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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 271–280 of 431 papers

TitleStatusHype
RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization—0
SCORE: Syntactic Code Representations for Static Script Malware Detection—0
Security Evaluation of Support Vector Machines in Adversarial Environments—0
SEdroid: A Robust Android Malware Detector using Selective Ensemble Learning—0
Semantic Adversarial Deep Learning—0
SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using Natural Language Processing (SecureNLP)—0
Semi-supervised classification for dynamic Android malware detection—0
SeqNet: An Efficient Neural Network for Automatic Malware Detection—0
SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment—0
Leveraging Large Language Models to Detect npm Malicious Packages—0
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