62 papers with code • 1 benchmarks • 2 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
A generative network is trained to minimize the generated adversarial examples' malicious probabilities predicted by the substitute detector.
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.
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.
While conventional signature and token based methods for malware detection do not detect a majority of new variants for existing malware, the results presented in this paper show that signatures generated by the DBN allow for an accurate classification of new malware variants.
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.
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.