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

Showing 221230 of 431 papers

TitleStatusHype
Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?Code1
DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems0
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of BytecodeCode1
ML-based IoT Malware Detection Under Adversarial Settings: A Systematic Evaluation0
Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights0
heterogeneous temporal graph transformer: an intelligent system for evolving android malware detectionCode1
Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints0
PDF-Malware: An Overview on Threats, Detection and Evasion Attacks0
Decision-forest voting scheme for classification of rare classes in network intrusion detection0
Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability0
Show:102550
← PrevPage 23 of 44Next →

No leaderboard results yet.