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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 101–125 of 431 papers

TitleStatusHype
Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!—0
Discovering Malicious Signatures in Software from Structural Interactions—0
Towards an in-depth detection of malware using distributed QCNN—0
Android Malware Detection with Unbiased Confidence Guarantees—0
A Malware Classification Survey on Adversarial Attacks and Defences—0
Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4Code1
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion AttacksCode1
Explaining high-dimensional text classifiers—0
Machine learning-based malware detection for IoT devices using control-flow data—0
Enhancing Malware Detection by Integrating Machine Learning with Cuckoo Sandbox—0
Enhancing Enterprise Network Security: Comparing Machine-Level and Process-Level Analysis for Dynamic Malware Detection—0
Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection—0
The Efficacy of Transformer-based Adversarial Attacks in Security Domains—0
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations—0
On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors—0
Nebula: Self-Attention for Dynamic Malware AnalysisCode1
Efficient Concept Drift Handling for Batch Android Malware Detection ModelsCode0
Adversarially Robust Learning with Optimal Transport Regularized DivergencesCode0
Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge SettingCode1
The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement LearningCode0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices—0
Optimized Deep Learning Models for Malware Detection under Concept Drift—0
A Comparison of Adversarial Learning Techniques for Malware Detection—0
A Feature Set of Small Size for the PDF Malware Detection—0
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