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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 71–80 of 431 papers

TitleStatusHype
RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization—0
Learning Temporal Invariance in Android Malware Detectors—0
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems—0
Integrating Explainable AI for Effective Malware Detection in Encrypted Network Traffic—0
Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines—0
Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network—0
Crystal ball: From innovative attacks to attack effectiveness classifierCode0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
Image-Based Malware Classification Using QR and Aztec Codes—0
Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity—0
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