Open Access

ARTICLE

Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks: An Empirical Study

by Shahad Alzahrani¹, Hatim Alsuwat², Emad Alsuwat^3,*

1 Independent Researcher Specializing in Data Security and Privacy, Taif, 26571, Saudi Arabia
2 Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, 24382, Saudi Arabia
3 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 26571, Saudi Arabia

* Corresponding Author: Emad Alsuwat. Email:

(This article belongs to the Special Issue: Information Security and Trust Issues in the Digital World)

Computer Modeling in Engineering & Sciences 2024, 139(2), 1635-1654. https://doi.org/10.32604/cmes.2023.044718

Received 07 August 2023; Accepted 17 November 2023; Issue published 29 January 2024

Abstract

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables. However, the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams. One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks, wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance. In this research paper, we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms. Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time. We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks. With regard to four different forms of data poisoning attacks, we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques, such as the PC algorithm. By doing this, we explore the complexity of this area and offer workable methods for identifying and reducing these sneaky dangers. Additionally, our research investigates one particular use case, the “Visit to Asia Network.” The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry, which is of utmost relevance. Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks. Additionally, our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

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Keywords

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