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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 91–100 of 431 papers

TitleStatusHype
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep LearningCode0
Improving Malware Detection Accuracy by Extracting Icon InformationCode0
DeepSign: Deep Learning for Automatic Malware Signature Generation and ClassificationCode0
Dynamic Malware Analysis with Feature Engineering and Feature LearningCode0
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced DatasetsCode0
Creating Valid Adversarial Examples of MalwareCode0
Adversarial Feature Selection against Evasion AttacksCode0
Crystal ball: From innovative attacks to attack effectiveness classifierCode0
ALOHA: Auxiliary Loss Optimization for Hypothesis AugmentationCode0
A learning model to detect maliciousness of portable executable using integrated feature setCode0
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