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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 221–230 of 431 papers

TitleStatusHype
Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection—0
Deep Image: A precious image based deep learning method for online malware detection in IoT Environment—0
MERLIN -- Malware Evasion with Reinforcement LearnINg—0
Toward the Detection of Polyglot Files—0
A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection—0
Adversarial Patterns: Building Robust Android Malware Classifiers—0
MaMaDroid2.0 -- The Holes of Control Flow GraphsCode0
Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations—0
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks—0
StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection—0
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