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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 171–180 of 431 papers

TitleStatusHype
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
ATWM: Defense against adversarial malware based on adversarial training—0
Efficient Malware Analysis Using Metric Embeddings—0
Enhanced Attacks on Defensively Distilled Deep Neural Networks—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
A Natural Language Processing Approach for Instruction Set Architecture Identification—0
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective—0
Enhancing Robustness of Neural Networks through Fourier Stabilization—0
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning—0
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes—0
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