Digital Investigation of Network Traffic Using Machine Learning

Saswati Chatterjee; Suneeta Satpathy; Arpita Nibedita

doi:10.4108/eetsis.4055

Authors

Saswati Chatterjee Sri Sri University
Suneeta Satpathy Siksha O Anusandhan University
Arpita Nibedita Trident Academy of Technology

DOI:

https://doi.org/10.4108/eetsis.4055

Keywords:

KDD, Hybrid Machine Learning, Network forensics, DDoS

Abstract

In this study, an intelligent system that can gather and process network packets is built. Machine learning techniques are used to create a traffic classifier that divides packets into hazardous and non-malicious categories. The system utilizing resources was previously classified using a number of conventional techniques; however, this strategy adds machine learning., a study area that is currently active and has so far yielded promising results. The major aims of this paper are to monitor traffic, analyze incursions, and control them. The flow of data collection is used to develop a traffic classification system based on features of observed internet packets. This classification will aid IT managers in recognizing the vague assault that is becoming more common in the IT industry The suggested methods described in this research help gather network data and detect which threat was launched in a specific network to distinguish between malicious and benign packets. This paper’s major goal is to create a proactive system for detecting network attacks using classifiers based on machine learning that can recognize new packets and distinguish between hostile and benign network packets using rules from the KDD dataset. The algorithm is trained to employ the characteristics of the NSL-KDD dataset.

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