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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 361–370 of 431 papers

TitleStatusHype
Exploring Adversarial Examples in Malware Detection—0
Detecting DGA domains with recurrent neural networks and side informationCode0
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content—0
Statistical Estimation of Malware Detection Metrics in the Absence of Ground TruthCode0
Efficient Formal Safety Analysis of Neural NetworksCode0
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples—0
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection—0
An investigation of a deep learning based malware detection system—0
apk2vec: Semi-supervised multi-view representation learning for profiling Android applications—0
Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks—0
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