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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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
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Zusammenfassung: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. Known malware is classified using an RNN-LSTM model, while an Autoencoder detects unfamiliar threats by learning typical benign behavior and flagging anomalies. To improve accuracy, XGBoost is used to select the most relevant features for analysis. Experimental results show that the model achieves 99% accuracy in detecting known malware and reliably identifies previously unseen attacks. This approach demonstrates strong potential for enhancing proactive and scalable cybersecurity defenses.
DOI:10.1109/ASSIC64892.2025.11158367