journal of Machine Learning and Cybernetics, 10(10), 2823-2836.
  21. Handa, A., Sharma, A., & Shukla, S. K. (2019). Machine learning in cybersecurity: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), e1306.
  22. Kaushik, D., Garg, M., Gupta, A., & Pramanik, S. (2022). Application of machine learning and deep learning in cybersecurity: An innovative approach. In An Interdisciplinary Approach to Modern Network Security (pp. 89-109). CRC Press.
  23. Shaukat, K., Luo, S., Varadharajan, V., Hameed, I. A., Chen, S., Liu, D., & Li, J. (2020). Performance comparison and current challenges of using machine learning techniques in cybersecurity. Energies, 13(10), 2509.
  24. Bharadiya, J. (2023). Machine learning in cybersecurity: Techniques and challenges. European Journal of Technology, 7(2), 1-14.
  25. Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., ... & Wang, C. (2018). Machine learning and deep learning methods for cybersecurity. Ieee access, 6, 35365-35381.
  26. Li, W., Wu, G., & Du, Q. (2017). Transferred deep learning for anomaly detection in hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 14(5), 597-601.
  27. Bian, H., Zhu, Z., Zang, X., Luo, X., & Jiang, M. (2022). A CNN based anomaly detection network for utility tunnel fire protection. Fire, 5(6), 212.
  28. Tang, Z., Chen, Z., Bao, Y., & Li, H. (2019). Convolutional neural network‐based data anomaly detection method using multiple information for structural health monitoring. Structural Control and Health Monitoring, 26(1), e2296.
  29. Caliva, F., De Ribeiro, F. S., Mylonakis, A., Demazi’ere, C., Vinai, P., Leontidis, G., & Kollias, S. (2018, July). A deep learning approach to anomaly detection in nuclear reactors. In 2018 International joint conference on neural networks (IJCNN) (pp. 1-8). IEEE.
  30. Choi, K., Yi, J., Park, C., & Yoon, S. (2021). Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines. IEEE access, 9, 120043-120065.
  31. Lu, S., Wei, X., Li, Y., & Wang, L. (2018, August). Detecting anomaly in big data system logs using convolutional neural network. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech) (pp. 151-158). IEEE.
  32. Patcha, A., & Park, J. M. (2007). An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer networks, 51(12), 3448-3470.
  33. Naseer, S., Saleem, Y., Khalid, S., Bashir, M. K., Han, J., Iqbal, M. M., & Han, K. (2018). Enhanced network anomaly detection based on deep neural networks. IEEE access, 6, 48231-48246.
  34. Rezaee, K., Rezakhani, S. M., Khosravi, M. R., & Moghimi, M. K. (2024). A survey on deep learning-based real-time crowd anomaly detection for secure distributed video surveillance. Personal and Ubiquitous Computing, 28(1), 135-151.
  35. Yin, C., Zhang, S., Wang, J., & Xiong, N. N. (2020). Anomaly detection based on convolutional recurrent autoencoder for IoT time series. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(1), 112-122.
  36. https://research.unsw.edu.au/projects/bot-iot-dataset
Index Terms

Computer Science
Information Sciences
Pattern Recognition
Machine Learning
Network Security
Deep Learning Algorithms
Data Analysis
Evaluation Metrics

Keywords

Network anomaly detection cybersecurity convolutional neural networks BoT-IoT dataset feature selection real-time detection and deep learning models.