| Autor: |
Muthulakshmi, R. M., Anithaashri, T. P., Nataraj, C., Talasila, V. S. N. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3161 Issue 1, p1-7, 7p |
| Abstrakt: |
The development of the cloud makes virtual machines a growing target for malware attacks. Virtual Environments are the legitimate target of Advanced Persistent Threats creating an undetected presence to gain access to sensitive data over a certain period. Rootkit is hidden in some arbitrary files accessed while opening and can modify or delete the data in the targeted system. In the proposed model the features are derived from the network attributes applied to the Artificial Neural Network of the private cloud. The classification is based on the anomaly detection of data in the cloud. Neural networks divide the network attributes and categorize them into two classes Benign and Root-Kit. Satisfactory results may be obtained from the mathematical models using security metrics due to their reliance on empirical data, qualitative methodologies, and compliance testing. The results show the high accuracy of detecting the rootkit malware using ANN with backward propagation. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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