Data mining tools -a case study for network intrusion detection
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Data mining tools -a case study for network intrusion detection Soodeh Hosseini 1,2 & Saman Rafiee Sardo 1 Received: 7 June 2020 / Revised: 21 August 2020 / Accepted: 16 September 2020 # The Author(s) 2020
Abstract
With the growth of data mining and machine learning approaches in recent years, many efforts have been made to generalize these sciences so that researchers from any field can easily utilize these sciences. One of the most important of these efforts is the development of data mining tools that try to hide the complexities from researchers so that they can achieve a professional output with any level of knowledge. This paper is focused on reviewing and comparing data mining and machine learning tools including WEKA, KNIME, Keel, Orange, Azure, IBM SPSS Modeler, R and Scikit-Learn to show what approach each of these methods has taken in the face of the complexities and problems of different scenarios of generalization of data mining and machine learning. In addition, for a more detailed review, this paper examines the challenge of network intrusion detection in two tools, Knime with graphical interface and Scikit-Learn with coding environment. Keywords Data mining tools . Machine learning algorithms . Intrusion detection . Knime . Scikitlearn
1 Introduction The growth and penetration of the Internet has led to the production of large amounts of data by companies. In addition, many software and databases have been developed to help companies maintain this data. There has also been a lot of research in recent years to extract
* Soodeh Hosseini [email protected] Saman Rafiee Sardo [email protected]
1
Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
2
Mahani Mathematical Research Center, Shahid Bahonar University of Kerman, Kerman, Iran
Multimedia Tools and Applications
useful information from these data. These researches are very valuable for companies and as a result, have led to the growth of data mining and machine learning technologies. For example, the Chinese Electric Company data review, examined in [36], can be analyzed to discover the peak hours of power consumption. Data mining is an appropriate extraction of hidden predictive information totally stored or captured in massive data centers. Recently, many free and commercial data mining and data analysis tools have been developed for solving problems across fields such as life sciences, financial services, telecom, and insurance [17]. Data mining or Knowledge Discovery from Data (KDD) tools allows us to analyze large datasets to solve decision problems. The data mining tools use historical information to build a model to predict customer’s behavior e.g., which customers are likely to respond to a new product. Another example is intrusion detection in local systems or networks by analyzing the activity of system and network and processes them by the data mining algorithm in data mining tools. However, these tools are not all powerful enough and do not support a
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