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 126–150 of 431 papers

TitleStatusHype
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection—0
A New Android Malware Detection Approach Using Bayesian Classification—0
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection—0
Coda: An End-to-End Neural Program Decompiler—0
A Neural-based Program Decompiler—0
Clustering based opcode graph generation for malware variant detection—0
Clipping Free Attacks Against Neural Networks—0
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content—0
Clipping free attacks against artificial neural networks—0
A Feature Set of Small Size for the PDF Malware Detection—0
Adaptive and Scalable Android Malware Detection through Online Learning—0
Classification under strategic adversary manipulation using pessimistic bilevel optimisation—0
Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing—0
Android Security using NLP Techniques: A Review—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Android Malware Detection with Unbiased Confidence Guarantees—0
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
Android Malware Detection Using Parallel Machine Learning Classifiers—0
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations—0
Android Malware Detection Using Machine Learning on Image Patterns—0
Adversary Resistant Deep Neural Networks with an Application to Malware Detection—0
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