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Malware Detection

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

Source: The Threat of Adversarial Attacks on Machine Learning in Network Security - A Survey

Papers

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Showing 191–200 of 431 papers

TitleStatusHype
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples—0
GLSearch: Maximum Common Subgraph Detection via Learning to Search—0
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations—0
Feature Cross-Substitution in Adversarial Classification—0
Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors—0
FGAM:Fast Adversarial Malware Generation Method Based on Gradient Sign—0
Flexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor—0
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking—0
From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy—0
Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity—0
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