2021
DOI: 10.1007/978-3-030-87839-9_5
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Attacks on Visualization-Based Malware Detection: Balancing Effectiveness and Executability

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Cited by 1 publication
(4 citation statements)
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“…Benkraouda et al [20] proposes a framework that mixes a mask generator to highlight the bytes that are possible to manipulate while retaining executability, adversarial example generation using a Carlini-Wagner (CW) attack [47] and an optimization step that iteratively modifies the masked bytes by comparing the generated adversarial data to a set of known instructions. It fits the "Content Manipulation" category illustrated in Figure 2.…”
Section: Malware Injectionmentioning
confidence: 99%
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“…Benkraouda et al [20] proposes a framework that mixes a mask generator to highlight the bytes that are possible to manipulate while retaining executability, adversarial example generation using a Carlini-Wagner (CW) attack [47] and an optimization step that iteratively modifies the masked bytes by comparing the generated adversarial data to a set of known instructions. It fits the "Content Manipulation" category illustrated in Figure 2.…”
Section: Malware Injectionmentioning
confidence: 99%
“…Regarding the data used to evaluate the attacks, most of the works listed here used some sort of private dataset either by collecting samples from malware hosting services or expanding public ones-Benkraouda et al [20] merged malimg [11] and benign samples from the Architecture Object Code Dataset (AOCD) [49], while Khormali et al [19] used BIG 2015 [31] and also formed a private IoT dataset. A summary of the functionalitypreserving attacks can be found in Table 2.…”
Section: Malware Injectionmentioning
confidence: 99%
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