Research Article
An Efficient Real-Time NIDS Using Machine Learning Methods
@INPROCEEDINGS{10.1007/978-3-031-48888-7_15, author={Konda Srikar Goud and M. Shivani and B. V. S. Selvi Reddy and Ch. Shravyasree and J. Shreeya Reddy}, title={An Efficient Real-Time NIDS Using Machine Learning Methods}, proceedings={Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I}, proceedings_a={IC4S}, year={2024}, month={1}, keywords={Intrusion Detection System Cyber-attacks DDoS attack Botnet Random Forest Real-time dataset}, doi={10.1007/978-3-031-48888-7_15} }- Konda Srikar Goud
M. Shivani
B. V. S. Selvi Reddy
Ch. Shravyasree
J. Shreeya Reddy
Year: 2024
An Efficient Real-Time NIDS Using Machine Learning Methods
IC4S
Springer
DOI: 10.1007/978-3-031-48888-7_15
Abstract
Recent developments in network technology and related services have caused a significant rise in data traffic. However, there has also been a massive rise in the negative consequences of cyber-attacks. Many new types of network attacks are emerging. As a result, designing a robust Intrusion detection system (IDS) has become essential. This paper presents a framework for designing an efficient IDS to enhance detection accuracy and reduce false positives on real-time data. This research used the CIC-IDS 2017 dataset to train Machine Learning models such as Logistic Regression, K Nearest Neighbor, Gaussian Naive Bayes, and Random Forest. Machine learning models often perform well on benchmark datasets but may encounter challenges when applied to real-time traffic scenarios. So, we created a Real-time dataset and tested it on the trained models. In the evaluation, the Random Forest classifier outperformed all other models and achieved an accuracy of 99.99%.
