Encrypted Application Classification with Convolutional Neural Network

Kun Yang; Lu Xu; Yang Xu; Jonathan Chao

Encrypted Application Classification with Convolutional Neural Network

Kun Yang, Lu Xu, Yang Xu, Jonathan Chao

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Encrypted application classification (EAC) has become an emerging and challenging task for network monitoring and management, and statistical-based approaches are less impacted by encrypted streams. However, much effort is required from domain experts to handcraft statistical features. To solve this problem, this paper proposes an end-to-end encrypted application classification framework (E2E-EACF) based on one dimensional convolutional neural network (1D-CNN). Only encrypted payload (EncP) and inter-arrival time (IAT) are required by the framework to classify encrypted flows. Experimental results denmonstrate that E2E-EACF can achieve more than 91.00% accuracy and 0.92 F1 score (the harmonic average of precision and recall) on a public dataset (WRCCDC), better than classical machine learning algorithms (e.g., decision tree and support vector machine).

Original language	English (US)
Title of host publication	IFIP Networking 2020 Conference and Workshops, Networking 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	499-503
Number of pages	5
ISBN (Electronic)	9783903176287
State	Published - Jun 2020
Event	2020 IFIP Networking Conference and Workshops, Networking 2020 - Paris, France Duration: Jun 22 2020 → Jun 25 2020

Publication series

Name	IFIP Networking 2020 Conference and Workshops, Networking 2020

Conference

Conference	2020 IFIP Networking Conference and Workshops, Networking 2020
Country/Territory	France
City	Paris
Period	6/22/20 → 6/25/20

Keywords

Convolutional Neural Network (CNN)
Deep Learning (DL)
Deep Packet Inspection (DPI)
Encrypted Application Classification (EAC)
Machine Learning (ML)

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Information Systems and Management
Safety, Risk, Reliability and Quality

Cite this

Encrypted Application Classification with Convolutional Neural Network. / Yang, Kun; Xu, Lu; Xu, Yang et al.
IFIP Networking 2020 Conference and Workshops, Networking 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 499-503 9142772 (IFIP Networking 2020 Conference and Workshops, Networking 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yang, K, Xu, L, Xu, Y & Chao, J 2020, Encrypted Application Classification with Convolutional Neural Network. in IFIP Networking 2020 Conference and Workshops, Networking 2020., 9142772, IFIP Networking 2020 Conference and Workshops, Networking 2020, Institute of Electrical and Electronics Engineers Inc., pp. 499-503, 2020 IFIP Networking Conference and Workshops, Networking 2020, Paris, France, 6/22/20.

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title = "Encrypted Application Classification with Convolutional Neural Network",

abstract = "Encrypted application classification (EAC) has become an emerging and challenging task for network monitoring and management, and statistical-based approaches are less impacted by encrypted streams. However, much effort is required from domain experts to handcraft statistical features. To solve this problem, this paper proposes an end-to-end encrypted application classification framework (E2E-EACF) based on one dimensional convolutional neural network (1D-CNN). Only encrypted payload (EncP) and inter-arrival time (IAT) are required by the framework to classify encrypted flows. Experimental results denmonstrate that E2E-EACF can achieve more than 91.00% accuracy and 0.92 F1 score (the harmonic average of precision and recall) on a public dataset (WRCCDC), better than classical machine learning algorithms (e.g., decision tree and support vector machine).",

keywords = "Convolutional Neural Network (CNN), Deep Learning (DL), Deep Packet Inspection (DPI), Encrypted Application Classification (EAC), Machine Learning (ML)",

author = "Kun Yang and Lu Xu and Yang Xu and Jonathan Chao",

note = "Publisher Copyright: {\textcopyright} 2020 IFIP. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 IFIP Networking Conference and Workshops, Networking 2020 ; Conference date: 22-06-2020 Through 25-06-2020",

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language = "English (US)",

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AU - Yang, Kun

AU - Xu, Lu

AU - Xu, Yang

AU - Chao, Jonathan

PY - 2020/6

Y1 - 2020/6

N2 - Encrypted application classification (EAC) has become an emerging and challenging task for network monitoring and management, and statistical-based approaches are less impacted by encrypted streams. However, much effort is required from domain experts to handcraft statistical features. To solve this problem, this paper proposes an end-to-end encrypted application classification framework (E2E-EACF) based on one dimensional convolutional neural network (1D-CNN). Only encrypted payload (EncP) and inter-arrival time (IAT) are required by the framework to classify encrypted flows. Experimental results denmonstrate that E2E-EACF can achieve more than 91.00% accuracy and 0.92 F1 score (the harmonic average of precision and recall) on a public dataset (WRCCDC), better than classical machine learning algorithms (e.g., decision tree and support vector machine).

AB - Encrypted application classification (EAC) has become an emerging and challenging task for network monitoring and management, and statistical-based approaches are less impacted by encrypted streams. However, much effort is required from domain experts to handcraft statistical features. To solve this problem, this paper proposes an end-to-end encrypted application classification framework (E2E-EACF) based on one dimensional convolutional neural network (1D-CNN). Only encrypted payload (EncP) and inter-arrival time (IAT) are required by the framework to classify encrypted flows. Experimental results denmonstrate that E2E-EACF can achieve more than 91.00% accuracy and 0.92 F1 score (the harmonic average of precision and recall) on a public dataset (WRCCDC), better than classical machine learning algorithms (e.g., decision tree and support vector machine).

KW - Convolutional Neural Network (CNN)

KW - Deep Learning (DL)

KW - Deep Packet Inspection (DPI)

KW - Encrypted Application Classification (EAC)

KW - Machine Learning (ML)

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ER -

Encrypted Application Classification with Convolutional Neural Network

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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