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 1–25 of 431 papers

TitleStatusHype
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Empirical Quantification of Spurious Correlations in Malware Detection—0
Network Threat Detection: Addressing Class Imbalanced Data with Deep Forest—0
EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware ClassifiersCode2
System Calls for Malware Detection and Classification: Methodologies and Applications—0
Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion—0
MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation—0
Adapting Novelty towards Generating Antigens for Antivirus systems—0
LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift AnalysisCode1
Malware families discovery via Open-Set Recognition on Android manifest permissions—0
On the Security Risks of ML-based Malware Detection Systems: A Survey—0
Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data—0
Evaluating the Robustness of Adversarial Defenses in Malware Detection SystemsCode0
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced DatasetsCode0
Dual Explanations via Subgraph Matching for Malware Detection—0
Optimized Approaches to Malware Detection: A Study of Machine Learning and Deep Learning Techniques—0
On the Consistency of GNN Explanations for Malware Detection—0
OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques—0
Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering—0
Malware Detection in Docker Containers: An Image is Worth a Thousand Logs—0
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
Show:102550
← PrevPage 1 of 18Next →

No leaderboard results yet.