miscellaneous-7
Malware Detection
3260 papers • 126 benchmarks • 313 datasets
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
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Android Malware Dataset
Android Malware Dataset
MalNet
MalNet
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Most implemented papers
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
Weiwei Hu, Ying Tan•Sun Feb 19 2017
The superiority of MalGAN over traditional gradient based adversarial example generation algorithms is that MalGAN is able to decrease the detection rate to nearly zero and make the retraining based defensive method against adversarial examples hard to work.
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