Results 151 to 160 of about 707,343 (186)

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

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, Yiping Li, Lei Wu, Ziqi Yang, Kui Ren, Zhan Qin   +5 more
semanticscholar   +1 more source

Machine Learning-Powered Malware Detection in Encrypted IoT Traffic

2025 IEEE 101st Vehicular Technology Conference (VTC2025-Spring)
The exponential growth of encrypted network traffic in IoT ecosystems has created a critical challenge: maintaining privacy while enabling effective malware detection.
Arshad Farhad, Muhammad Irfan Khan, A. Sodhro, Muhammad Khurram Ehsan, Fatiha Djebbar   +4 more
semanticscholar   +1 more source

Intelligent Malware Detection: Enhancing Accuracy with Recurrent Neural Network

2025 8th International Conference on Trends in Electronics and Informatics (ICOEI)
Aim: The purpose of this research is to use Artificial Neural Networks (ANN) to create an intelligent malware detection system and evaluate how well it performs in comparison to ClamA V, a conventional antivirus program that relies on signatures.
C. A. Kandasamy, P. Chandru, J. K. Kanimozhi, E. Loganathan, N. Prakash, A. Yoganathan   +5 more
semanticscholar   +1 more source

Malware Detection with LLM

Karlsruhe Institute of Technology - Proceedings
The increasing sophistication of malware poses critical challenges to traditional detection techniques, particularly in the face of polymorphic and evasive threats. Recent advances in Natural Language Processing (NLP), specifically through the deployment
M. Adamec, M. Turčaník
semanticscholar   +1 more source

Multi-Perspective Analysis Integrating API and DLL Features for Malware Detection

International Conference on the Software Process
API has become one of the important means for malware detection due to its numerous features that are beneficial for such detection. Besides API sequences, the DLL called during software runtime can also serve as a means to assist in malware detection ...
Anyang Yin, Hongjiao Li, Ming Jin
semanticscholar   +1 more source

Enhancing Malware Detection and Analysis using Deep Learning and Explainable AI (XAI)

International Journal of Network Security & Its Applications
The rising complexity of malware threats has raised significant concerns within the antimalware community. The rapid evolution of cyber threats, particularly malware, is one of the most dangerous cybercrimes for online users due to its fast speed and ...
S. Alajmani, Ebtihal Aljuaid, Ben Soh, Raneem Y. Alyami   +3 more
semanticscholar   +1 more source

Context-Aware Natural Language Processing for Malware Detection

Silicon Valley Cybersecurity Conference
As malware continues to evolve and cyber attacks become increasingly prevalent, it is critical to develop effective malware classification techniques for the detection and prevention of such malicious attacks.
Helen Liu, Summer McCune, Quang Duy Tran, Fabio Di Troia, Younghee Park   +4 more
semanticscholar   +1 more source

Malware Detection Using Transformers-based Model GPT-2

2021
Zararlı içeriğin çeşitliliği, karmaşıklığının yanı sıra Bilgi ve İletişim Teknolojilerinin (BİT) son kullanıcılarını önemli ölçüde etkilemiştir. Zararlı içeriğin etkisini azaltmak, kullanıcı sistemlerini zararlı yazılımlara karşı proaktif olarak savunmak için otomatikleştirilmiş makine öğrenme teknikleri geliştirildi.
openaire   +1 more source

Advancing Malware Detection in Network Traffic With Self-Paced Class Incremental Learning

IEEE Internet of Things Journal
Ensuring network security, effective malware detection (MD) is of paramount importance. Traditional methods often struggle to accurately learn and process the characteristics of network traffic data, and must balance rapid processing with retaining ...
Xiaohu Xu, Xixi Zhang, Qianyun Zhang, Yu Wang, B. Adebisi, Tomoaki Ohtsuki, Hikmet Sari, Guan Gui   +7 more
semanticscholar   +1 more source