Malware Detection Using Perceptrons and Support Vector Machines
In this paper we explore the capabilities of a framework that can use different machine learning algorithms to successfully detect malware files, aiming to minimize the number of false positives. We report the results obtained in our framework, working firstly with cascades of one-sided perceptron a...
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| creator | Gavrilut, D. Cimpoesu, M. Anton, D. Ciortuz, L. |
| description | In this paper we explore the capabilities of a framework that can use different machine learning algorithms to successfully detect malware files, aiming to minimize the number of false positives. We report the results obtained in our framework, working firstly with cascades of one-sided perceptron and kernelized one-sides perceptrons and secondly with cascade of one-sided support vector machines. |
| doi_str_mv | 10.1109/ComputationWorld.2009.85 |
| format | Conference Proceeding |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Application software Computer displays Computer networks Computer science Face detection Machine learning Machine learning algorithms malware perceptrons Support vector machine classification Support vector machines Testing viruses |
| title | Malware Detection Using Perceptrons and Support Vector Machines |
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