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Journals / Journal of Soft Computing and Artificial Intelligence / 2025 / Volume: 6 Issue: 2

Deep Learning for Cyberattack Detection: A Comparative Analysis of Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN)

Gloria Odiaga(Jaramogi Oginga Odinga University of Science and Technology)0009-0007-4748-7804
Newton Masinde(Jaramogi Oginga Odinga University of Science and Technology)0000-0002-2578-4361
Castro Yoga(Jaramogi Oginga Odinga University of Science and Technology)0009-0003-1320-0685
Pages
—
Publication Date
2025-12-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

LSTM outperformed RNN and DNN, achieving the highest accuracy, precision, specificity, and sensitivity at 94.7%, 94.3%, 98.9%, and 94.7% respectively, validating LSTM's superior generalizability for cyberattack detection tasks.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:While the growing accessibility of technology improves usability, it also creates more opportunities for cybercriminals to exploit vulnerabilities, significantly accelerating the proliferation of cybersecurity attacks.
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Key Findings & Data:Deep learning (DL) approaches present significant advancements over conventional machine learning (ML) techniques by automating feature selection and extraction while minimizing external dependencies.
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Conclusion & Impact:LSTM outperformed RNN and DNN, achieving the highest accuracy, precision, specificity, and sensitivity at 94.7%, 94.3%, 98.9%, and 94.7% respectively, validating LSTM's superior generalizability for cyberattack detection tasks.
🔬Design:Modeling & Simulation
📍Scope:2017 Verileri
⚙️Variables:Deep Learning · Cyberattack · Security

Abstract

While the growing accessibility of technology improves usability, it also creates more opportunities for cybercriminals to exploit vulnerabilities, significantly accelerating the proliferation of cybersecurity attacks. Deep learning (DL) approaches present significant advancements over conventional machine learning (ML) techniques by automating feature selection and extraction while minimizing external dependencies. This study proposes a deep learning-based model to enhance cyberattack detection and ensure that data security goals are achieved. This study employs a quantitative research design, utilizing simulation and modeling as the primary analytical tools. The dataset used is the Canadian Institute for Cybersecurity Intrusion Detection System (CIC-IDS-2017) dataset. Three distinct DL algorithms are used to design the detection models, namely, Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). The performance comparison metrics are F1-score, accuracy, sensitivity, false positive rate (FPR), specificity, positive predictive value (PPV), false negative rate (FNR), and negative predictive value (NPV). Optimization concepts are integrated to enhance the detection efficiency in web-based systems, including loss functions, gradient-based optimization, and efficient model generalization techniques. The results of k-fold cross-validation show LSTM’s higher scores for F1-score (94.6%), recall (94.7%), accuracy (94.8%), and precision (94.6%). LSTM outperformed RNN and DNN, achieving the highest accuracy, precision, specificity, and sensitivity at 94.7%, 94.3%, 98.9%, and 94.7% respectively, validating LSTM's superior generalizability for cyberattack detection tasks.

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Cite This Article

Odiaga, G., Masinde, N., Yoga, C. (2025). Deep Learning for Cyberattack Detection: A Comparative Analysis of Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Journal of Soft Computing and Artificial Intelligence, Volume: 6 Issue: 2. https://doi.org/10.55195/jscai.1820478

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