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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 221–230 of 431 papers

TitleStatusHype
Unmasking the Shadows: Pinpoint the Implementations of Anti-Dynamic Analysis Techniques in Malware Using LLM—0
Unraveling the Key of Machine Learning Solutions for Android Malware Detection—0
Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks—0
Use of Multi-CNNs for Section Analysis in Static Malware Detection—0
Using Deep Neural Network for Android Malware Detection—0
Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks—0
Virus-MNIST: A Benchmark Malware Dataset—0
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment—0
WebEye - Automated Collection of Malicious HTTP Traffic—0
Word Embedding Techniques for Malware Evolution Detection—0
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