Intrusion Detection System Using Machine Learning Based on NSL KDD Dataset

Todays digital era network may become unstable due to malicious activity on the Internet. One of the best protection methods is an intrusion detection system (IDS), which lowers security losses and shields you from online threats. One method of identifying irregularities in network traffic is the detection of invasion. The challenge of cybersecurity has grown along with the number of devices linked to the Internet. Any harmful behavior on the network must be identified right away in order to guarantee the confidentiality, availability, and integrity of user data. IDS employs machine learning algorithms, which are utilized in anomaly detection techniques to identify network attacks, to address this issue. This study classified the data as either normal or intrusive using machine learning (ML) classifiers, including Random Forest (RF), Extra Tree Classifier (ETC), and Decision Tree (DT). Four variables from the NSL-KDD dataset—a publicly available dataset for identifying DoS, Probe, R2L, and U2R attacks—were used to forecast the model’s performance.