Network Intrusion Detection System Using Artificial Intelligence

Abstract

This paper focuses on the development of an Advanced Network Intrusion Detection System (ANIDS) that leverages a combination of Machine Learning Algorithms, including Recurrent Neural Networks (RNN), K- Nearest Neighbours (KNN), CatBoost and AdaBoost to enhance the accuracy and efficiency of Intrusion Detection over Wireless Network. By Employing the fusion approach, the system aims to capitalize on the strengths of each algorithm to improve overall performance in identifying malicious activities and potential threats within Network Traffic. This paper utilizes scalar encoding for effective feature representation and applies Synthetic Minority Over Sampling Technique (SMOTE) to address class imbalance in the dataset, ensuring a more robust and fairer training process. Through a comprehensive training code and processing test code, the system is designed to accurately classify normal and abnormal network behaviour, significantly reducing false positives and improving detection rates. This Innovative approach not only enhances the security of network infrastructures but also provides a scalable solution for real time monitoring and response to cybersecurity threats, thereby contributing to safer digital environments.

Introduction

In the current digital age, network security is under increasing threat from sophisticated cyber-attacks. Traditional Intrusion Detection Systems (IDS), which rely on predefined rules or signatures, struggle to detect new or evolving threats and often suffer from high false positive rates. This paper proposes a more advanced and adaptive Machine Learning-based Network Intrusion Detection System (ML-NIDS) that integrates multiple algorithms—RNN, KNN, CatBoost, and AdaBoost—to improve detection accuracy and robustness.

The hybrid approach leverages the strengths of each model:

• RNN captures temporal patterns in sequential data,

• KNN performs spatial pattern recognition,

• CatBoost and AdaBoost enhance model performance via ensemble learning.

To address challenges such as imbalanced datasets and large-scale data, the system employs scalar encoding for preprocessing and SMOTE (Synthetic Minority Oversampling Technique) to balance classes, thus reducing bias toward benign traffic and lowering false negatives.

The system is designed for real-time detection, capable of processing large volumes of traffic with high scalability and adaptability, making it suitable for both small networks and cloud-based infrastructures.

The literature review highlights prior works using machine learning for IDS, noting progress but also limitations in scalability, accuracy, and adaptability. The proposed system advances this field by offering a comprehensive, intelligent solution that is more accurate, faster, and responsive to emerging threats, including zero-day attacks.

Conclusion

The intrusion discovery system developed in this design offers a robust,multi-faceted approach to relatingand mollifyingnetwork pitfalls. By integrating intermittent Neural Networks (RNN), K- Nearest Neighbors (KNN), CatBoost, and AdaBoost into a single frame, the system combines the strengths of temporal pattern recognition, spatial anomaly discovery, and ensemble literacy. This mongrel system not only improves discovery delicacy but also minimizes false cons, icing dependable network security. The use of scalar encoding and SMOTE for data preprocessing enhances the model\'s capability to handle imbalanced datasets, making it more effective in relating rare but critical attacks. With this comprehensive approach, the design provides a scalable and adaptive result for real- time network intrusion discovery, perfecting overall security and adaptability in ultramodernnetwork surroundings. The stoner with little training can get the needed report. The software executes successfully by fulfilling the objects of the design. farther extensions to this system can be made required with minor variations.

References

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Copyright

Copyright © 2025 Dr. L. Malathi, Ms. Gayathri B. 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.