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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 201–225 of 431 papers

TitleStatusHype
AI-based Malware and Ransomware Detection Models—0
PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection—0
Parallel Instance Filtering for Malware Detection—0
Multifamily Malware Models—0
Malware Detection and Prevention using Artificial Intelligence Techniques—0
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective—0
When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic ClassificationCode0
On the impact of dataset size and class imbalance in evaluating machine-learning-based windows malware detection techniques—0
Generative Adversarial Networks and Image-Based Malware Classification—0
Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection—0
Support Vector Machines under Adversarial Label Contamination—0
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware DetectionCode0
BagFlip: A Certified Defense against Data PoisoningCode0
Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine LearningCode0
Fast & Furious: Modelling Malware Detection as Evolving Data StreamsCode0
A two-steps approach to improve the performance of Android malware detectors—0
SeqNet: An Efficient Neural Network for Automatic Malware Detection—0
SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment—0
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive ConditionsCode0
A Natural Language Processing Approach for Instruction Set Architecture Identification—0
Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection—0
Deep Image: A precious image based deep learning method for online malware detection in IoT Environment—0
MERLIN -- Malware Evasion with Reinforcement LearnINg—0
Toward the Detection of Polyglot Files—0
A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection—0
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