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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
Survey of Malware Analysis through Control Flow Graph using Machine Learning—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
A Survey on Malware Detection with Graph Representation Learning—0
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified RobustnessCode1
PAD: Towards Principled Adversarial Malware Detection Against Evasion AttacksCode1
MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-based Malware Detection—0
Generative Adversarial Networks for Malware Detection: a Survey—0
Sequential Embedding-based Attentive (SEA) classifier for malware classificationCode0
Continuous Learning for Android Malware DetectionCode1
Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection—0
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