Performance comparison of random forest and BILSTM for intrusion detection in cyber security environment

A. Prashanthi and R. Ravinder Reddy

International Journal of Mathematics in Operational Research, 2026, vol. 33, issue 2, 168-183

Abstract: The widespread adoption of the internet has led to an increase in network attacks, making traditional signature-based detection methods less effective against zero-day attacks. This research examines the efficacy of anomaly-based detection techniques in identifying these threats, using two artificial intelligence models: CNN-BiLSTM and random forest classifier. Data for training and testing these models was sourced from the CICIDS2017 dataset. Results showed a high success rate, with CNN-BiLSTM achieving 95% and random forest classifier achieving 98%. These findings suggest that anomaly-based detection offers a robust strategy for detecting zero-day network attacks. The research also underscores the necessity of assessing detection systems through various performance metrics, including accuracy, precision, recall, and F1 score. Such metrics provide a comprehensive understanding of an algorithm's effectiveness in diverse scenarios, which is crucial for developing more advanced and secure network security systems capable of addressing emerging threats.

Keywords: anomaly-based detection; zero-day attacks; machine learning; BiLSTM; random forest classifier; CICIDS2017. (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.inderscience.com/link.php?id=152320 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmore:v:33:y:2026:i:2:p:168-183

Access Statistics for this article

More articles in International Journal of Mathematics in Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().