A Machine Learning-based Approach for Detecting Network Intrusions in Large-scale Networks

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P.Venkata Krishna
K Venkatesh Sharma
A MallaReddy


The objective of this research is to explore and enhance the mechanisms for detecting network intrusions, particularly focusing on large-scale networks. Traditional Intrusion Detection Systems (IDS) are frequently challenged by several limitations. These include high rates of false alarms, an inability to adapt swiftly to new and evolving threats, and challenges in scaling to accommodate large volumes of network traffic. Addressing these limitations, the study introduces a comprehensive approach that incorporates machine learning techniques to bolster network security. The methodology specifically employs Support Vector Machines (SVM) and Decision Trees as classifiers. SVM is known for its effectiveness in classifying high-dimensional data, while Decision Trees are favoured for their ease of interpretation and decision-making transparency. The research meticulously evaluates and contrasts the proposed approach with existing IDS. It reveals that the integration of SVM and Decision Trees significantly improves the accuracy of intrusion detection, with the model achieving an accuracy rate of up to 95% in certain test scenarios. This marks a substantial enhancement compared to traditional IDS. Furthermore, the study emphasizes the model's capability to adapt in real-time to emerging threats. This adaptability ensures that the IDS remains robust and effective even as network threats evolve, thereby addressing a critical gap in existing systems. In conclusion, this research underscores the potential of machine learning, specifically through the use of SVM and Decision Trees, in enhancing the precision, adaptability, and scalability of intrusion detection systems in large-scale networks. The findings suggest that such an approach can mitigate prevalent challenges in network security and contribute significantly to establishing a more secure and resilient cyber environment.

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How to Cite
P.Venkata Krishna, K Venkatesh Sharma, and A MallaReddy, “A Machine Learning-based Approach for Detecting Network Intrusions in Large-scale Networks ”, Int. J. Comput. Eng. Res. Trends, vol. 10, no. 2, pp. 69–76, Feb. 2023.
Research Articles


] Al-Jarrah, O. Y., Siddiqui, A., Elsalamouny, M., Yoo, P. D., Muhaidat, S., & Kim, K. (2014). Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection. In 2014 IEEE 34th International Conference on Distributed Computing Systems Workshops (ICDCSW) (pp. 177-181). Madrid, Spain. https://doi.org/10.1109/ICDCSW.2014.14

] Abdulhammed, R., Musafer, H., Alessa, A., Faezipour, M., & Abuzneid, A. (2019). Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection. Electronics, 8(3), 322. https://doi.org/10.3390/electronics8030322

] Siddiqi, M. A., & Pak, W. (2021). An Agile Approach to Identify Single and Hybrid Normalization for Enhancing Machine Learning-Based Network Intrusion Detection. IEEE Access, 9, 137494-137513. https://doi.org/10.1109/ACCESS.2021.3118361

] Liu, L., Wang, P., Lin, J., & Liu, L. (2021). Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning and Deep Learning. IEEE Access, 9, 7550-7563. https://doi.org/10.1109/ACCESS.2020.3048198

] Kumar, S. A. P., & Xu, B. (2018). A Machine Learning Based Approach to Detect Malicious Fast Flux Networks. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1676-1683). Bangalore, India. https://doi.org/10.1109/SSCI.2018.8628729

] Pinto, E. M. d. L., Lachowski, R., Pellenz, M. E., Penna, M. C., & Souza, R. D. (2018). A Machine Learning Approach for Detecting Spoofing Attacks in Wireless Sensor Networks. In 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA) (pp. 752-758). Krakow, Poland. https://doi.org/10.1109/AINA.2018.00113

] Kotla Venkata, R. (2022). A Machine Learning Based Approach for Detection of Distributed Denial of Service Attacks. In B. Unhelker, H. M. Pandey, & G. Raj (Eds.), Applications of Artificial Intelligence and Machine Learning (pp. 89-100). Springer. https://doi.org/10.1007/978-981-19-4831-2_7

] Anbar, M., Abdullah, R., Al-Tamimi, B. N., & Al-Qahtani, A. (2018). A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks. Cognitive Computation, 10, 201-214. https://doi.org/10.1007/s12559-017-9519-8

] Vinayakumar, R., Chaganti, R., & Alazab, M. (2022). Recurrent deep learning-based feature fusion ensemble meta-classifier approach for intelligent network intrusion detection system. Computers and Electrical Engineering, 102, 108156. https://doi.org/10.1016/j.compeleceng.2022.108156

] Wang, X., Yin, S., Li, H., & Li, Y. (2020). A Network Intrusion Detection Method Based on Deep Multi-scale Convolutional Neural Network. International Journal of Wireless Information Networks, 27, 503-517. https://doi.org/10.1007/s10776-020-00495-3

] Ravi, V., Chaganti, R., & Alazab, M. (2022). Deep Learning Feature Fusion Approach for an Intrusion Detection System in SDN-Based IoT Networks. IEEE Internet of Things Magazine, 5(2), 24-29. https://doi.org/10.1109/IOTM.003.2200001

] Gurina, A., & Eliseev, V. (2019). Anomaly-Based Method for Detecting Multiple Classes of Network Attacks. Information, 10(3), 84. https://doi.org/10.3390/info10030084

] Sethi, K., Sai Rupesh, E., Kumar, R., & Singh, S. (2020). A context-aware robust intrusion detection system: a reinforcement learning-based approach. International Journal of Information Security, 19, 657-678. https://doi.org/10.1007/s10207-019-00482-7

] Sahu, A., Mao, Z., Davis, K., & Goulart, A. E. (2020). Data Processing and Model Selection for Machine Learning-based Network Intrusion Detection. In 2020 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR) (pp. 1-6). Stevenson, WA, USA. https://doi.org/10.1109/CQR47547.2020.9101394

] Zhang, K. (2019). A Machine Learning Based Approach to Identify SQL Injection Vulnerabilities. In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 1286-1288). San Diego, CA, USA. https://doi.org/10.1109/ASE.2019.00164