Retracted: An efficient stacking model with SRPF classifier technique for intrusion detection system

Abstract

Machine learning process is an important part in intrusion detection system (IDS) and plays an essential role in security information. Analysts operate IDS for the study of server logs or packets of data to identify network traffic malware. IDS automates this process with machine learning-based techniques that allow packet detection on a continuous basis without much human effort or intervention through network analyzing data. It has the following limitations on low rate of detection, false alarm rate is high, and so on, to which the conventional IDS has indicated. We proposed a stacking model that is more accurate to detect the attacks than other techniques of Random Forest, Naïve Bayes, and Decision Tree C4.5. Different researchers are exploring various techniques to classify the rules for associations. But the measure of confidence itself, as investigated in various literature, produces many times inaccurate results. So based on a recent value test called the significant rule power factor (SRPF), this paper discusses classifier. The experiments are carried out by implementation in Weka's method. Results indicate that the proposed classifier is a newly created, light weight classifier with better accuracy performance. Detection of different attacks uses NSL-KDD dataset to train and test the data in this technique. This proposed work is measured using different performance metrics to other intrusion detection models. Compared to existing methods, our proposed model offers prominent increase in detection accuracy.