Automated Intrusion Detection Using Deep Learning Techniques

Abstract

Intrusion Detection Systems (IDSs) are essential for keeping an eye on and identifying dangerous or unauthorized activity occurring within computer networks. Conventional intrusion detection systems, which frequently depend on rule-based detection techniques, find it difficult to adjust to new attack patterns and changing cyber threats. This work suggests a sophisticated IDS architecture that combines Long Short-Term Memory (LSTM) networks with Deep Convolutional Neural Networks (DCNN) in a hybrid deep learning technique to get over these drawbacks.In order to improve detection accuracy,LSTM is utilized to learn temporal and sequential relationships, whereas DCNN focuses on capturing spatial patterns within network traffic data to boost detection accuracy. The NSL-KDD dataset, a refined and generally recognized benchmark for intrusion detection research, is used to train and assess the suggested model. According to experimental results, indicate that the DCNN-LSTM system is an effective and trustworthy approach for detecting threats in real-time within complex and rapidly changing network environments, offering superior detection rates and classification accuracy.

Introduction

To enhance network security, this study proposes a hybrid Intrusion Detection System (IDS) combining Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks. Traditional rule-based IDSs struggle to detect novel threats due to their static nature. In contrast, deep learning models, particularly LSTM (for temporal patterns) and DCNN (for spatial features), provide adaptive and precise threat detection by learning complex patterns from large datasets.

The system is trained and evaluated using the NSL-KDD dataset, a refined version of the KDD Cup 1999 dataset that eliminates redundant records and includes four major attack types: DoS, R2L, U2R, and Probe. The DCNN-LSTM architecture leverages DCNN for extracting key spatial features from network traffic, while LSTM handles the sequence-based behavior, enabling accurate classification of malicious versus benign activity.

Key Components

1. Intrusion Detection Systems (IDS):
   IDS detect unauthorized access or anomalies in network/system behavior using:

   • Signature-based detection: Effective against known attacks.

   • Anomaly-based detection: Identifies deviations but prone to false positives.

   • Types: Host-Based IDS (HIDS) and Network-Based IDS (NIDS).

2. Deep Learning in Cybersecurity:
   Deep learning models like CNN, RNN, and LSTM can automatically learn from raw data without manual feature engineering. These models outperform traditional ML in handling complex, large-scale datasets and detecting sophisticated threats.

3. DCNN-LSTM Hybrid Model:

   • DCNN extracts spatial features from structured network data.

   • LSTM captures long-term dependencies in sequences of network behavior.

   • This hybrid enables robust intrusion detection by learning both spatial and temporal patterns in traffic data.

4. NSL-KDD Dataset:

   • Improved over the original KDD by reducing redundancy.

   • Offers a balanced mix of normal and attack types across 41 features.

   • Provides a benchmark for testing IDS models’ generalization abilities.

Literature Review Insights

• Various studies proposed enhancements in IDS using:

  • SDN frameworks for blocking ransomware (Akbanov).

  • Multi-layered ensembles for wireless and corporate networks (Mikhail).

  • Feature selection and ensemble classifiers to handle high-dimensional data (Zhou).

  • Stacked ensembles for web attack detection (Tama).

  • Pure DCNN-based IDS for real-time threat detection (Hnamte).

Methodology Overview

The hybrid system follows these steps:

1. Data Loading: Imports the NSL-KDD dataset.

2. Data Preprocessing: Cleans data, encodes categorical variables, and normalizes features.

3. Feature Extraction: DCNN extracts meaningful spatial features.

4. Training & Testing: Model learns attack patterns from training data and is evaluated on test data.

5. Model Evaluation: Uses metrics like accuracy, precision, recall, F1-score, and confusion matrices to assess effectiveness.

Algorithm Design

• The DCNN layers identify spatial dependencies in the traffic.

• The LSTM layers analyze sequential behavior.

• Together, they enable nuanced classification of traffic types.

• The model is trained on labeled data from NSL-KDD to distinguish normal from attack traffic.

Result Analysis

• Achieved high accuracy and low false positive rates across all attack types.

• Precision and recall metrics confirmed strong classification ability.

• F1-score balanced precision and recall, reinforcing model robustness.

• Confusion matrices highlighted the model’s strengths and weaknesses per attack category.

• Outperformed traditional IDS methods, particularly in detecting complex, evolving attacks.

Conclusion

To sum up, the suggested intrusion detection system successfully tackles the difficulties of identifying network intrusions by using a hybrid architecture of Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks. The system performs better in classifying network traffic and recognizing different kinds of attacks by fusing the sequential learning strengths of LSTM with the spatial feature extraction capabilities of DCNN.High accuracy, precision, recall, and F1-score are among the evaluation outcomes that support the efficacy of this method in differentiating between benign and malevolent activity. The model\'s capacity to manage intricate and varied assault patterns is further confirmed by the usage of the NSL-KDD dataset for testing. Compared to conventional rule-based systems, this hybrid model offers notable advantages, offering a viable way to increase network security and lessen possible dangers. The model could be improved and made more capable of handling different attack vectors and network settings in future research.

References

To sum up, the suggested intrusion detection system successfully tackles the difficulties of identifying network intrusions by using a hybrid architecture of Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks. The system performs better in classifying network traffic and recognizing different kinds of attacks by fusing the sequential learning strengths of LSTM with the spatial feature extraction capabilities of DCNN.High accuracy, precision, recall, and F1-score are among the evaluation outcomes that support the efficacy of this method in differentiating between benign and malevolent activity. The model\'s capacity to manage intricate and varied assault patterns is further confirmed by the usage of the NSL-KDD dataset for testing. Compared to conventional rule-based systems, this hybrid model offers notable advantages, offering a viable way to increase network security and lessen possible dangers. The model could be improved and made more capable of handling different attack vectors and network settings in future research.

Copyright

Copyright © 2025 B. Manivannan, K. Abarna, D. Radhika. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.