DocumentCode :
1696137
Title :
Framework for cloud intrusion detection system service
Author :
Aljurayban, Nouf Saleh ; Emam, Ahmed
Author_Institution :
Inf. Syst. Dept., King Saud Univ., Riyadh, Saudi Arabia
fYear :
2015
Firstpage :
1
Lastpage :
5
Abstract :
In this Internet era, the use of cloud computing is causing a massive volume of online financial transactions, and the exchange of personal and sensitive information over the internet. Attackers use many different types of malware in searches motivated by curiosity or financial gain. In this paper, we propose an efficient framework called the Layered Intrusion Detection Framework (LIDF) that can be applied on the different layers of cloud computing in order to identify the presence of normal traffic among the monitored cloud traffic. The proposed framework uses data mining, especially an Artificial Neural Network, which makes it accurate, fast, and scalable. At the same time, the LIDF can reduce the rate of the analyzed traffic and achieve better performance by increasing the throughput without affecting its main goal.
Keywords :
cloud computing; data mining; invasive software; neural nets; telecommunication traffic; ANN; Internet era; LIDF; artificial neural network; cloud computing; cloud intrusion detection system service; cloud traffic; data mining; layered intrusion detection framework; malware; normal traffic; online financial transactions; Artificial neural networks; Cloud computing; Computational modeling; Intrusion detection; Monitoring; Training; Artificial Neural Network; Data Mining; Intrusion Detection; cloud computing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Web Applications and Networking (WSWAN), 2015 2nd World Symposium on
Conference_Location :
Sousse
Print_ISBN :
978-1-4799-8171-7
Type :
conf
DOI :
10.1109/WSWAN.2015.7210298
Filename :
7210298
Link To Document :
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