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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 121–130 of 431 papers

TitleStatusHype
Investigating Feature and Model Importance in Android Malware Detection: An Implemented Survey and Experimental Comparison of ML-Based Methods—0
Data Augmentation for Opcode Sequence Based Malware Detection—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples—0
Decentralised firewall for malware detection—0
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
A Survey of Malware Detection Using Deep Learning—0
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS—0
Analysis of Bayesian Classification based Approaches for Android Malware Detection—0
Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems—0
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