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 76–100 of 431 papers

TitleStatusHype
Accelerating Malware Classification: A Vision Transformer SolutionCode0
Evading Malware Classifiers via Monte Carlo Mutant Feature DiscoveryCode0
How to 0wn the NAS in Your Spare TimeCode0
Detecting DGA domains with recurrent neural networks and side informationCode0
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware DetectionCode0
DeepXplore: Automated Whitebox Testing of Deep Learning SystemsCode0
Deep Transfer Learning for Static Malware ClassificationCode0
DetectBERT: Towards Full App-Level Representation Learning to Detect Android MalwareCode0
Fast & Furious: Modelling Malware Detection as Evolving Data StreamsCode0
Evaluating Explanation Methods for Deep Learning in SecurityCode0
Deep learning at the shallow end: Malware classification for non-domain expertsCode0
Classification with Costly Features in Hierarchical Deep SetsCode0
Adversarially Robust Learning with Optimal Transport Regularized DivergencesCode0
Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput ComputingCode0
Behavioural Reports of Multi-Stage MalwareCode0
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep LearningCode0
Improving Malware Detection Accuracy by Extracting Icon InformationCode0
DeepSign: Deep Learning for Automatic Malware Signature Generation and ClassificationCode0
Dynamic Malware Analysis with Feature Engineering and Feature LearningCode0
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced DatasetsCode0
Creating Valid Adversarial Examples of MalwareCode0
Adversarial Feature Selection against Evasion AttacksCode0
Crystal ball: From innovative attacks to attack effectiveness classifierCode0
ALOHA: Auxiliary Loss Optimization for Hypothesis AugmentationCode0
A learning model to detect maliciousness of portable executable using integrated feature setCode0
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