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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

Showing 3140 of 431 papers

TitleStatusHype
Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware DetectionCode1
Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware DetectionCode1
Probabilistic Jacobian-based Saliency Maps AttacksCode1
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware DetectionCode1
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacksCode1
HYDRA: A multimodal deep learning framework for malware classificationCode1
NetML: A Challenge for Network Traffic AnalyticsCode1
Why an Android App is Classified as Malware? Towards Malware Classification InterpretationCode1
A Framework for Enhancing Deep Neural Networks Against Adversarial MalwareCode1
Mind Your Weight(s): A Large-scale Study on Insufficient Machine Learning Model Protection in Mobile AppsCode1
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