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MDEA: Malware Detection with Evolutionary Adversarial Learning | IEEE Conference Publication | IEEE Xplore

MDEA: Malware Detection with Evolutionary Adversarial Learning


Abstract:

Malware detection have used machine learning to detect malware in programs. These applications take in raw or processed binary data to neural network models to classify a...Show More

Abstract:

Malware detection have used machine learning to detect malware in programs. These applications take in raw or processed binary data to neural network models to classify as benign or malicious files. Even though this approach has proven effective against dynamic changes, such as encrypting, obfuscating and packing techniques, it is vulnerable to specific evasion attacks where that small changes in the input data cause misclassification at test time. This paper proposes a new approach: MDEA, an Adversarial Malware Detection model uses evolutionary optimization to create attack samples to make the network robust against evasion attacks. By retraining the model with the evolved malware samples, its performance improves a significant margin.
Date of Conference: 19-24 July 2020
Date Added to IEEE Xplore: 03 September 2020
ISBN Information:
Conference Location: Glasgow, UK

I. Introduction

The high proliferation of and dependence on computing resources in daily life has greatly increased the potential of malware to harm consumers [1]. It is estimated that almost one in four computers operating in the U.S. were already infected by malware in 2008 [2] and according to Kaspersky Lab, up to one billion dollars was stolen from financial institutions worldwide due to malware attacks in 2015 [3]. More recently, the notorious and widespread NotPetya ransomware attack is estimated to have caused $ 10 billion dollars in damages worldwide. Even worse, as reported by McAfee Labs, the diversity of malware is still evolving in expanding areas such that in Q1 2018, on average, five new malware samples were generated per second [4]. As a specific example, total coin miner malware rose by 629% in Q1 to more than 2.9 million samples in 2018 [4].

References

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