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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 91–100 of 431 papers

TitleStatusHype
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers—0
BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection—0
Traffic Refinery: Cost-Aware Data Representation for Machine Learning on Network Traffic—0
Android Malware Detection using Feature Ranking of Permissions—0
Android Malware Detection Using Machine Learning on Image Patterns—0
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Analyzing Machine Learning Approaches for Online Malware Detection in Cloud—0
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