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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–431 of 431 papers

TitleStatusHype
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers—0
BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection—0
Traffic Refinery: Cost-Aware Data Representation for Machine Learning on Network Traffic—0
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing—0
Classification under strategic adversary manipulation using pessimistic bilevel optimisation—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
Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version)—0
Contextual Weisfeiler-Lehman Graph Kernel For Malware Detection—0
Counteracting Concept Drift by Learning with Future Malware Predictions—0
Cross-Language Binary-Source Code Matching with Intermediate Representations—0
Data Augmentation for Opcode Sequence Based Malware Detection—0
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples—0
Decentralised firewall for malware detection—0
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
Decoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach—0
Deep Image: A precious image based deep learning method for online malware detection in IoT Environment—0
Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges—0
Deep Multi-Task Learning for Malware Image Classification—0
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems—0
Design of secure and robust cognitive system for malware detection—0
Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid—0
Show:102550
← PrevPage 9 of 9Next →

No leaderboard results yet.