Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection
This work addresses malware detection for cybersecurity, but it is incremental as it applies existing methods to a specific domain without introducing new techniques.
The paper compared Deep Neural Networks (DNN) with Random Forest (RF) for malware classification, finding that RF consistently outperformed DNN across different feature sets and layer architectures.
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.