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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 421–430 of 431 papers

TitleStatusHype
A New Android Malware Detection Approach Using Bayesian Classification—0
Android Malware Detection Using Parallel Machine Learning Classifiers—0
N-opcode Analysis for Android Malware Classification and Categorization—0
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large GraphsCode1
Adaptive and Scalable Android Malware Detection through Online Learning—0
Contextual Weisfeiler-Lehman Graph Kernel For Malware Detection—0
Adversarial Perturbations Against Deep Neural Networks for Malware Classification—0
Feature Cross-Substitution in Adversarial Classification—0
Knowledge Engineering for Planning-Based Hypothesis Generation—0
Security Evaluation of Support Vector Machines in Adversarial Environments—0
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