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Towards Detecting and Classifying
Network Intrusion Traffic Using Deep Learning Frameworks Ram B. Basnet1+, Riad Shash1, Clayton Johnson1,
Lucas Walgren1, and Tenzin Doleck2 1Colorado
Mesa University, Grand
Junction, CO 81501 USA rbasnet@coloradomesa.edu, {rshash,
cpjohnson, lawalgren}@mavs.coloradomesa.edu 2McGill
University, Quebec, CA tenzin.doleck@mail.mcgill.ca
Abstract Recent breakthroughs in deep learning algorithms
have enabled researchers and practitioners to make significant progress in
various hard computer science problems and applications from computer vision
and perception, natural language processing and interpretation to complex
reasoning tasks such as playing board games (e.g., Go, Chess, etc.) and even
overthrowing human champions. Considering the expected acceleration and
increase in computer threats, in this article, we explore the utility and
capability of deep learning algorithms in the important area of network
intrusion detection. We apply and compare various state-of-the-art deep
learning frameworks (e.g., Keras, TensorFlow, Theano, fast.ai,
and PyTorch) in detecting network intrusion traffic
and also in classifying common network attack types using the recent
CSE-CIC-IDS2018 dataset. Experimental results show that fast.ai, a highly
opinionated wrapper for PyTorch, provided the
highest accuracy of about 99% with low false positive and negative rates in
both detecting and classifying various intrusion types. Our results provide
evidence of the utility of various deep learning frameworks detecting network
intrusion traffic. Keywords: Intrusion Detection, Deep Learning, Network Security,
Web Security, Brute Force, +: Corresponding
author: Ram B. Basnet Department of Computer Science and Engineering,
Colorado Mesa University, 1100 North Aveneue, Grand
Junction, CO 81501 USA, Tel: +1-970-248-1682, Web: https://rambasnet.github.io Journal
of Internet Services and Information Security
(JISIS), 9(4): 1-17,
November 2019 DOI: 10.22667/JISIS.2019.11.30.001 [pdf] |