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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 51–75 of 431 papers

TitleStatusHype
Adapting Novelty towards Generating Antigens for Antivirus systems—0
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
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
Learning Temporal Invariance in Android Malware Detectors—0
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems—0
Integrating Explainable AI for Effective Malware Detection in Encrypted Network Traffic—0
Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines—0
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