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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 271–280 of 431 papers

TitleStatusHype
Virus-MNIST: A Benchmark Malware Dataset—0
Identification of Significant Permissions for Efficient Android Malware Detection—0
Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain—0
A Non-Intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones—0
Robust Android Malware Detection System against Adversarial Attacks using Q-Learning—0
A novel DL approach to PE malware detection: exploring Glove vectorization, MCC_RCNN and feature fusion—0
Towards interpreting ML-based automated malware detection models: a survey—0
Towards Interpretable Ensemble Learning for Image-based Malware Detection—0
Echelon: Two-Tier Malware Detection for Raw Executables to Reduce False Alarms—0
Powershell malware detection method based on features combination—0
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