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Neural Classification of Malware-As-Video with Considerations for In-Hardware Inferencing

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University of Cincinnati Cincinnati United States

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The objective of this thesis is to explore the classification of assembly code as benign or malicious through the use of neural networks, and while building these networks, giving consideration to the creation of malware detecting hardware. Neural networks have become a go-to solution in many fields due to their ability to learn from an enormous number of features. Fully entrusting security to a neural network may be unwise due to issues with bias in training data and the ultimately unknowable nature of how the network makes a classification. If a proficient system is achieved for low cost in terms of memory or time, however, it could be another tool in the toolbox for fighting malware.

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Technical Report,17 Mar 2019,26 Apr 2019



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Approved For Public Release;

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