2018 · 109 citations · 19 references
ComputingComputer ScienceArtificial IntelligenceComputer SecurityMachine LearningDeep LearningMalware ClassificationNeural NetworksPattern RecognitionRepresentation LearningVirus ClassificationClassical Machine LearningDeep Learning For Malware ClassificationDeep Learning-based DetectionDeep Learning-based Malware DetectionMachine Learning For Malware ClassificationMachine Learning-based Malware DetectionNeural Machine TranslationRandom Forest Classification
Recently, Deep Learning has been showing promising results in various Artificial Intelligence applications like image recognition, natural language processing, language modeling, neural machine translation, etc. Although, in general, it is computationally more expensive as compared to classical machine learning techniques, their results are found to be more effective in some cases. Therefore, in this paper, we investigated and compared one of the Deep Learning Architecture called Deep Neural Network (DNN) with the classical Random Forest (RF) machine learning algorithm for the malware classification. We studied the performance of the classical RF and DNN with 2, 4 & 7 layers architectures with the four different feature sets, and found that irrespective of the features inputs, the classical RF accuracy outperforms the DNN.
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Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
Haibo He, Yang Bai, Edwardo A. Garcia et al. · 2008 · 4.4K citations
A Tutorial on the Cross-Entropy Method
Pieter-Tjerk de Boer, Dirk P. Kroese, Shie Mannor et al. · Annals of Operations Research · 2005 · 3K citations · Full text
Convergence Results for Neural Networks via Electrodynamics
arXiv (Cornell University) · 2018 · 2.9K citations · Full text
Computing, Computer Science, Physics +13