Diverse Analysis of Data Mining and Machine Learning Algorithms to Secure Computer Network

Network attacks are becoming more complex, making it more difficult to detect intrusions. Various research have been done over the years, employing different categorization techniques of Data Mining (DM) and Machine Learning (ML) inspired hybrid approaches to develop robust IDS. Almost all researchers suggested to improve accuracy in intrusion detection with low computational cost. Authors observed that dissimilar sets of features were picked for different classifiers to get the highest accuracy. This paper is dedicated to a review of relevant research, where an in-depth investigation was carried out with two emphasis points of IDS, which contain distinct pre-processing techniques in the form of feature selection and a diversity of classification algorithms. In addition, this paper presents a comparative algorithmic assessment of the DM and ML techniques applied to create an intelligent IDS. A novel feature selection method based on the CART algorithm has also introduced which provides optimal feature subset of the dataset so perfectly that it has made various existing DM and ML classifying algorithms more performant than earlier, this makes classifiers independent of feature selection. To validate the performance of proposed work, experiments have performed using the 'Python' programming language and 'corrected' & '10_percent' of 'Kddcup99' datasets used as benchmark. As an outcome proposed work, we found that feature reduction and selecting a classifier had a significant influence on the rate of intrusion detection accuracy. The results of simulation and comparison analysis of proposed work with existing DM and ML based classification approaches show that the suggested work is more competent in true prediction and attaining maximum intrusion detection accuracy with minimal computing cost of prediction.

Automated summary

The paper explores the development of an intelligent Intrusion Detection System (IDS) through a review of relevant research, focusing on feature selection and classification algorithms. Experimental results show that the proposed method outperforms existing DM and ML based approaches in achieving maximum intrusion detection accuracy with minimal computing cost.