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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 201–210 of 431 papers

TitleStatusHype
Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity—0
Generative Adversarial Networks for Malware Detection: a Survey—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors—0
Graph Neural Network-based Android Malware Classification with Jumping Knowledge—0
HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and Statistical Analysis—0
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses—0
HeNet: A Deep Learning Approach on Intel^ Processor Trace for Effective Exploit Detection—0
Heterogeneous Graph Matching Networks—0
A Malware Classification Survey on Adversarial Attacks and Defences—0
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