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  1. 1
    Discovering optimal features using static analysis and a genetic search based method for Android malware detection

    Discovering optimal features using static analysis and a genetic search based method for Android malware detection by Firdaus, Ahmad, Anuar, Nor Badrul, Karim, Ahmad, Razak, Mohd Faizal Ab

    Published 2018
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  2. 2
    Discovering optimal features using static analysis and a genetic search based method for Android malware detection

    Discovering optimal features using static analysis and a genetic search based method for Android malware detection by Ahmad Firdaus, Zainal Abidin, Nor Badrul, Anuar, Ahmad, Karim, Mohd Faizal, Ab Razak

    Published 2018
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  3. 3
    Fraud detection in telecommunication using pattern recognition method / Mohd Izhan Mohd Yusoff

    Fraud detection in telecommunication using pattern recognition method / Mohd Izhan Mohd Yusoff by Mohd Yusoff, Mohd Izhan

    Published 2014
    “…We introduce a new algorithm that could detect fraud activities in telecommunication industry (e.g. intrusion fraud which occurs when legitimate account is comprised by an intruder who makes or sells calls on this account) that uses Gaussian Mixed Model (or GMM), a probabilistic model normally used in fraud detection via speech recognition. …”
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  4. 4
    Fraud detection in telecommunication industry using Gaussian mixed model

    Fraud detection in telecommunication industry using Gaussian mixed model by Mohd Yusoff, Mohd Izhan, Mohamed, Ibrahim, Abu Bakar, Mohd Rizam

    Published 2013
    “…In this article, we propose a new fraud detection algorithm using Gaussian mixed model (GMM), a probabilistic model successfully used in speech recognition problem. …”
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  5. 5
    Detection of Fraud and Non-Fraud Transaction using Machine Learning Algorithm

    Detection of Fraud and Non-Fraud Transaction using Machine Learning Algorithm by Sujang Karing, Darryl

    Published 2022
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    Final Year Project
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  6. 6
    Fraud Detection in Shipping Industry using K-NN Algorithm

    Fraud Detection in Shipping Industry using K-NN Algorithm by Subramaniam G., Mahmoud M.A.

    Published 2023
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  7. 7
    Fraud detection in shipping industry based on location using machine learning comparison techniques

    Fraud detection in shipping industry based on location using machine learning comparison techniques by Ganesan Subramaniam, Mr.

    Published 2023
    “…This research discusses fraud detection specifically within the shipping industry using data analytics techniques. …”
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  8. 8
    Credit Card Fraud Detection Using AdaBoost and Majority Voting

    Credit Card Fraud Detection Using AdaBoost and Majority Voting by Randhawa, Kuldeep, Loo, Chu Kiong, Seera, Manjeevan, Lim, Chee Peng, Nandi, Asoke K.

    Published 2018
    “…In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. …”
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  9. 9
    Online Transaction Fraud Detection Using Backlogging On E-Commerce Website: A Review

    Online Transaction Fraud Detection Using Backlogging On E-Commerce Website: A Review by Atiqah, S.M.T., Nurdiana, A.

    Published 2024
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  10. 10
    Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm

    Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm by Yap K.S., Abidin I.Z., Ahmad A.R., Hussien Z.F., Pok H.L., Ismail F.I., Mohamad A.M.

    Published 2023
    “…It provides an increased convergence and globally optimized solutions. The algorithm has been tested using actual customer consumption data from SESB. 10 fold cross validation method is used to confirm the consistency of the detection accuracy. …”
    Conference Paper
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  11. 11
    A Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniques

    A Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniques by Subramaniam G.A.L., Mahmoud M.A., Abdulwahid S.N., Gunasekaran S.S.

    Published 2025
    “…A study reviewed existing fraud detectionFraud detection models and identified the most effective algorithm for the shipping industry. …”
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  12. 12
    Employing Artificial Intelligence to Minimize Internet Fraud

    Employing Artificial Intelligence to Minimize Internet Fraud by Wong, E.S.K.

