All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

NBA of Obfuscated Network Vulnerabilities' Exploitation Hidden into HTTPS Traffic

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F14%3APU112017" target="_blank" >RIV/00216305:26230/14:PU112017 - isvavai.cz</a>

  • Result on the web

    <a href="http://www.fit.vutbr.cz/research/pubs/all.php?id=10658" target="_blank" >http://www.fit.vutbr.cz/research/pubs/all.php?id=10658</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    NBA of Obfuscated Network Vulnerabilities' Exploitation Hidden into HTTPS Traffic

  • Original language description

    This paper examines the detection properties of obfuscated network buffer overflow attacks by selected IDS and NBA. The obfuscation was performed by tunneling the malicious traffic in HTTP and HTTPS protocols with the intention of simulating the usual legitimate characteristics of the HTTP traffic's flow. The buffer overflow vulnerabilities of four services were used: Samba, BadBlue, Apache, DCOM RPC. Exploitation was performed in a virtual network environment by using scenarios simulating real traffic's conditions as well as legitimate traffic simulations which were performed. Captured data were examined by SNORT and by ASNM network features of the AIPS representing statistically and behaviorally based NBA. The achieved results show an obfuscated attacks transparency for SNORT detection and low detection performance of the AIPS trained by direct attacks and legitimate traffic only in contrast with high classification accuracy of the AIPS trained with an inclusion of obfuscated attacks. Data mining analysis was performed by using both bi-nominal and poly-nominal classifications, resulting into better performance of poly-nominal classification. At the summary, we emphasize the necessity of training the statistically and behaviorally based NBAs with divergent obfuscation techniques to strengthen their detection capabilities.

  • Czech name

  • Czech description

Classification

  • 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)

Result continuities

  • Project

    <a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>

  • Continuities

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

Others

  • Publication year

    2014

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Proceedings of International Conference for Internet Technology and Secured Transactions (ICITST-2014)

  • ISBN

    978-1-908320-40-7

  • ISSN

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    311-318

  • Publisher name

    IEEE Computer Society

  • Place of publication

    London

  • Event location

    Londýn

  • Event date

    Dec 8, 2014

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article