1811, 2020

ROOTs 2020: A survey on practical adversarial examples for malware classifiers – Daniel Park

Sanna/ November 18, 2020/ ROOTS

Machine learning based models have proven to be effective in a variety of problem spaces, especially in malware detection and classification. However, with the discovery of deep learning models’ vulnerability to adversarial perturbations, a new attack has been developed against these models. The first attacks based on adversarial example research focused on generating feature vectors, but more recent research shows it is possible to generate evasive malware samples. In this talk, I will discuss several attacks that have been developed against machine learning based malware classifiers that leverage adversarial perturbations to develop an adversarial malware example. Adversarial malware examples differ from adversarial examples in the natural image domain in that they must retain the original malicious program logic in addition to evading detection or classification. Adversarial machine learning has become increasingly popular and is

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2211, 2019

ROOTS 2019 Talk: Shallow Security: on the Creation of Adversarial Variants to Evade ML-Based Malware Detectors – Fabricio Ceschin

Sanna/ November 22, 2019/ ROOTS

The use of Machine Learning (ML) techniques for malware detection has been a trend in the last two decades. More recently, researchers started to investigate adversarial approaches to bypass these ML-based malware detectors. Adversarial attacks became so popular that a large Internet company (ENDGAME Inc.) has launched a public challenge to encourage researchers to bypass their (three) ML-based static malware detectors. Our research group teamed to participate in this challenge in August/2019 and accomplishing the bypass of all 150 tests proposed by the company. To do so, we implemented an automatic exploitation method which moves the original malware binary sections to resources and includes new chunks of data to it to create adversarial samples that not only bypassed their ML detectors, but also real AV engines as well (with a lower detection rate than

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