Results 191 to 200 of about 4,236,332 (208)

Enhancing Android Malware Detection with XGBoost and Convolutional Neural Networks

open access: diamond
Atif Raza Zaidi   +5 more
openalex   +1 more source

Dynamic Prototype Network Based on Sample Adaptation for Few-Shot Malware Detection

IEEE Transactions on Knowledge and Data Engineering, 2023
The continuous increase and spread of malware have caused immeasurable losses to social enterprises and even the country, especially unknown malware. Most existing methods use predefined class samples to train models, which cannot handle unknown malware ...
Yuhan Chai   +4 more
semanticscholar   +1 more source

Deep Learning for Zero-day Malware Detection and Classification: A Survey

ACM Computing Surveys, 2023
Zero-day malware is malware that has never been seen before or is so new that no anti-malware software can catch it. This novelty and the lack of existing mitigation strategies make zero-day malware challenging to detect and defend against.
Fatemeh Deldar, M. Abadi
semanticscholar   +1 more source

A Knowledge Transfer-Based Semi-Supervised Federated Learning for IoT Malware Detection

IEEE Transactions on Dependable and Secure Computing, 2023
As the demand for Internet of Things (IoT) technologies continues to grow, IoT devices have been viable targets for malware infections. Although deep learning-based malware detection has achieved great success, the detection models are usually trained ...
Xin-jun Pei   +4 more
semanticscholar   +1 more source

MsDroid: Identifying Malicious Snippets for Android Malware Detection

IEEE Transactions on Dependable and Secure Computing, 2023
Machine learning has shown promise for improving the accuracy of Android malware detection in the literature. However, it is challenging to (1) stay robust towards real-world scenarios and (2) provide interpretable explanations for experts to analyse. In
Yiling He   +5 more
semanticscholar   +1 more source

Comprehensive Android Malware Detection Based on Federated Learning Architecture

IEEE Transactions on Information Forensics and Security, 2023
Android malware and its variants are a major challenge for mobile platforms. However, there are two main problems in the existing detection methods: $a$ ) The detection method lacks the evolution ability for Android malware, which leads to the low ...
Wenbo Fang   +7 more
semanticscholar   +1 more source

API2Vec: Learning Representations of API Sequences for Malware Detection

International Symposium on Software Testing and Analysis, 2023
Analyzing malware based on API call sequence is an effective approach as the sequence reflects the dynamic execution behavior of malware.Recent advancements in deep learning have led to the application of these techniques for mining useful information ...
Lei Cui   +5 more
semanticscholar   +1 more source

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