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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 131–140 of 431 papers

TitleStatusHype
Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems—0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data—0
Adversarial Patterns: Building Robust Android Malware Classifiers—0
Transferable Cost-Aware Security Policy Implementation for Malware Detection Using Deep Reinforcement Learning—0
A short review on Applications of Deep learning for Cyber security—0
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization—0
Artificial Neural Network for Cybersecurity: A Comprehensive Review—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection—0
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