Multilevel Fraud Detection System using Voting Techniques
Anjali Agrawal1, Mahesh Parma2
1Anjali Agrawal, CSE/IT Department, Madhav Institute Of Technology & Science, Gwalior, India.
2Mahesh Parmar, CSE/IT Department, Madhav Institute Of Technology & Science, Gwalior, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 1805-1811 | Volume-8 Issue-12, October 2019. | Retrieval Number: L28401081219/2019©BEIESP | DOI:10.35940/ijitee.L2840.1081219
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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: Fraud detection is an enduring topic that pose a threat to the information security systems. Data mining helps detect and rapidly identify fraud and take instant action to reduce losses. It analysis the various forms of attacks through classifications algorithms of data mining. In this dissertation the data set of NSL-KDD is examined in identifying anomalies in network traffic patterns and the importance of various classification techniques is studied. Firstly the data is classify in two classes Normal and Anomaly and after then the anomaly data categories in various Attack forms i.e Probe, U2R, R2L, DOS to detect the fraud. The analysis performed through feature selection and classification techniques present in WEKA tool of data mining. The features are extracted through feature selection (CfsSubsetEval, InfoGain, FilterSubsetEval and FilterAttributeEval) methods from 41 attributes to 11 attributes using CfsSubsetEval with best first Search and after extraction the features the classification algorithms(Naïve Bayes, RC, RT, RF and DT) applied for finding Accuracy. The best Result in terms of Accuracy is finding through the voting method using Random Committee and Random Forest is 99.94%.
Keywords: fraud detection, Data Mining, classification algorithms, NSL-KDD dataset, Anomaly
Scope of the Article: Data Mining