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 21–30 of 431 papers

TitleStatusHype
Imbalanced malware classification: an approach based on dynamic classifier selectionCode0
Leveraging VAE-Derived Latent Spaces for Enhanced Malware Detection with Machine Learning Classifiers—0
BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection—0
Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection—0
CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security DataCode1
Malware Detection at the Edge with Lightweight LLMs: A Performance Evaluation—0
LAMD: Context-driven Android Malware Detection and Classification with LLMs—0
Malware Detection based on API callsCode0
Recent Advances in Malware Detection: Graph Learning and Explainability—0
RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization—0
Show:102550
← PrevPage 3 of 44Next →

No leaderboard results yet.