Features for Behavioral Anomaly Detection of Connectionless Network Buffer Overflow Attacks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F16%3APU123243" target="_blank" >RIV/00216305:26230/16:PU123243 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007%2F978-3-319-56549-1_6" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-56549-1_6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-56549-1_6" target="_blank" >10.1007/978-3-319-56549-1_6</a>

Result language

angličtina

Original language name

Features for Behavioral Anomaly Detection of Connectionless Network Buffer Overflow Attacks

Original language description

Buffer overflow (BO) attacks are one of the most dangerous threads in the area of network security. Methods for detection of BO attacks basically use two approaches: signature matching against packets' payload versus analysis of packets' headers with the behavioral analysis of the connection's flow. The second approach is intended for detection of BO attacks regardless of packets' content which can be ciphered. In this paper, we propose a technique based on Network Behavioral Anomaly Detection (NBAD) aimed at connectionless network traffic. A similar approach has already been used in related works, but focused on connection-oriented traffic. All principles of connection-oriented NBAD cannot be applied in connectionless anomaly detection. There is designed a set of features describing the behavior of connectionless BO attacks and the tool implemented for their offline extraction from network traffic dumps. Next, we describe experiments performed in the virtual network environment utilizing SIP and TFTP network services exploitation and further data mining experiments employing supervised machine learning (ML) and Naive Bayes classifier. The exploitation of services is performed using network traffic modifications with intention to simulate real network conditions. The experimental results show the proposed approach is capable of distinguishing BO attacks from regular network traffic with high precision and class recall.

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

<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

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

Article name in the collection

Information Security Applications - 17th International Workshop, WISA 2016, Jeju Island, Korea, August 25-27, 2016, Revised Selected Papers

ISBN

978-3-319-56549-1

ISSN

0302-9743

e-ISSN

—

Number of pages

13

Pages from-to

66-78

Publisher name

Springer International Publishing

Place of publication

Jeju Island

Event location

Jeju Island, South Korea

Event date

Aug 25, 2016

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000426125100006