Fraud Detection Model by Using Support Vector Machine Techniques

2013 ◽  
Vol 7 (2) ◽  
pp. 32-42 ◽  
Author(s):  
Shaio Yan Huang
Keyword(s):  
Support Vector Machine ◽  
Fraud Detection ◽  
Support Vector ◽  
Detection Model

scholarly journals Credit Card Fraud Detection Using Weighted Support Vector Machine

2020 ◽  
Vol 11 (12) ◽  
pp. 1275-1291
Author(s):  
Dongfang Zhang ◽  
Basu Bhandari ◽  
Dennis Black
Keyword(s):  
Support Vector Machine ◽  
Credit Card ◽  
Fraud Detection ◽  
Support Vector ◽  
Credit Card Fraud

An SVM-Based Ensemble Approach for Intrusion Detection

2019 ◽  
Vol 14 (1) ◽  
pp. 66-84 ◽  
Author(s):  
Santosh Kumar Sahu ◽  
Akanksha Katiyar ◽  
Kanchan Mala Kumari ◽  
Govind Kumar ◽  
Durga Prasad Mohapatra
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Learning Algorithm ◽  
Majority Voting ◽  
Support Vector ◽  
Detection Model ◽  
Ensemble Approach ◽  
Efficient Learning

The objective of this article is to develop an intrusion detection model aimed at distinguishing attacks in the network. The aim of building IDS relies on upon preprocessing of intrusion data, choosing most relevant features and in the plan of an efficient learning algorithm that properly groups the normal and malicious examples. In this experiment, the detection model uses an ensemble approach of supervised (SVM) and unsupervised (K-Means) to detect the patterns. This technique first divides the data and forms two clusters as per K-Means and labels the clusters using the Support Vector Machine (SVM). The parameters of K-Means and SVM are tuned and optimized using an intrusion dataset. The SVM provides up to 88%, and K-Means provides up to 83% accuracy individually. However, the ensemble of K-Means and SVM provides more than 99% on three benchmarked datasets in less time. The SVM only classifies three instances of each cluster randomly and labels them as per a majority voting approach. The proposed approach outperforms compared to earlier ensemble approaches on intrusion datasets.

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