Volume V | Issue 3 | 2026 | Paper ID: IJATEM-V05I03P2

Deep Learning-Based ResNet- BiLSTM Framework for Network Intrusion Detection

Author(s)

, R. K. Padmashini

Cite This

R. K. Padmashini, "Deep Learning-Based ResNet- BiLSTM Framework for Network Intrusion Detection", International Journal of Advanced Trends in Engineering and Management, vol. 05, no. 03, pp. 9-19, 2026.

Abstract

Intrusion detection is a security method that monitors and analyzes system or network activity to identify malicious attacks, unauthorized access, or policy violations. It is essential for safeguarding digital infrastructures against continuously evolving cyberthreats. Traditional Intrusion Detection Systems (IDS) often struggle with poor scalability under heavy network traffic. They also suffer from high false alarm rates and weak adaptability to new attack patterns. These drawbacks make them less reliable in complex environments and limit their real-time detection capability. To tackle these issues, this paper proposes an optimized Residual Network – Bidirectional Long Short-Term Memory (ResNet-BiLSTM) model. The study is conducted using the benchmark NSL-KDD dataset for evaluation. Preprocessing steps include addressing missing values, applying Min-Max normalization, and performing one-hot encoding. A balanced dataset is then generated to minimize the adverse effects of class imbalance. The Fennec Fox Optimization Algorithm (FFOA) is applied for effective feature selection, ensuring the most relevant inputs are retained. The optimized ResNet-BiLSTM captures both spatial and temporal dependencies in the processed features. The proposed framework achieves precision of 0.96, recall of 0.94, F1-score of 0.95, and accuracy of 0.94. Implemented in Python, the system reduces false alarms and delivers robust performance for modern intrusion detection.

Keywords

Intrusion Detection Systems (IDS), Residual Network – Bidirectional Long Short-Term Memory (ResNet-BiLSTM), Fennec Fox Optimization Algorithm (FFOA)

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