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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 401–410 of 431 papers

TitleStatusHype
Creating Valid Adversarial Examples of MalwareCode0
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved FeaturesCode0
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial AttacksCode0
An Efficient Approach For Malware Detection Using PE Header SpecificationCode0
Convolutional Neural Network for Classification of Malware Assembly CodeCode0
Multitask Learning for Network Traffic ClassificationCode0
A learning model to detect maliciousness of portable executable using integrated feature setCode0
Black-Box Attacks against RNN based Malware Detection AlgorithmsCode0
Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware DetectionCode0
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware DetectionCode0
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