<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The security of user information and the precision of user services are paramount, necessitating effective detection of abnormal user behavior in vast datasets. This study introduces the QGAN-BDE algorithm, which leverages a quantum generative adversarial network combined with a novel approach for detecting and evaluating abnormal user behavior. Through a feature matching strategy, the algorithm ensures close data alignment between the generator and discriminator. At the same time, the integration of a classical convolutional neural network within the BDE network assesses user behavior abnormalities. Setting distinct thresholds for abnormal behavior and threats enables the differentiation between normal and abnormal activities. Utilizing a dataset and financial stock log data for simulation, the proposed method achieves an AUC value of approximately 0.912 with small negative data samples. Additionally, it records generator and discriminator loss values within the ranges of [1.05,1.55] and [0.49,0.61], respectively, and demonstrates over 80% accuracy in detecting financial stock log anomalies. This method’s reliance on comprehensive big data allows for an in-depth analysis of user behavior, facilitating the timely identification and management of abnormalities.</p>
</abstract>

The security of user information and the precision of user services are paramount, necessitating effective detection of abnormal user behavior in vast datasets. This study introduces the QGAN-BDE algorithm, which leverages a quantum generative adversarial network combined with a novel approach for detecting and evaluating abnormal user behavior. Through a feature matching strategy, the algorithm ensures close data alignment between the generator and discriminator. At the same time, the integration of a classical convolutional neural network within the BDE network assesses user behavior abnormalities. Setting distinct thresholds for abnormal behavior and threats enables the differentiation between normal and abnormal activities. Utilizing a dataset and financial stock log data for simulation, the proposed method achieves an AUC value of approximately 0.912 with small negative data samples. Additionally, it records generator and discriminator loss values within the ranges of [1.05,1.55] and [0.49,0.61], respectively, and demonstrates over 80% accuracy in detecting financial stock log anomalies. This method’s reliance on comprehensive big data allows for an in-depth analysis of user behavior, facilitating the timely identification and management of abnormalities.

Abnormal User Behavior Detection for Industry Big Data Analysis

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Abnormal User Behavior Detection for Industry Big Data Analysis"}

The security of user information and the precision of user services are paramount, necessitating effective detection of abnormal user behavior in vast...

Abnormal User Behavior Detection for Industry Big Data Analysis

Pubblicato online: 03 mag 2024

Pagine: -

Ricevuto: 15 apr 2024

Accettato: 21 apr 2024

DOI: https://doi.org/10.2478/amns-2024-0935

Parole chiave
Quantum generative adversarial network, Abnormal behavior detection, QGAN-BDE algorithm, Feature matching, generator

© 2024 Zhe Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Abnormal User Behavior Detection for Industry Big Data Analysis

Pubblicato online: 03 mag 2024

Pagine: -

Ricevuto: 15 apr 2024

Accettato: 21 apr 2024

DOI: https://doi.org/10.2478/amns-2024-0935

Parole chiaveQuantum generative adversarial network, Abnormal behavior detection, QGAN-BDE algorithm, Feature matching, generator

© 2024 Zhe Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Quantum generative adversarial network, Abnormal behavior detection, QGAN-BDE algorithm, Feature matching, generator