Abstract: This study introduces an innovative Intrusion Detection System (IDS) that leverages the combined strengths of Principal Component Analysis (PCA) and the Random Forest machine learning algorithm. The primary goal of this approach is to efficiently identify and classify network intrusions while minimizing data noise and enhancing computational performance. The proposed framework employs PCA to reduce the dimensionality of input data and utilizes a Random Forest classifier to accurately identify threats and malicious activities.The performance of the model was evaluated using the NSL-KDD dataset, a widely recognized benchmark for IDS research. The results demonstrate that integrating PCA and Random Forest creates a robust and efficient IDS capable of adapting to evolving cyber threats. The study also explores the system's implementation details, potential for integration with existing security infrastructure, and scalability for real-time applications. Future directions include exploring the use of deep learning models and unsupervised anomaly detection techniques to further advance intrusion detection capabilities.
Keywords: Intrusion Detection System, Machine Learning, PCA, Random Forest, Network Security, Cybersecurity.
|
DOI:
10.17148/IJARCCE.2025.14477
