Survey on the Usage of Machine Learning Techniques for Malware Analysis
By Daniele Ucci, Leonardo Aniello, Roberto Baldoni. Computers & security, 2017
361 citations recorded by the source corpus.
Coping with malware is getting more and more challenging, given their relentless growth in complexity and volume. One of the most common approaches in literature is using machine learning techniques, to automatically learn models and patterns behind such complexity, and to develop technologies for keeping pace with the speed of development of novel malware. This survey aims at providing an overview on the way machine learning has been used so far in the context of malware analysis. We systematize surveyed papers according to their objectives (i.e., the expected output, what the analysis aims to), what information about malware they specifically use (i.e., the features), and what machine learning techniques they employ (i.e., what algorithm is used to process the input and produce the output). We also outline a number of problems concerning the datasets used in considered works, and finally introduce the novel concept of malware analysis economics, regarding the study of existing tradeoffs among key metrics, such as analysis accuracy and economical costs.
Subfields: Computer Networks and Communications, Information Systems, Signal Processing
View Paper: https://www.semanticscholar.org/paper/66a932c0273a9d21c24e38161fed0a8f3bde3322
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In Computer Networks and Communications
In Signal Processing
Citations in the corpus
- Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, methodology): “5 [17] 2018 N/A N/A √ ≈ N/A N/A √ √ ≈ N/A √”