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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 301–310 of 431 papers

TitleStatusHype
Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights—0
Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection—0
MalPhase: Fine-Grained Malware Detection Using Network Flow Data—0
MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-based Malware Detection—0
Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability—0
AI-based Malware and Ransomware Detection Models—0
Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network—0
Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices—0
Malware Classification Using Long Short-Term Memory Models—0
Malware Classification with GMM-HMM Models—0
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