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MDEA: Malware Detection with Evolutionary Adversarial Learning (2020)
Xiruo Wang
,
Risto Miikkulainen
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.
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Citation:
In
Proceedings of the 2020 IEEE Congress on Evolutionary Computation
, 2020.
Bibtex:
@inproceedings{wang:cec20, title={MDEA: Malware Detection with Evolutionary Adversarial Learning }, author={Xiruo Wang and Risto Miikkulainen}, booktitle={Proceedings of the 2020 IEEE Congress on Evolutionary Computation}, month={ }, url="http://www.cs.utexas.edu/users/ai-lab?wang:cec20", year={2020} }
Presentation:
Video
People
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
Xiruo Wang
Masters Alumni
kevinxrwang [at] utexas edu
Areas of Interest
Applications
Neuroevolution
Labs
Neural Networks