Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy...

Area of Science:

• Cybersecurity
• Computer Science
• Artificial Intelligence

Background:

• Network security is paramount due to escalating digital threats.
• Intrusion Detection Systems (IDS) are vital for monitoring and preventing network attacks.
• Combining Machine Learning (ML) and Deep Learning (DL) offers advanced solutions for network defense.

Purpose of the Study:

• To improve network security by integrating ML and DL methods for intrusion detection.
• To evaluate the effectiveness of various ML and DL models in identifying and preventing network intrusions.
• To determine the most suitable models for real-world IDS applications based on performance and interpretability.

Main Methods:

• Implementation and testing of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN) models.
• Analysis of network traffic data to differentiate between normal and intrusive behaviors.
• Comparative evaluation of model performance in terms of precision, accuracy, and recall.

Main Results:

• All tested models demonstrated the capability to process network traffic data and distinguish normal from intrusive activities.
• Traditional ML models like SVM, KNN, RF, and DT showed effective intrusion detection capabilities.
• Deep learning models, LSTM and ANN, proved highly effective in identifying complex, long-term patterns and nuanced threats with high precision and accuracy.

Conclusions:

• SVM and Random Forest are recommended for practical IDS due to their balance of reliability and explainability.
• LSTM and ANN are suitable for detecting sophisticated and evolving cyber threats requiring the analysis of sequential data.
• The study highlights the potential of hybrid ML and DL approaches for robust network security.