AI-Based Network Intrusion Detection System

Abstract

The fast development of digital infrastructure has contributed to the rising number of sophisticated cyberattacks. Classical Intrusion Detection Systems are predominantly based on signatures, which makes it easy to detect previously known cyberattacks but fail to find the novel ones. In this paper, we develop an AI-based Network Intrusion Detection System (NIDS) using the combination of the LSTM algorithm and Random Forest. Long Short-Term Memory helps in capturing temporal char-acteristics of network attacks, whereas Random Forest enhances the robustness of the classification. We use NSL-KDD dataset for testing the model and conduct an evaluation using accuracy, precision, recall, and F1 score. The hybrid model outperforms the models trained separately by detecting intrusions faster and minimizing the false positives. The research shows the effectiveness of using combined ma-chine learning and deep learning approaches in creating adaptive and scalable intrusion detection systems.

Introduction

The text discusses the growing importance of network security due to increased internet usage, cloud computing, and interconnected devices, which have led to rising cyber threats such as DoS attacks, malware, intrusion, and data theft. Traditional Network Intrusion Detection Systems (NIDS), especially signature-based ones, are effective for known attacks but fail to detect complex or zero-day threats, highlighting the need for smarter AI-based solutions.

To address this, the study proposes a hybrid intrusion detection system combining Long Short-Term Memory (LSTM) and Random Forest algorithms. LSTM captures temporal patterns in network traffic, while Random Forest performs robust classification and reduces overfitting. The system uses preprocessing steps such as data cleaning, normalization, and feature engineering to improve data quality and model performance. A weighted ensemble of both models is used to make the final decision, enabling better accuracy and reduced false positives.

The system is trained and evaluated using the NSL-KDD dataset and aims to detect both known and unknown attacks. The methodology includes data preprocessing, feature extraction, LSTM-based sequence learning, Random Forest classification, and ensemble decision-making.

Conclusion

The above-discussed paper has proposed a novel hybrid AI-based Network Intrusion Detection System that utilizes the Long Short-Term Memory technique and the Random Forest for the network traffic classification problem. The discussed technique combines two different approaches for the improvement of intrusion detection. The offered system solves most drawbacks that are faced when using other conventional techniques and allows us to consider network traffic as a time series data. It gives a deeper understanding of the importance of using both deep and machine learning techniques for cybersecurity.

References

[1] M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “An intrusion detection dataset and intrusion traffic characterization,” in Proc. Int. Conf. Information Systems Security and Privacy, 2009, pp. 108–116. [2] KDD Cup 1999 Data, UCI KDD Repository. [Online]. Available: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html [3] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [4] S. Mukkamala, G. Janoski, and A. Sung, “Intrusion detection using neural networks and support vector machines,” in Proc. IEEE Int. Joint Conf. Neural Networks, 2002, pp. 1702–1707. [5] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. [6] C. Yin, Y. Zhu, J. Fei, and X. He, “A deep learning approach for intrusion detection using recurrent neural networks,” IEEE Access, vol. 5, pp. 21954–21961, 2017. [7] G. Kim, S. Lee, and S. Kim, “A novel hybrid intrusion detection method integrating anomaly detection with misuse detection,” Expert Systems with Applications, vol. 41, no. 4, pp. 1690–1700, 2014. [8] A. Javaid, Q. Niyaz, W. Sun, and M. Alam, “A deep learning approach for network intrusion detection system,” in Proc. ACM Int. Conf. Bioinformatics and Computational Biology, 2016, pp. 21–26. [9] I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization,” in Proc. Int. Conf. Information Systems Security and Privacy, 2018, pp. 108–116. [10] M. Ring, D. Wunderlich, D. Grudl, D. Landes, and A. Hotho, “A survey of network-based intrusion detection data sets,” Computers & Security, vol. 86, pp. 147–167, 2019. [11] H. Hindy et al., “A taxonomy of network threats and the effect of current datasets on intrusion detection systems,” IEEE Access, vol. 8, pp. 104650–104675, 2020.

Copyright

Copyright © 2026 Anuradha Tiwari, Sandeep Bind, Ajeet Kanaujia, Neeraj Pal, Sameer Awasthi. 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.