Conference paper

HOMOLIAK Ivan, ŠULÁK Ladislav and HANÁČEK Petr. Features for Non-payload Based Behavioral Intrusion Detection of Connectionless Network Buffer Overflow Attacks. In: Information Security Applications - 17th International Workshop, WISA 2016, Jeju Island, Korea, August 25-27, 2016, Revised Selected Papers. Jeju Island: Springer International Publishing, 2017, pp. 66-78. ISBN 978-3-319-56549-1.
Publication language:english
Original title:Features for Non-payload Based Behavioral Intrusion Detection of Connectionless Network Buffer Overflow Attacks
Title (cs):Anomální Behaviorální Analýza Nespojově Orientovaných Síťových Útoků Prováděných Prostřednictvím Zneužití Zranitelností Přetečení Zásobníku
Pages:66-78
Proceedings:Information Security Applications - 17th International Workshop, WISA 2016, Jeju Island, Korea, August 25-27, 2016, Revised Selected Papers
Conference:The 17th World Conference on Information Security Applications
Series:Lecture Notes in Computer Science
Place:Jeju Island, KR
Year:2017
ISBN:978-3-319-56549-1
Publisher:Springer International Publishing
Files: 
+Type Name Title Size Last modified
iconWISA-camera-ready.pdf297 KB2017-03-31 10:49:27
^ Select all
With selected:
Keywords
Buffer overflow, Connectionless traffic, SIP, TFTP, UDP vulnerabilities, NBAD, Naive Bayes
Annotation
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.
BibTeX:
@INPROCEEDINGS{
   author = {Ivan Homoliak and Ladislav {\v{S}}ul{\'{a}}k and Petr
	Han{\'{a}}{\v{c}}ek},
   title = {Features for Non-payload Based Behavioral Intrusion
	Detection of Connectionless Network Buffer Overflow Attacks},
   pages = {66--78},
   booktitle = {Information Security Applications - 17th International
	Workshop, WISA 2016, Jeju Island, Korea, August 25-27, 2016,
	Revised Selected Papers},
   series = {Lecture Notes in Computer Science},
   year = {2017},
   location = {Jeju Island, KR},
   publisher = {Springer International Publishing},
   ISBN = {978-3-319-56549-1},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=10931}
}

Your IPv4 address: 107.20.115.174
Switch to IPv6 connection

DNSSEC [dnssec]