Conference paper

VRÁNA Roman and KOŘENEK Jan. Acceleration of Feature Extraction for Real-Time Analysis of Encrypted Network Traffic. In: 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits & Systems (DDECS). Cluj-Napoca: Institute of Electrical and Electronics Engineers, 2019, pp. 1-6. ISBN 978-1-72810-073-9.
Publication language:english
Original title:Acceleration of Feature Extraction for Real-Time Analysis of Encrypted Network Traffic
Title (cs):Akcelerace extrakce parametrů pro analýzu šifrovaného síťového provozu v reálném čase
Pages:1-6
Proceedings:2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits & Systems (DDECS)
Conference:22nd IEEE International Symposium on Design and Diagnostics of Electronic Circuits and Systems 2019
Place:Cluj-Napoca, RO
Year:2019
ISBN:978-1-72810-073-9
DOI:10.1109/DDECS.2019.8724658
Publisher:Institute of Electrical and Electronics Engineers
Keywords
  • Entropy
  • Feature extraction
  • Payloads
  • Cryptography
  • Real-time systems
  • Acceleration
  • Computer architecture
Annotation
With the growing amount of encrypted network traffic, it is important to have tools for the analysis and classification of encrypted network data. Encrypted network traffic is usually analysed by statistical methods because Deep Packet Inspection or pattern matching is not applicable. However, the statistical methods are usually designed to work offline on already captured network traffic. For real-time analysis, hardware acceleration is needed to achieve wire-speed 10 Gbps throughput. Therefore, we focus on real-time monitoring of encrypted network traffic and propose a new acceleration method to extract features from encrypted network data. Approximate computing is used to speed up the computation of entropy for the input data stream and to reduce FPGA logic utilization. As can be seen in the results, the precision of classification has decreased only by 0.1 to 0.2. Moreover, proposed hardware architecture has very low FPGA logic utilization and can operate on high frequency.
Abstract
With the growing amount of encrypted network traffic, it is important to have tools for the analysis and classification of encrypted network data. Encrypted network traffic is usually analysed by statistical methods because Deep Packet Inspection or pattern matching is not applicable. However, the statistical methods are usually designed to work offline on already captured network traffic. For real-time analysis, hardware acceleration is needed to achieve wire-speed 10 Gbps throughput. Therefore, we focus on real-time monitoring of encrypted network traffic and propose a new acceleration method to extract features from encrypted network data. Approximate computing is used to speed up the computation of entropy for the input data stream and to reduce FPGA logic utilization. As can be seen in the results, the precision of classification has decreased only by 0.1 to 0.2. Moreover, proposed hardware architecture has very low FPGA logic utilization and can operate on high frequency.
BibTeX:
@INPROCEEDINGS{
   author = {Roman Vr{\'{a}}na and Jan Ko{\v{r}}enek},
   title = {Acceleration of Feature Extraction for Real-Time
	Analysis of Encrypted Network Traffic},
   pages = {1--6},
   booktitle = {2019 22nd International Symposium on Design and Diagnostics
	of Electronic Circuits \& Systems (DDECS)},
   year = {2019},
   location = {Cluj-Napoca, RO},
   publisher = {Institute of Electrical and Electronics Engineers},
   ISBN = {978-1-72810-073-9},
   doi = {10.1109/DDECS.2019.8724658},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.en.iso-8859-2?id=11899}
}

Your IPv4 address: 54.172.234.236