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

Main Article Content

P.Venkata Krishna
K Venkatesh Sharma
A MallaReddy

Abstract

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.

Article Details

How to Cite
[1]
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.
Section
Research Articles

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