Novel machine learning based decision support system for fraud prevention

Bereczki, Norman [Bereczki, Norman (Mesterséges Intel...), szerző] Hálózati Rendszerek és Szolgáltatások Tanszék (BME / VIK); Simon, Vilmos [Simon, Vilmos (Mobil távközlés), szerző] Hálózati Rendszerek és Szolgáltatások Tanszék (BME / VIK); Wiandt, Bernát [Wiandt, Bernát (számítógéphálózatok), szerző] Hálózati Rendszerek és Szolgáltatások Tanszék (BME / VIK)

Angol nyelvű Konferenciaközlemény (Könyvrészlet) Tudományos
    • MTMT: 33526977
    • ResearchGate publ.: 366617714
    • (BME-NVA-02 (TKP2021))
    • Villamosmérnöki és informatikai tudományok
    Due to the popularity of cloud services it is unavoidable, that not just legitimate, but fraudulent registrations will happen. For a service with good reputation it is essential to prevent fraud users. A common way is to filter these cases during the registration process by analysts. This paper presents a novel decision support system, that can recognise anomalous behavioural patterns and classify accounts based on the available data thus implementing an automated fraud prevention system. The process uses both supervised and unsupervised approaches, thus avoiding errors due to inaccurate labeling. As a supervised machine learning algorithm random forest classifier and logistic regression, as an unsupervised auto encoder is used. The developed flow gives a recommendation to the analyst whether a new user is potentially fraud or not and provides feedback on the accuracy of analysts’ work based on the results of the unsupervised approach. The newly developed process is able to supervise the decisions made by analysts thus improving the labeling process. The main goal of this paper is to present a new, more deterministic labeling workflow with the ability to provide feedback so it can improve the correctness of the training data set.
    Hivatkozás stílusok: IEEEACMAPAChicagoHarvardCSLMásolásNyomtatás
    2024-07-21 16:26