Feature Extraction vs Fine-tuning for Cyber Intrusion Detection Model

Abstract

This study investigates the effectiveness of feature extraction and fine-tuning approaches in developing robust cyber intrusion detection models using the Network-based Security Lab - KDD dataset (NSL-KDD). The role of cyber intrusion detection is pivotal in securing computer networks from unauthorized access and malicious activities. Feature extraction, involving methods such as PCA, LDA, and Autoencoders, aims to transform raw data into informative representations, while fine-tuning leverages pre-trained models for task-specific adaptation. The study follows a comprehensive research method encompassing data collection, preprocessing, model development, and experimental evaluation. Results indicate that LDA and Autoencoders excel in the feature extraction phase, demonstrating precision, high accuracy, F1-Score, and recall. However, fine-tuning a pre-trained Multilayer Perceptron model surpasses individual feature extraction methods, achieving superior performance across all metrics. The discussion emphasizes the complexity and flexibility of these approaches, with fine-tuned models showcasing higher adaptability. In conclusion, this study provides valuable insights into the comparative effectiveness of feature extraction and fine-tuning for cyber intrusion detection. The findings underscore the importance of leveraging pre-trained knowledge and adapting models to specific tasks, offering a foundation for further advancements in enhancing network security through advanced machine learning techniques.