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 51–75 of 431 papers

TitleStatusHype
How to Train your Antivirus: RL-based Hardening through the Problem-SpaceCode0
An Efficient Approach For Malware Detection Using PE Header SpecificationCode0
How to 0wn NAS in Your Spare TimeCode0
How to 0wn the NAS in Your Spare TimeCode0
Fast & Furious: Modelling Malware Detection as Evolving Data StreamsCode0
Evasion Attacks against Machine Learning at Test TimeCode0
Evading Malware Classifiers via Monte Carlo Mutant Feature DiscoveryCode0
Improving Malware Detection Accuracy by Extracting Icon InformationCode0
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware DetectionCode0
Evaluating the Robustness of Adversarial Defenses in Malware Detection SystemsCode0
Generating Adversarial Malware Examples for Black-Box Attacks Based on GANCode0
Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN PerformanceCode0
Efficient Concept Drift Handling for Batch Android Malware Detection ModelsCode0
Dynamic Malware Analysis with Feature Engineering and Feature LearningCode0
Efficient Formal Safety Analysis of Neural NetworksCode0
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware DetectionCode0
Evaluating Explanation Methods for Deep Learning in SecurityCode0
DetectBERT: Towards Full App-Level Representation Learning to Detect Android MalwareCode0
DeepXplore: Automated Whitebox Testing of Deep Learning SystemsCode0
Detecting DGA domains with recurrent neural networks and side informationCode0
DeepSign: Deep Learning for Automatic Malware Signature Generation and ClassificationCode0
Accelerating Malware Classification: A Vision Transformer SolutionCode0
Deep Transfer Learning for Static Malware ClassificationCode0
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep LearningCode0
Deep learning at the shallow end: Malware classification for non-domain expertsCode0
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