Comparative Study of Deep Learning and Machine Learning in Intrusion Detection

Deep Learning for Intrusion Detection Systems

Overview of Deep Learning Models

The increasing volume of network traffic and sophisticated cyber threats necessitate the development of advanced Intrusion Detection Systems (IDS). Traditional IDS often struggle with high false positive rates, complex feature engineering, and class imbalances in datasets. To address these challenges, various deep learning models have been implemented, including multilayer perceptron (MLP), convolutional neural networks (CNN), and long short-term memory (LSTM) networks. Traditional algorithms such as logistic regression, naive Bayes, random forest, K-nearest neighbors, and decision trees are also analyzed.

Performance Comparison of Models

A significant contribution of the research is the application of the synthetic minority over-sampling technique (SMOTE) to handle class imbalances, enhancing the learning process. The models undergo extensive performance comparisons, which involve correlation-based feature selection and hyperparameter tuning to maximize detection accuracy. Results reveal that deep learning models, particularly CNN and LSTM, outperform traditional machine learning approaches in detecting cyber threats, achieving accuracy rates of 98%. Notably, random forest stands out with the highest accuracy at 99.9%.

Deep Learning vs. Machine Learning for Intrusion Detection
Using Deep Learning Techniques for Network Intrusion Detection
ArXiv Paper on IDS Using Deep Learning

Computational Efficiency and Practical Deployment

The study also evaluates computational efficiency and practical deployment considerations, discussing the trade-offs between accuracy and resource consumption. These insights are crucial for selecting the most suitable IDS models based on specific network environments and security requirements.

Conclusion

For organizations seeking to enhance their cybersecurity measures, implementing advanced IDS using deep learning techniques offers a promising solution. The findings underscore the potential benefits of utilizing deep learning models tailored to specific network conditions.