Zero-Day Malware Detection Using Autoencoder and Hybrid Deep Learning Model

Malware continues to pose a serious threat to cybersecurity, especially with the rise of unknown or zero day attacks that bypass the traditional antivirus tools. This study proposes a hybrid detection framework that combines machine learning and deep learning for more effective threat identification...

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Bibliographische Detailangaben

Veröffentlicht in:	2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) S. 1 - 5
Hauptverfasser:	Sai Kiran, Chintha Reddy, S, Hariharasitaraman, Ahmed, Sajjad, Phulre, Ajay Kumar
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 16.05.2025
Schlagworte:	Accuracy Adaptation models Autoencoders Computational modeling Computer security Data models Deep learning machine learning Malware Real-time systems Reliability zero day attack
Online-Zugang:	Volltext
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