AI-Enhanced Intrusion Detection System Using Deep Learning on NSL-KDD Dataset
Bhagyashree D Kale1, Sushma V. Telrandhe2, Ram Madhav Deshmukh3
1Bhagyashree D. Kale, Department of Computer Science and Engineering, Nagpur University, Pune (Maharashtra), India.
2Dr. Sushma V. Telrandhe, Department of Computer Science and Engineering, Nagpur University, Pune (Maharashtra), India.
3Ram M. Deshmukh, Assistant Professor, Department of Computer Science and Engineering, Nagpur University, Pune (Maharashtra), India.
Manuscript Received on 24 October 2025 | Revised Manuscript Received on 03 November 2025 | Manuscript Accepted on 15 November 2025 | Manuscript published on 30 November 2025 | PP: 5-9 | Volume-15 Issue-5, November 2025 | Retrieval Number: 100.1/ijsce.F369815060126 | DOI: 10.35940/ijsce.F3698.15051125
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the rise in cyberattacks targeting modern networks, Intrusion Detection Systems (IDS) have become a critical component of cybersecurity. Traditional IDS approaches relying on signature-based methods often fail to detect zero-day attacks or novel intrusion patterns. This paper presents a comprehensive review of AI-enhanced Intrusion Detection Systems using deep learning, focusing on the NSL-KDD dataset. The study explores state-of-the-art architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), Autoencoders, and hybrid deep learning approaches. Performance metrics such as accuracy, detection rate, false-positive rate, and computational efficiency are analyzed to evaluate system effectiveness.
Keywords: Intrusion Detection System (IDS), Deep Learning, NSL-KDD, Cybersecurity, Machine Learning, CNN, LSTM, Autoencoders.
Scope of the Article: Artificial Intelligence