多层感知器神经网络欺诈检测技术

Aji Mubalaike Mubarek, E. Adali
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引用次数: 24

摘要

欺诈检测是一个经久不衰的话题,它对银行、保险、金融部门以及入侵检测系统(IDS)等信息安全系统构成威胁。数据挖掘和机器学习技术有助于预测和快速检测欺诈,并立即采取行动,以最大限度地降低成本。本文从入侵检测系统的定义及其类型入手,重点介绍了一组著名的机器学习分类算法(决策树、朴素贝叶斯和人工神经网络)的实现,这些算法可以减少入侵检测系统现有的缺点。在NSL-KDD数据集上的实验结果表明,我们的ANN-MLP方法(多层感知器)通过计算“混淆矩阵”产生了平均更好的性能,从而帮助我们计算诸如“检测率准确性”,“精度”和“召回率”等性能度量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multilayer perceptron neural network technique for fraud detection
Fraud detection is an enduring topic that pose a threat to banking, insurance, financial sectors and information security systems such as intrusion detection systems (IDS), etc. Data mining and machine learning techniques help to anticipate and quickly detect fraud and take immediate action to minimize costs. This paper starts with the definition of intrusion detection system and its types, focuses on the implementation of a set of well-known machine learning classification algorithms (Decision Trees, Naive Bayes and Artificial Neural Networks), which can reduce the existing disadvantages of the intrusion detection systems. Experimental results on NSL-KDD dataset infer that our ANN-MLP method (Multilayer Perceptron) yields average better performance by calculating “confusion matrix” that in turn helps us to calculate performance measure such as, “Detection Rate Accuracy”, “precision” and “recall”.
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