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 101–125 of 431 papers

TitleStatusHype
Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing—0
Classification under strategic adversary manipulation using pessimistic bilevel optimisation—0
A survey on practical adversarial examples for malware classifiers—0
Clipping free attacks against artificial neural networks—0
Clipping Free Attacks Against Neural Networks—0
Clustering based opcode graph generation for malware variant detection—0
Coda: An End-to-End Neural Program Decompiler—0
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection—0
Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection—0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
Analyzing Machine Learning Approaches for Online Malware Detection in Cloud—0
Contextual Weisfeiler-Lehman Graph Kernel For Malware Detection—0
A Survey on Malware Detection with Graph Representation Learning—0
A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection—0
A Survey on Cross-Architectural IoT Malware Threat Hunting—0
Counteracting Concept Drift by Learning with Future Malware Predictions—0
An investigation of a deep learning based malware detection system—0
Feature Extraction for Novelty Detection in Network Traffic—0
Analyzing CNN Based Behavioural Malware Detection Techniques on Cloud IaaS—0
Adversarial Perturbations Against Deep Neural Networks for Malware Classification—0
Investigating Feature and Model Importance in Android Malware Detection: An Implemented Survey and Experimental Comparison of ML-Based Methods—0
Data Augmentation for Opcode Sequence Based Malware Detection—0
A Survey of Malware Detection Using Deep Learning—0
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples—0
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS—0
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