The SVM classifier is approved to detect unknown samples of malware with the probability of 72- 86 percent. The detection principle is that, SVM algorithm generates detection model learning from the sufficient dataset of malicious software.
Abstract: In this paper, we propose a polymorphic viruses detection method based on support vector machine (SVM) in the Windows platform.
Abstract: In this paper, we propose a polymorphic viruses detection method based on support vector machine (SVM) in the Windows platform.
Our system uses a hybrid approach for discovering malware based on support vector machine classifier so that potential of malware detection system can be.
Detecting malware evolution using support vector machines
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Apr 1, 2020 · The experimental results reported in this paper show that our SVM-based technique can detect abrupt changes in malware families. We also ...
This system uses a hybrid approach for discovering malware based on support vector machine classifier so that potential of malware detection system can be ...
May 1, 2019 · Abstract. A paper presents a new technique for the mobile malware detection based on the malware's network features analysis is proposed.
An example of utilizing such feature is explained in [6] , which utilized Support Vector Machine (SVM) for detecting unknown virus. In this paper we combined ...
Nov 3, 2023 · This paper aims to propose a support vector machine (SVM)-based deep learning system that will classify the data extracted from servers to ...
In this paper, we study a malware detection scheme for Android platform using an SVM-based approach, which integrates both risky permission combinations and ...
Missing: method virus.