Intrusion detection system for cloud forensics using bayesian fuzzy clustering and optimization based SVNN
| Autor: | A. S. N. Chakravarthy, Siva Rama Krishna Tummalapalli |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Fuzzy clustering
Artificial neural network Computer science business.industry Cognitive Neuroscience 020206 networking & telecommunications Cloud computing 02 engineering and technology Intrusion detection system computer.software_genre Constant false alarm rate Support vector machine ComputingMethodologies_PATTERNRECOGNITION Mathematics (miscellaneous) Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Data mining business Cluster analysis computer Classifier (UML) |
| Zdroj: | Evolutionary Intelligence. 14:699-709 |
| ISSN: | 1864-5917 1864-5909 |
| Popis: | Intrusion detection has emerged as one of the major challenges involved in the cloud forensics. This work introduces an intrusion detection framework for the cloud environment with clustering and two-level classifiers. In the first step of the process, a Bayesian fuzzy clustering is used for clustering the nodes in the cloud. And in the next step, two-level gravitational group search-based support vector neural network (GG-SVNN) classifier identifies intrusion in clusters. GG-SVNN is a novel optimization scheme proposed in this work, by combining the group search optimizer, and gravitational search algorithm. The intrusion information provided by level 1 classifier is arranged to form compact data, and provided to the level 2 classifier. The level 2 classifier finally identifies total nodes affected by the intruders. The simulation of the proposed intrusion detection is done with the help of KDD cup dataset. From the simulation results, it is evident that the proposed GG-SVNN classifier has achieved overall best performance by achieving high accuracy value of 92.41% and low false alarm rate of 4.75% respectively. |
| Databáze: | OpenAIRE |
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