    Published 2009
    “…Following this, an a ttempt is made to propose using the MonITARS (Monitoring In sider Trading and Regulatory Surveillance) Systems framework which uses a combination of genetic algorithms, neural nets and statistical analysis in detecting insider dealing, to be used in the detection of transaction fraud. …”
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  13. 13
    Improved expectation maximization algorithm for Gaussian mixed model using the kernel method

    Improved expectation maximization algorithm for Gaussian mixed model using the kernel method by Mohd Yusoff, Mohd Izhan, Mohamed, Ibrahim, Abu Bakar, Mohd Rizam

    Published 2013
    “…Finally, for illustration, we apply the improved algorithm to real telecommunication data. The modified method will pave the way to introduce a comprehensive method for detecting fraud calls in future work.…”
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  14. 14
    Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo

    Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo by Terence, Yong Koon Beh, Swee, Chuan Tan, Hwee, Theng Yeo

    Published 2014
    “…This paper reports our experience in applying data balancing techniques to develop a classifier for an imbalanced real-world fraud detection data set. We evaluated the models generated from seven classification algorithms with two simple data balancing techniques. …”
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  15. 15
    RFID-enabled supply chain detection using clustering algorithms

    RFID-enabled supply chain detection using clustering algorithms by Azahar, T.F., Mahinderjit-Singh, M., Hassan, R.

    Published 2015
    “…Furthermore, RFID data nature characteristics faces the issues likes RFID just carry simple information, in-flood of data, inaccuracy data from RFID readers and difficulties to track spatial and place. We propose to use clustering algorithms in order to detect counterfeit in supply chain management. …”
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  16. 16
    Credit Card Fraud Detection Using New Preprocessing And Hybrid Machine Learning Techniques

    Credit Card Fraud Detection Using New Preprocessing And Hybrid Machine Learning Techniques by Gasim, Esraa Faisal Malik

    Published 2023
    “…The second contribution to this research is to develop multiple hybrid machine learning models in order to enhance the detection of fraudulent activities in the credit card fraud detection domain.…”
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  17. 17
    Forensic accountants, auditors and fraud capability and competence requirements in the Nigerian public sector

    Forensic accountants, auditors and fraud capability and competence requirements in the Nigerian public sector by Popoola, Oluwatoyin Muse Johnson

    Published 2014
    “…The ten out of fifteen hypotheses were tested through the use of PLS-SEM algorithm and bootstrap techniques on the hypothesized relationships while the remaining five hypotheses of differences among groups were tested using the Mann-Whitney U Test. …”
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  18. 18
    Botnet detection using automated script / Norfathin Rosli

    Botnet detection using automated script / Norfathin Rosli by Rosli, Norfathin

    Published 2020
    “…This paper is about a Botnet Detection using Automated Script, the programming language is and the operating system is Windows 7. …”
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  19. 19
    A voting-based hybrid machine learning approach for fraudulent financial data classification / Kuldeep Kaur Ragbir Singh

    A voting-based hybrid machine learning approach for fraudulent financial data classification / Kuldeep Kaur Ragbir Singh by Kuldeep Kaur , Ragbir Singh

    Published 2019
    “…Standard base machine learning algorithms, which include a total of twelve individual methods as well as the AdaBoost and Bagging methods, are firstly used. …”
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  20. 20
    SEDF: a spoofed email detection framework employed by a lightweight web browser plug-in for detecting the spoofed email / Al Ya@Geogiana Buja...[et al.]

    SEDF: a spoofed email detection framework employed by a lightweight web browser plug-in for detecting the spoofed email / Al Ya@Geogiana Buja...[et al.] by Buja, Al Ya@Geogiana, Ahmad, Illiasaak, Zolkeply, Mohd Syafiq

    Published 2014
    “…The method used to detect fake e-mail is by tracing back the email header. …”
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    Research Reports
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