With the widespread application of machine learning techniques in malware detection, researchers have proposed various adversarial attack methods to generate adversarial examples (AEs) of malware, thereby evading detection. Previous studies have shown ...

Static analysis is a crucial protection layer that enables modern antivirus systems to address the rampant proliferation of malware. These systems are increasingly relying on deep neural networks (DNNs) to automatically extract reliable features and ...

Targeted malware attacks are usually more purposeful and harmful than untargeted attacks, so it is important to perform the malware family classification. In classification tasks, convolutional neural networks (CNN) have shown superior ...

Modern cyber security systems increasingly rely on deep neural networks (DNNs) to tackle the surge in security threats. However, it is well-known that DNNs are vulnerable to adversarial examples. The state-of-the-art adversarial malware attacks ...

Deep neural networks are vulnerable to adversarial examples, which can fool deep models by adding subtle perturbations. Although existing attacks have achieved promising results, it still leaves a long way to go for generating ...