MDEA : malware detection with evolutionary adversarial learning

Many applications have used machine learning as a tool to detect malware. These applications take in raw or processed binary data to feed neural network models to classify benign or malicious files. Even though this approach has proved effective against dynamic changes, such as encrypting, obfuscating and packing techniques, it is vulnerable to specific evasion attacks to where that small changes to the input data cause misclassification at test time. In this paper, I propose MDEA, an Adversarial Malware Detection model that combines a neural network and evolutionary optimization attack samples to make the network robust against evasion attacks. By retraining the model with the evolved malware samples, network performance improves a big margin.

Name thesis:degree_name

Master of Science in Computer Sciences

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Computer Science

Grantor

The University of Texas at Austin

Year dc:date.issued

2019

Author dc:creator

• Wang, Xiruo

Advisor dc:contributor.advisor

• Miikkulainen, Risto

• Evolutionary algorithm
• Malware detection

Language dc:language.iso

en

Identifier URI

http://dx.doi.org/10.26153/tsw/7424

OAI identifier oai:identifier

oai:repositories.lib.utexas.edu:2152/80408