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Probabilistic graphical models are employed in a variety of areas such as artificial intelligence and machine learning to depict causal relations among sets of random variables. In this research, we employ probabilistic graphical models in the form of Bayesian network to detect coronavirus disease 2019 (denoted as COVID-19) disease. We propose two efficient Bayesian network models that are potent in encoding causal relations among random variable, i.e., COVID-19 symptoms. The first Bayesian network model, denoted as BN1, is built depending on the acquired knowledge from medical experts. We collect data from clinics and hospitals in Saudi Arabia for our research. We name this authentic dataset DS covid . The second Bayesian network model, denoted as BN2, is learned from the real dataset DS covid depending on Chow-Liu tree approach. We also implement our proposed Bayesian network models and present our experimental results. Our results show that the proposed approaches are capable of modeling the issue of making decisions in the context of COVID-19. Moreover, our experimental results show that the two Bayesian network models we propose in this work are effective for not only extracting casual relations but also reducing uncertainty and increasing the effectiveness of causal reasoning and prediction.

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In this research, we study data poisoning attacks against Bayesian network structure learning algorithms. We propose to use the distance between Bayesian network models and the value of data conflict to detect data poisoning attacks. We propose a 2-layered framework that detects both one-step and long-duration data poisoning attacks. Layer 1 enforces "reject on negative impacts" detection; i.e., input that changes the Bayesian network model is labeled potentially malicious. Layer 2 aims to detect long-duration attacks; i.e., observations in the incoming data that conflict with the original Bayesian model. We show that for a typical small Bayesian network, only a few contaminated cases are needed to corrupt the learned structure. Our detection methods are effective against not only one-step attacks but also sophisticated long-duration attacks. We also present our empirical results.

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Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning (ML) models. Due to attackers' (and/or benign equivalents') dynamic behavior changes, testing data distribution frequently diverges from original training data over time, resulting in substantial model failures. Due to their dispersed and dynamic nature, distributed denial-of-service attacks pose a danger to cybersecurity, resulting in attacks with serious consequences for users and businesses. This paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service (DDOS) in the network. The goal of this architecture combination is to accurately represent data and create an effective cyber security prediction agent. The intrusion detection system and concept drift of the network has been analyzed using secure adaptive windowing with website data authentication protocol (SAW_WDA). The network has been analyzed by authentication protocol to avoid malware attacks. The data of network users will be collected and classified using multilayer perceptron gradient decision tree (MLPGDT) classifiers. Based on the classification output, the decision for the detection of attackers and authorized users will be identified. The experimental results show output based on intrusion detection and concept drift analysis systems in terms of throughput, end-end delay, network security, network concept drift, and results based on classification with regard to accuracy, memory, and precision and F-1 score.