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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 51–60 of 431 papers

TitleStatusHype
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved FeaturesCode0
Efficient Formal Safety Analysis of Neural NetworksCode0
An Efficient Approach For Malware Detection Using PE Header SpecificationCode0
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware DetectionCode0
Evaluating Explanation Methods for Deep Learning in SecurityCode0
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware DetectionCode0
Dynamic Malware Analysis with Feature Engineering and Feature LearningCode0
Detecting DGA domains with recurrent neural networks and side informationCode0
DetectBERT: Towards Full App-Level Representation Learning to Detect Android MalwareCode0
Efficient Concept Drift Handling for Batch Android Malware Detection ModelsCode0
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