Network Intrusion Detection Using Hybrid Simplified Swarm Optimization and Random Forest Algorithm on Nsl-Kdd Dataset

Provided by: International Journal Of Engineering And Computer Science
Topic: Security
Format: PDF
During the last decade the analysis of intrusion detection has become very significant, the researcher focuses on various dataset to improve system accuracy and to reduce false positive rate based on DAPRA 98 and later the updated version as KDD cup 99 dataset which shows some statistical issues, it degrades the evaluation of anomaly detection that affects the performance of the security analysis which leads to the replacement of KDD cup 99 to NSL-KDD dataset. This paper focus on detailed analysis on NSL- KDD dataset and proposed a new technique of combining swarm intelligence (simplified swarm optimization) and data mining algorithm (random forest) for feature selection and reduction.

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