Klm-PPSA: Klm-based profiling and preventing security attacks for cloud environments: Invited Paper
| Autor: | Mohamed Sadik, Essaid Sabir, Nahid Eddermoug, Abdeljebar Mansour, Mohamed Azmi |
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| Rok vydání: | 2019 |
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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 |
| Zdroj: | WINCOM |
| DOI: | 10.1109/wincom47513.2019.8942509 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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