Classifier fusion for VoIP attacks classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F17%3A10132895" target="_blank" >RIV/63839172:_____/17:10132895 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1117/12.2262744" target="_blank" >http://dx.doi.org/10.1117/12.2262744</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.2262744" target="_blank" >10.1117/12.2262744</a>

Result language

angličtina

Original language name

Classifier fusion for VoIP attacks classification

Original language description

SIP is one of the most successful protocols in the field of IP telephony communication. It establishes and manages VoIP calls. As the number of SIP implementation rises, we can expect a higher number of attacks on the communication system in the near future. This work aims at malicious SIP traffic classification. A number of various machine learning algorithms have been developed for attack classification. The paper presents a comparison of current research and the use of classifier fusion method leading to a potential decrease in classification error rate. Use of classifier combination makes a more robust solution without difficulties that may affect single algorithms. Different voting schemes, combination rules, and classifiers are discussed to improve the overall performance. All classifiers have been trained on real malicious traffic. The concept of traffic monitoring depends on the network of honeypot nodes. These honeypots run in several networks spread in different locations. Separation of honeypots allows us to gain an independent and trustworthy attack information.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of SPIE 10200; Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI

ISBN

978-1-5106-0901-3

ISSN

0277-786X

e-ISSN

neuvedeno

Number of pages

7

Pages from-to

"102001F"-"102001F7"

Publisher name

SPIE

Place of publication

Neuveden

Event location

Anaheim, California, United States

Event date

Apr 9, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000424391600040