Intrusion Detection System Using PCA with Random Forest

Abstract

In the face of adding high- tech pitfalls, the passing of state- of- the- art interruption discovery systems( IDS) is essential for conclusive network protection. This paper presents an innovative IDS foundation that integrates star element Analysis( PCA) accompanying Random Forest( RF) classifiers to embellish both discovery veracity and computational effectiveness. PCA is employed to act range decline above- dimensional network business dossier, that streamlines the data while maintaining crucial countenance. This decline process mitigates the challenges associated with period of range and reduces computational above, making the dossier more controllable for analysis. After asking PCA, the refashioned dossier is subordinated to categorization exercising the Random Forest invention. Random Forest, an ensemble education fashion, builds diversified conclusion shrubs and summations their labors to make further correct vaticinations. By using the compound anticipations of these different timbers, Random Forest upgrades categorization performance and reduces the threat of overfitting.. The results show that this approach achieves larger discovery rates and smaller dishonest a still picture taken with a camera, making it a more secure and effective answer for over- to- date high- tech trouble discovery. The junction of PCA and RF specifies a adaptable and high- definition IDS, agitating the growing complexity of network freedom challenges and donation a strong form for securing fine surroundings.

Introduction

This study presents a novel Intrusion Detection System (IDS) that combines Principal Component Analysis (PCA) for dimensionality reduction with a Random Forest classifier for enhanced cybersecurity. The system is designed to improve the accuracy, efficiency, and scalability of detecting cyber threats in complex and high-volume network environments.

Key Components:

1. Problem & Motivation

• Traditional IDSs face issues like high false positive rates, difficulty handling high-dimensional data, and inefficiency in detecting zero-day attacks.

• The need for a more intelligent, scalable, and real-time IDS has grown due to increasing cyber threats.

2. Proposed System

• PCA reduces data dimensions by retaining only the most informative features, which simplifies and speeds up processing.

• Random Forest, a robust ensemble learning method, classifies intrusions effectively by combining multiple decision trees to reduce overfitting and boost accuracy.

• The integrated system offers real-time threat detection, suitable for large-scale and high-speed network traffic.

3. System Implementation

• Admin and user modules include features for managing IDS datasets, performing PCA analysis, visualizing results, and user authentication.

• System architecture supports dynamic interactions and operational control over intrusion detection processes.

4. Results & Performance

• Achieved 97.5% detection accuracy and 40% reduction in training time.

• PCA reduced features from 42 to 20 while preserving 95% of the data variance.

• The combined PCA-Random Forest model outperformed Decision Tree and SVM classifiers in both accuracy and computational efficiency.

• Minor limitations include potential loss of features during dimensionality reduction and limited detection of zero-day attacks.

5. Literature Review Insights

• Various papers show the effectiveness of machine learning (ML) and deep learning (DL) in IDS, including the use of feature selection, GRU models, hybrid ANN-SVM systems, and lightweight models for IoT.

• Emphasis on combining ML/DL with intelligent feature reduction techniques to optimize IDS performance.

Conclusion

Integrating Principal Component Analysis (PCA) with Random Forest algorithms in Intrusion Detection Systems (IDS) presents a highly effective solution for enhancing cybersecurity, particularly in handling high-dimensional and complex network data. PCA plays a crucial role by reducing the dataset’s dimensionality, eliminating redundant and less informative features while preserving the essential characteristics required for accurate analysis. This not only simplifies the data structure but also improves the efficiency of subsequent modeling. Random Forest, on the other hand, brings the power of ensemble learning to the table, excelling at managing non-linear relationships and intricate classification problems. Its inherent robustness and ability to generalize well across varied datasets make it ideal for detecting diverse and sophisticated cyber threats. This integrated approach follows a structured pipeline, beginning with data preprocessing—where raw inputs are cleaned, normalized, and prepared for analysis. Following this, dimensionality reduction through PCA helps in extracting the most relevant features. The refined data is then used to train the Random Forest model, which learns to identify patterns associated with malicious activities. Finally, the model is deployed in a real-time environment, enabling continuous monitoring, detection, and generation of actionable alerts when anomalies or intrusions are detected. Moreover, the implementation of such a system requires a thorough evaluation of associated costs, including those related to potential attacks, system operations, data handling, human resources, and ongoing maintenance. Despite these investments, the system offers substantial benefits by significantly improving threat detection accuracy, reducing false positives, and enhancing the overall resilience of network infrastructure. By combining PCA’s dimensionality reduction with Random Forest’s robust classification capabilities, organizations can build a sophisticated, scalable, and adaptive IDS framework that serves as a valuable defense mechanism against a broad spectrum of cyber threats—ensuring stronger protection and a more proactive cybersecurity posture.

References

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Copyright

Copyright © 2025 Malleboina Kalyan , Korrapati Shamitha, Pedapolu Deepika Sai. 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.