TY  - EJOU
AU  - Alzahrani, Shahad 
AU  - Alsuwat, Hatim 
AU  - Alsuwat, Emad 

TI  - Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks: An Empirical Study
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 139
IS  - 2
SN  - 1526-1506

AB  - 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.
KW  - Bayesian networks; data poisoning attacks; latent variables; structure learning algorithms; adversarial attacks

DO  - 10.32604/cmes.2023.044718