DocumentCode
1922470
Title
An approach with two-stage mode to detect cache-based side channel attacks
Author
Si Yu ; Xiaolin Gui ; Jiancai Lin
Author_Institution
Shaanxi Province Key Lab. of Comput. Network, Xi´an Jiaotong Univ., Xi´an, China
fYear
2013
fDate
28-30 Jan. 2013
Firstpage
186
Lastpage
191
Abstract
Side channel attacks, which intend to analyze third party sharing resources responses, has become a significant security threat to cloud, in particular the cache-based side channel attacks. In this paper, to eliminate such a security threat in cloud, based on the observation that the creation of a side channel has certain effects on the resource utilization in both the host and guest, we investigate the detection approach for detecting cache-based side channel attacks, named CSDA. The approach uses the two-stage detection mode which consists of host detection and guest detection, combines shape test and regularity test to extract the attack features from hosts and guests, and uses pattern recognition techniques to distinguish the attack VMs from the legitimate VMs. At last, a series of experiments are conducted, and the experimental results show that CSDA is capable of detecting them in cloud effectively.
Keywords
cloud computing; resource allocation; telecommunication channels; telecommunication security; CSDA; cache-based side channel attacks; cloud computing; guest detection; host detection; pattern recognition; regularity test; resource utilization; security threat; shape test; third party sharing resources responses; two-stage detection mode; two-stage mode; Cloud computing; Feature extraction; Security; Shape; Standards; Synthetic aperture sonar; Timing; attack detection; cache-based side channel attacks; cloud computing; virtualization security;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Networking (ICOIN), 2013 International Conference on
Conference_Location
Bangkok
ISSN
1976-7684
Print_ISBN
978-1-4673-5740-1
Electronic_ISBN
1976-7684
Type
conf
DOI
10.1109/ICOIN.2013.6496374
Filename
6496374
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