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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 141–150 of 431 papers

TitleStatusHype
Android Security using NLP Techniques: A Review—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Android Malware Detection with Unbiased Confidence Guarantees—0
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
Android Malware Detection Using Parallel Machine Learning Classifiers—0
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations—0
Android Malware Detection Using Machine Learning on Image Patterns—0
Adversary Resistant Deep Neural Networks with an Application to Malware Detection—0
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