Abstract
Benchmark datasets are the inevitable tool required to scrutinize vulnerabilities and tools in network security. Current datasets lack correlation between normal and the real-time network traffic. Behind every evaluation and establishment of attack detection, such datasets are the cornerstone deployed by research community. Creating our own dataset is a herculean task. Hence analyzing the subsisting datasets aids to provide a thorough clarity on the effectiveness when deployed in real time environments. This paper work focus on analysis and comparison of UNSW-NB15 with NSL-KDD dataset based on performance analysis and accuracy using machine learning classifiers. Feasibility, reliability and dependability of the dataset is reviewed and discussed by considering various performance measures such as precision, recall, F-score, specificity using various machine learning classifiers Naïve Bayes, Logistic Regression, SMO, J48 and Random Forest. Experimental results give out its noticeable classification accuracy of 0.99 with the random forest classifier having 0.998 recall and specificity 0.999 respectively. Research studies reveal the fact that threat diagnosis using conventional dataset and sophisticated technologies cover only 25% of threat taxonomy and hence the poor performance of existing intrusion detection systems. Thorough analysis and exploration of the dataset will pave the way for the outstanding performance of the intelligent Intrusion Detection System.
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Dickson, A., Thomas, C. (2021). Analysis of UNSW-NB15 Dataset Using Machine Learning Classifiers. In: Thampi, S.M., Piramuthu, S., Li, KC., Berretti, S., Wozniak, M., Singh, D. (eds) Machine Learning and Metaheuristics Algorithms, and Applications. SoMMA 2020. Communications in Computer and Information Science, vol 1366. Springer, Singapore. https://doi.org/10.1007/978-981-16-0419-5_16
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