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Klm-PPSA: Klm-based profiling and preventing security attacks for cloud environments: Invited Paper
- Source :
- WINCOM
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- Cloud computing is the newly emerged technology adopted by many organizations due to its different benefits. Unfortunately, despite all the benefits offered by the cloud, there are certain concerns regarding the security issues related to the cloud platform which can threaten its widespread adoption. In this study, we suggest a scalable model to profile and prevent security attacks in the application layer of a cloud environment using an accurate and interpretable machine learning algorithm called regularized class association rules. The proposed model is based, first, on three additional security factors $(k, l$ and $m)$ , second, on the traditional authentication methods such as passwords and biometrics including keystroke to grant access to the cloud services/resources for an authorized user. Moreover, a case study of the proposal is given in order to validate the model and its usefulness. Eventually, a simulation was done to test the model performances.
- Subjects :
- Password
Cloud computing security
Computer science
business.industry
020206 networking & telecommunications
Cloud computing
02 engineering and technology
Intrusion detection system
Keystroke logging
Computer security
computer.software_genre
01 natural sciences
Application layer
010104 statistics & probability
Scalability
0202 electrical engineering, electronic engineering, information engineering
Profiling (information science)
0101 mathematics
business
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2019 International Conference on Wireless Networks and Mobile Communications (WINCOM)
- Accession number :
- edsair.doi...........9e7787bf2662f9e5b622968495ccc286