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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 351–375 of 431 papers

TitleStatusHype
On the impact of dataset size and class imbalance in evaluating machine-learning-based windows malware detection techniques—0
On the Security Risks of ML-based Malware Detection Systems: A Survey—0
OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques—0
Open Image Content Disarm And Reconstruction—0
Optimization of Lightweight Malware Detection Models For AIoT Devices—0
Optimized Approaches to Malware Detection: A Study of Machine Learning and Deep Learning Techniques—0
ORSA: Outlier Robust Stacked Aggregation for Best- and Worst-Case Approximations of Ensemble Systems\—0
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks—0
Parallel Instance Filtering for Malware Detection—0
PDF-Malware: An Overview on Threats, Detection and Evasion Attacks—0
PECAN: A Deterministic Certified Defense Against Backdoor Attacks—0
PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection—0
Powershell malware detection method based on features combination—0
Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware—0
Practical Fast Gradient Sign Attack against Mammographic Image Classifier—0
Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines—0
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections—0
Randomized Prediction Games for Adversarial Machine Learning—0
Recent Advances in Malware Detection: Graph Learning and Explainability—0
Efficient Formal Safety Analysis of Neural NetworksCode0
Efficient Concept Drift Handling for Batch Android Malware Detection ModelsCode0
Malware Detection based on API callsCode0
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep LearningCode0
Interpreting Machine Learning Malware Detectors Which Leverage N-gram AnalysisCode0
Dynamic Malware Analysis with Feature Engineering and Feature LearningCode0
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