Year: 2026
Type: conference-paper
Abstract: Data poisoning attacks are a serious threat to machine learning models used in cybersecurity.In this paper, we performed three types of data poisoning attacks -Label Flipping, Targeted Poisoning, and Clean Label Boundary Attack -on the NSL-KDD cybersecurity dataset at two poisoning rates of 20% and 50%.We used the Isolation Forest algorithm to detect and remove poisoned samples and then retrained a Random Forest classifier to recover model accuracy.Our results show that at 20% poisoning, Clean Label Boundary Attack caused the biggest accuracy drop of 5.92%, reducing accuracy from 91.87% to 85.95%.At 50% poisoning, Label Flipping caused the most severe degradation with accuracy dropping by 19.00% from 91.33% to 72.33%.Isolation Forest provided meaningful but incomplete recovery across all attacks, performing best against Targeted Poisoning at 50% where accuracy was restored to 91.36%.These findings show that cybersecurity datasets are vulnerable to data poisoning attacks and stronger defense methods are needed to fully protect intrusion detection systems.
Source: ICACNC
Institution: Islamia University of Bahawalpur
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7202371200