SOTAVerified

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 151–175 of 431 papers

TitleStatusHype
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
Efficient Concept Drift Handling for Batch Android Malware Detection ModelsCode0
Adversarially Robust Learning with Optimal Transport Regularized DivergencesCode0
The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement LearningCode0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Optimized Deep Learning Models for Malware Detection under Concept Drift—0
Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices—0
A Comparison of Adversarial Learning Techniques for Malware Detection—0
A Feature Set of Small Size for the PDF Malware Detection—0
LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning—0
Open Image Content Disarm And Reconstruction—0
Hidden Markov Models with Random Restarts vs Boosting for Malware Detection—0
ATWM: Defense against adversarial malware based on adversarial training—0
A Natural Language Processing Approach to Malware Classification—0
Review of Deep Learning-based Malware Detection for Android and Windows System—0
From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy—0
Creating Valid Adversarial Examples of MalwareCode0
On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities—0
A Survey on Cross-Architectural IoT Malware Threat Hunting—0
Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features—0
FGAM:Fast Adversarial Malware Generation Method Based on Gradient Sign—0
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses—0
Survey of Malware Analysis through Control Flow Graph using Machine Learning—0
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