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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 101–110 of 431 papers

TitleStatusHype
Creating Valid Adversarial Examples of MalwareCode0
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity DetectionCode0
A learning model to detect maliciousness of portable executable using integrated feature setCode0
Crystal ball: From innovative attacks to attack effectiveness classifierCode0
Deep learning at the shallow end: Malware classification for non-domain expertsCode0
Malware Detection based on API callsCode0
A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion—0
A Non-Intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones—0
AiDroid: When Heterogeneous Information Network Marries Deep Neural Network for Real-time Android Malware Detection—0
An MDL-Based Classifier for Transactional Datasets with Application in Malware Detection—0
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