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, 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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We show in experiments that our method can attack a gradient-boosted machine learning model with evasion rates that are substantial and appear to be strongly dependent on the dataset.
First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs.
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.
A large body of research has focused on adversarial attacks which require to modify all input features with small $l_2$- or $l_\infty$-norms.
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.
However, deep learning is often criticized for its lack of robustness in adversarial settings (e. g., vulnerability to adversarial inputs) and general inability to rationalize its predictions.
This motivates us to investigate which kind of robustness the ensemble defense or effectiveness the ensemble attack can achieve, particularly when they combat with each other.
In this paper, we consider the problem of malware detection and classification based on image analysis.