Credit card fraud detection using decision tree for tracing email and IP

被引:0
|
作者
Dhanapal, R. [1 ]
Gayathiri, P. [2 ]
机构
[1] Department of Computer Applications, Eswari Engineering College, Chennai-600089, India
[2] Research Scholar in Manonmaniam Sundaranar University, Department of Computer Science, Kanchi Sri Krishna College, Kanchipuram, India
来源
关键词
Electronic mail - Data mining - Crime;
D O I
暂无
中图分类号
学科分类号
摘要
Credit card fraud is a wide-ranging term for theft and fraud committed using a credit card or any similar payment mechanism as a fraudulent source of funds in a transaction. The purpose may be to obtain goods without paying, or to obtain unauthorized funds from an account. Transactions completed with credit cards seem to become more and more popular with the introduction of online shopping and banking. Correspondingly, the number of credit card frauds has also increased. Currently; data mining is a popular way to combat frauds because of its effectiveness. Data mining is a welldefined procedure that takes data as input and produces output in the forms of models or patterns. In other words, the task of data mining is to analyze a massive amount of data and to extract some usable information that we can interpret for future uses. Frauds has also increased .Currently, data mining is a popular way to combat frauds because of its effectiveness. Data mining is a well-defined procedure that takes data as input and produces output in the forms of models or patterns. In other words, the task of data mining is to analyze a massive amount of data and to extract some usable information that we can interpret for future uses. © 2012 International Journal of Computer Science Issues.
引用
收藏
页码:406 / 412
相关论文
共 50 条
  • [21] Credit card fraud detection using hidden Markov model
    Srivastava, Abhinav
    Kundu, Amlan
    Sural, Shamik
    Majumdar, Arun K.
    IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 2008, 5 (01) : 37 - 48
  • [22] Credit card fraud detection using asexual reproduction optimization
    Ghahfarokhi, Anahita Farhang
    Mansouri, Taha
    Moghaddam, Mohammad Reza Sadeghi
    Bahrambeik, Nila
    Yava, Ramin
    Sani, Mohammadreza Fani
    KYBERNETES, 2022, 51 (09) : 2852 - 2876
  • [23] DETECTION OF CREDIT CARD FRAUD USING RESAMPLING AND BOOSTING TECHNIQUE
    Jose, Suni
    Devassy, Deepa
    Antony, Anly M.
    2023 ADVANCED COMPUTING AND COMMUNICATION TECHNOLOGIES FOR HIGH PERFORMANCE APPLICATIONS, ACCTHPA, 2023,
  • [24] Credit Card Fraud Detection using Machine Learning Algorithms
    Dornadula, Vaishnavi Nath
    Geetha, S.
    2ND INTERNATIONAL CONFERENCE ON RECENT TRENDS IN ADVANCED COMPUTING ICRTAC -DISRUP - TIV INNOVATION , 2019, 2019, 165 : 631 - 641
  • [25] Detection of Credit Card Fraud using a Hybrid Ensemble Model
    Saraf, Sayali
    Phakatkar, Anupama
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (09) : 464 - 474
  • [26] Using deep networks for fraud detection in the credit card transactions
    Kazemi, Zahra
    Zarrabi, Houman
    2017 IEEE 4TH INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED ENGINEERING AND INNOVATION (KBEI), 2017, : 630 - 633
  • [27] Credit Card Fraud Detection using autoencoder based clustering
    Zamini, Mohamad
    Montazer, Gholamali
    2018 9TH INTERNATIONAL SYMPOSIUM ON TELECOMMUNICATIONS (IST), 2018, : 486 - 491
  • [28] Credit card fraud detection using machine learning algorithms
    de Souza, Daniel H. M.
    Bordin Jr, Claudio J.
    REVISTA BRASILEIRA DE COMPUTACAO APLICADA, 2023, 15 (01): : 1 - 11
  • [29] Using Variational Auto Encoding in Credit Card Fraud Detection
    Tingfei, Huang
    Guangquan, Cheng
    Kuihua, Huang
    IEEE ACCESS, 2020, 8 : 149841 - 149853
  • [30] Fraud Detection in Credit Card Transactions by using Classification Algorithms
    Devi, Vimala J.
    Kavitha, K. S.
    2017 INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN COMPUTER, ELECTRICAL, ELECTRONICS AND COMMUNICATION (CTCEEC), 2017, : 125 - 131