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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 411–420 of 431 papers

TitleStatusHype
Generating Adversarial Malware Examples for Black-Box Attacks Based on GANCode0
A learning model to detect maliciousness of portable executable using integrated feature setCode0
N-gram Opcode Analysis for Android Malware Detection—0
"Influence Sketching": Finding Influential Samples In Large-Scale Regressions—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
Adversary Resistant Deep Neural Networks with an Application to Malware Detection—0
One-Class SVM with Privileged Information and its Application to Malware Detection—0
Randomized Prediction Games for Adversarial Machine Learning—0
Analysis of Bayesian Classification based Approaches for Android Malware Detection—0
High Accuracy Android Malware Detection Using Ensemble Learning—0
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