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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 41–50 of 431 papers

TitleStatusHype
Learning from Context: Exploiting and Interpreting File Path Information for Better Malware DetectionCode1
Learning Security Classifiers with Verified Global Robustness PropertiesCode1
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified RobustnessCode1
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion AttacksCode1
AiDroid: When Heterogeneous Information Network Marries Deep Neural Network for Real-time Android Malware Detection—0
A Hierarchical Convolutional Neural Network for Malware Classification—0
Agent-based Vs Agent-less Sandbox for Dynamic Behavioral Analysis—0
Feature Extraction for Novelty Detection in Network Traffic—0
Adaptive and Scalable Android Malware Detection through Online Learning—0
A Feature Set of Small Size for the PDF Malware Detection—0
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