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May 9, 2020 · The proposed approach overcomes the problems of static and dynamic techniques in malware detection. The novel classification approach senses all kinds of ...
The proposed approach overcomes the problems of static and dynamic techniques in malware detection. The novel classification approach senses all kinds of source ...
The proposed approach overcomes the problems of static and dynamic techniques in malware detection. The novel classification approach senses all kinds of source ...
An Improved Ensemble Based Machine Learning Technique for Efficient Malware Classification ... effective and efficient behavior-based Android malware detection ...
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In this paper, the authors have designed two methods based on ensemble learning and big data for improving the performance of malware detection at a large scale ...
Ensemble Learning for Effective Run-Time Hardware-Based ...
ieeexplore.ieee.org › abstract › document
The experimental results show that the proposed ensemble learning-based malware detection with just 2 HPCs using ensemble technique outperforms standard ...
Missing: Efficient | Show results with:Efficient
Dec 21, 2019 · In this paper, we introduce a malware detection model based on ensemble learning. The model is trained using the minimum number of signification ...
In this paper, we build a modified Two-hidden-layered Extreme Learning Machine (TELM), which uses the dependency of malware sequence elements.
This work proposes an efficient malware detection system based on deep learning that uses a reweighted class-balanced loss function in the final classification ...
A novel hybrid classification model for malware detection based on ensemble learning of three models, which enables us to improve malware detection accuracy.