Malware Detection is a significant part of endpoint security including workstations, servers, cloud instances, and mobile devices. Malware Detection is used to detect and identify malicious activities caused by malware. With the increase in the variety of malware activities on CMS based websites such as malicious malware redirects on WordPress site (Aka, WordPress Malware Redirect Hack) where the site redirects to spam, being the most widespread, the need for automatic detection and classifier amplifies as well. The signature-based Malware Detection system is commonly used for existing malware that has a signature but it is not suitable for unknown malware or zero-day malware
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With the rapid proliferation and increased sophistication of malicious software (malware), detection methods no longer rely only on manually generated signatures but have also incorporated more general approaches like machine learning detection.
First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs.
Sparse adversarial perturbations received much less attention in the literature compared to $l_2$- and $l_\infty$-attacks.
This paper proposes a generative adversarial network (GAN) based algorithm named MalGAN to generate adversarial malware examples, which are able to bypass black-box machine learning based detection models.
The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not.
In the experiments conducted on the novel test data set the accuracy was observed as 89. 23% for the integrated feature set which is 15% improvement on accuracy achieved with raw-feature set alone.
Our approach can check different safety properties and find concrete counterexamples for networks that are 10$\times$ larger than the ones supported by existing analysis techniques.
Recent work has shown that adversarial Windows malware samples - referred to as adversarial EXEmples in this paper - can bypass machine learning-based detection relying on static code analysis by perturbing relatively few input bytes.
The proposed DLMD technique uses both the byte and ASM files for feature engineering, thus classifying malware families.