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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
Multifamily Malware Models—0
Natural Language Outlines for Code: Literate Programming in the LLM Era—0
Optimized Deep Learning Models for Malware Detection under Concept Drift—0
New Approach to Malware Detection Using Optimized Convolutional Neural Network—0
Benchmark Static API Call Datasets for Malware Family Classification—0
New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning—0
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification—0
N-gram Opcode Analysis for Android Malware Detection—0
N-opcode Analysis for Android Malware Classification and Categorization—0
NtMalDetect: A Machine Learning Approach to Malware Detection Using Native API System Calls—0
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