Machine learning raw network traffic detection

Michael J. De Lucia; Paul E. Maxwell; Nathaniel D. Bastian; Ananthram Swami; Brian Jalaian; Nandi Leslie

doi:10.1117/12.2586114

12 April 2021 Machine learning raw network traffic detection

Michael J. De Lucia, Paul E. Maxwell, Nathaniel D. Bastian, Ananthram Swami, Brian Jalaian, Nandi Leslie

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117460V (2021) https://doi.org/10.1117/12.2586114
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Increasingly cyber-attacks are sophisticated and occur rapidly, necessitating the use of machine learning techniques for detection at machine speed. However, the use of machine learning techniques in cyber security requires the extraction of features from the raw network traffic. Thus, subject matter expertise is essential to analyze the network traffic and extract optimum features to detect a cyber-attack. Consequently, we propose a novel machine learning algorithm for malicious network traffic detection using only the bytes of the raw network traffic. The feature vector in our machine learning method is a structure containing the headers and a variable number of payload bytes. We propose a 1D-Convolutional Neural Network (1D-CNN) and Feed Forward Network for detection of malicious packets using raw network bytes.

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Michael J. De Lucia, Paul E. Maxwell, Nathaniel D. Bastian, Ananthram Swami, Brian Jalaian, and Nandi Leslie "Machine learning raw network traffic detection", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117460V (12 April 2021); https://doi.org/10.1117/12.2586114

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KEYWORDS

Machine learning

Network security

Detection and tracking algorithms

Feature extraction

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