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All Journal JOINCS (Journal of Informatics, Network, and Computer Science)
Hassan Mohammed , Zainab
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Computer Science & IT

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Journal : JOINCS (Journal of Informatics, Network, and Computer Science)

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A Robust Hybrid CNN–LSTM Framework for High-Accuracy Zero-Day Intrusion and Ransomware Detection Using the UGRansome Dataset: A Robust Hybrid CNN–LSTM Framework for High-Accuracy Zero-Day Intrusion and Ransomware Detection Using the UGRansome Dataset Hatem Khorsheed, Farah; Abbas Abed , Enas; Hassan Mohammed , Zainab; Badr Khudhair Alwan , Walaa
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 9 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v9i1.1688

Abstract

The rapid evolution of cyber-attacks—particularly zero-day intrusions and ransomware—has intensified the need for intelligent and resilient detection systems capable of handling imbalanced, high-dimensional network traffic. This research proposes a robust hybrid deep learning framework combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for enhanced anomaly detection using the UGRansome dataset, a realistic benchmark designed for ransomware and zero-day behavior analysis. The methodology integrates advanced preprocessing, including categorical encoding, feature normalization, and Synthetic Minority Over-sampling Technique (SMOTE) to alleviate class imbalance. The hybrid architecture leverages CNN layers for spatial feature extraction and LSTM layers for modeling temporal dependencies, enabling improved detection of emerging and stealthy threats. Experimental results demonstrate superior performance compared to standalone deep learning baselines, achieving 97.89% accuracy, 0.999 macro AUC, and strong detection capability across minority classes. Confusion matrix visualizations and classification metrics confirm the model’s robustness and generalization. The findings highlight the potential of hybrid deep learning models for proactive cybersecurity defense and establish a foundation for future intelligent intrusion detection systems
Co-Authors Abbas Abed , Enas Badr Khudhair Alwan , Walaa Hatem Khorsheed, Farah