Net Flow based Cyber Threat Classification using J48 and Random Forest Machine Learning Algorithms
Rakesh Kumar1, Rajeev Singh2
1Rakesh Kumar*, Computer Engineering Dept, College of Technology Pantngar, U. S. Nagar, India.
2Rajeev Singh, Computer Engineering Dept, College of Technology Pantngar, U. S. Nagar, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2973-2979 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1326109119/2019©BEIESP | DOI: 10.35940/ijeat.A1326.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the field of information technology cyber security plays a vital role. Securing information is the biggest challenge now a days. As the word cyber security comes in our mind the fear of cybercrime comes in us at the same time. Cyber threats are nothing but an activity by which any targeted system can be compromised by altering the availability, integrity, and confidentiality of the system. To overcome such type of threats there are number of mechanisms available. Recently the Machine Learning (ML) approaches have proved to be a milestone for the classification of NetFlows. The NetFlow is a network protocol designed by CISCO which is used to collect the network traffic (NetFlows). In this paper J48 and Random Forest (RF) machine learning algorithms are used for classification of cyber threats using NetFlows. The results are obtained by applying classification algorithms on NetFlows using Weka ML tool and RStudio. A comparison is made in various perspectives like accuracy, true positive (TP), false positive (FP), etc.
Keywords: Classification Algorithms, J48, Machine Learning, Net Flows, Random Forest.