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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 121–130 of 431 papers

TitleStatusHype
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices—0
Optimized Deep Learning Models for Malware Detection under Concept Drift—0
A Comparison of Adversarial Learning Techniques for Malware Detection—0
A Feature Set of Small Size for the PDF Malware Detection—0
LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning—0
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model PerformanceCode1
Open Image Content Disarm And Reconstruction—0
Hidden Markov Models with Random Restarts vs Boosting for Malware Detection—0
ATWM: Defense against adversarial malware based on adversarial training—0
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