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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 31–40 of 431 papers

TitleStatusHype
Learning Security Classifiers with Verified Global Robustness PropertiesCode1
Avast-CTU Public CAPE DatasetCode1
MASKDROID: Robust Android Malware Detection with Masked Graph RepresentationsCode1
Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge SettingCode1
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware DetectionCode1
Federated Learning for Malware Detection in IoT DevicesCode1
Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?Code1
HYDRA: A multimodal deep learning framework for malware classificationCode1
Data Augmentation Based Malware Detection using Convolutional Neural NetworksCode1
Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware DetectionCode1
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