Comparative Analysis on Intrusion Detection System Using Machine Learning Approach

The increasing popularity of online data storage and access has raised concerns about security and privacy in the face of growing online threats. However, with the rise of online threats, security and privacy have become major concerns. Intrusion detection systems (IDS) play an important role in protecting data integrity by identifying and quarantining records in the event of unexpected changes. Anomaly-based IDS, which uses machine learning-based approach and algorithms, is an effective way to detect known and unknown attacks, including zero-day attacks. The proposed project is used to create model to implement and analyze anomaly-based IDS to classify malicious attack types such as normal (non-intrusion), DoS, Probe, U2R and R2L. The analysis is conducted on KDDCup99 Dataset which consists of different attacks that a IDS go through. The Machine Learning Algorithms like KNN, SVM, Random Forest and LightGBM are used for the analysis. The Comparitive Analysis is made on KDDCup99 Dataset using the above Machine Learning Algorithms that uses the hybrid techniques and Ensemble techniques like Bagging and Boosting.