Paper 2023/1843
Zero-day vulnerability prevention with recursive feature elimination and ensemble learning
Abstract
This study focuses on spotting and stopping new types of online threats by improving the UGRansome dataset to detect unusual activity in real-time. By blending different machine learning methods, like naïve tree-based ensemble learning and recursive feature elimination (RFE), the research achieves a high accuracy rate of 97%. Naïve Bayes (NB) stands out as the most effective classifier. The suggested setup, combining gradient boosting (GB) and random forest (RF) with NB, effectively identifies and prevents unknown vulnerabilities in computer systems. UGRansome successfully blocks over 100 kilobits per second (kbps) of harmful online traffic by using details pinpointed by the RFE method, specifically uniform resource locators (URLs). This outperforms existing Intrusion Detection System (IDS) datasets. It's particularly good at stopping secure shell attacks, proving the dataset's usefulness in making networks safer. This research marks significant progress in detecting intrusions. The NB model excels in accuracy, precision, and remembering patterns, especially in identifying new threats. Moreover, the suggested naïve tree-based ensemble model shows outstanding accuracy, standing out as the best-performing technique among all models studied. Applying the UGRansome properties-based rule noticeably changes how traffic is sorted, decreasing unknown traffic while increasing unclassified traffic, which requires more investigation.
Note: This manuscript has been submitted to the International Journal of Intelligent Systems, a publication under Hindawi's scholarly journals.
Metadata
- Available format(s)
-
PDF
- Category
- Attacks and cryptanalysis
- Publication info
- Preprint.
- Keywords
- Cryptanalysis techniqueszero-day vulnerabilitiessecure shell attacksproperties-based rulemachine learning
- Contact author(s)
- mike wankongolo @ up ac za
- History
- 2023-12-01: approved
- 2023-11-30: received
- See all versions
- Short URL
- https://ia.cr/2023/1843
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2023/1843,
author = {Mike Nkongolo Wa Nkongolo},
title = {Zero-day vulnerability prevention with recursive feature elimination and ensemble learning},
howpublished = {Cryptology {ePrint} Archive, Paper 2023/1843},
year = {2023},
url = {https://eprint.iacr.org/2023/1843}
}