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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
Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid—0
A Review on The Use of Deep Learning in Android Malware Detection—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
Discovering Malicious Signatures in Software from Structural Interactions—0
Distinguishability of Adversarial Examples—0
DL-Droid: Deep learning based android malware detection using real devices—0
Design of secure and robust cognitive system for malware detection—0
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