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논문검색

Session Ⅵ : Artificial Intelligence

Cloud Security Issues Detection Using Fuzzy Logic

초록

영어

Cloud computing is computing that provides, storage, databases, networking, intelligence, software, and analytics over the internet. Cloud services are delivered remotely and almost always from an offsite data center. Cloud services manage a better computing infrastructure efficiently. This study presents security issues & challenges in cloud computing and tries to find out the possible solution for some of the problems. It also discusses some solutions that deal with cloud computing-related to its privacy and security challenges. The proposed Intelligent Cloud Security Issues Detection using Multilayer Mamdani Fuzzy Inference System (ICSID-ML-MFIS) Expert System, can classify the different types of threats. The Expert System has eight input variables at layer-I, three input variables at layers-II, three input variables at layers-III, and six input variables at layers-IV. At layer-I input variables are threat-to-software, Traffic Monitoring, Networking Threat, Resource Availability, Platform availability, Trusted-Service-Availability, Device Availability, and Network Availability that detects output condition of threats to be affected or Not-Affected. At layer-II input variables are Detect SAAS Threats, Detect PAAS Threat, and Detect IAAS Threats, which determine the output condition as Yes or No. At layer-III input variables are Monitoring, Gaining, and managing which determine the output condition as cloud security type DCST. At layer-IV input variables are security incident response (SR), privilege identity management, locate current security problem (SP), super user account, a factor leading to inability to control traffic, and locate social engineering attacks. At last output, the layer consists of eight output types to detect the cloud security issues such as lack of visibility of data, theft of data, inability to control data, hijacking, system vulnerability, social engineering attacks, data breaches, and no-security issues. The proposed model based on Fuzzy reached 91.5% of true positive cases.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. METHODOLOGY
IV. RESULTS AND DISCUSSION
V. CONCLUSION
REFERENCES

저자정보

  • Taher M. Ghazal School of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah,,United Arab Emirates.
  • Shahan Yamin Siddiqui School of Computer Science Minhaj University Lahore, 54000, Pakistan
  • Muhammad Ubaidullah School of Computer Science Minhaj University Lahore, 54000, Pakistan
  • Hafiz Muhammad Usama School of Computer Science Minhaj University Lahore, 54000, Pakistan
  • Ali Younas School of Computer Science Minhaj University Lahore, 54000, Pakistan
  • Atif Ali UIIT, PMAS Arid Agriculture University Rawalpindi, Pakistan

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