Detection of Malicious Network Flows with Low Preprocessing Overhead




Fox, Garett
Boppana, Rajendra V.

Journal Title

Journal ISSN

Volume Title



Machine learning (ML) is frequently used to identify malicious traffic flows on a network. However, the requirement of complex preprocessing of network data to extract features or attributes of interest before applying the ML models restricts their use to offline analysis of previously captured network traffic to identify attacks that have already occurred. This paper applies machine learning analysis for network security with low preprocessing overhead. Raw network data are converted directly into bitmap files and processed through a Two-Dimensional Convolutional Neural Network (2D-CNN) model to identify malicious traffic. The model has high accuracy in detecting various malicious traffic flows, even zero-day attacks, based on testing with three open-source network traffic datasets. The overhead of preprocessing the network data before applying the 2D-CNN model is very low, making it suitable for on-the-fly network traffic analysis for malicious traffic flows.



network security, deep learning, machine learning, convolutional neural networks, raw packet analysis


Network 2 (4): 628-642 (2022)


Computer Science