Novel HTTPS classifier driven by packet bursts, flows, and machine learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F21%3A10133378" target="_blank" >RIV/63839172:_____/21:10133378 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/21:00353112
Result on the web
<a href="http://dx.doi.org/10.23919/CNSM52442.2021.9615561" target="_blank" >http://dx.doi.org/10.23919/CNSM52442.2021.9615561</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/CNSM52442.2021.9615561" target="_blank" >10.23919/CNSM52442.2021.9615561</a>

Result language
angličtina
Original language name
Novel HTTPS classifier driven by packet bursts, flows, and machine learning
Original language description
Encryption of network traffic recently starts to cover remaining readable information, which is heavily used by current monitoring systems; thus, it is time to focus on novel methods of encrypted traffic analysis and classification. The aim of this paper is to define a new network traffic characteristic called Sequence of packet Burst Length and Time (SBLT), which was inspired by existing approaches and definitions. Contrary to other works, SBLT is feasible even for high-speed backbone networks as a part of IP flow data. The advantage of SBLT features is shown using a machine learning classification model for HTTPS traffic types as an example. This paper presents the definition of SBLT, proposes a new annotated public dataset of HTTPS traffic with 5 categories, and evaluates the developed classifier reaching accuracy over 99 %. This classifier can help analysts to deal with a huge amount of encrypted traffic and maintain situational awareness.
Czech name
—
Czech description
—

Project
<a href="/en/project/TH04010073" target="_blank" >TH04010073: Smart ADS</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 2021 17th International Conference on Network and Service Management
ISBN
978-3-903176-36-2
ISSN
2165-963X
e-ISSN
—
Number of pages
5
Pages from-to
345-349
Publisher name
IEEE
Place of publication
Piscataway , USA
Event location
Izmir, Turecko
Event date
Oct 25, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)