… We propose using Bayesian Networks (BNs) as an alternative way to implement, extend
the FAIR model and eliminate its restrictions, which is described in Section 4 and 7. …

… information security. The term is broad-ranging and applies to everything from computer
security … Boosted Bayesian networks had the highest accuracy and lowest false positive rate in …

… a fruitful data-driven IDS predictive model for providing intelligent services of cyber security.
… Naive Bayes is a kind of Bayesian network and is a commonly used machine learning …

… model with several traditional popular machine learning methods such as the naive Bayes
classifier, logistic regression, support vector machines… -based Bayesian network to classify the …

… Although the complete prevention of any cyber attack is an … By automatically “attacking”
existing security systems, ML … of ML models organized in a Bayesian Network Classifier (BNC) […

… The authors proposed a Bayesian network model to evaluate and ensure the cybersecurity
… to accumulate some information about the target computer security ML model. The extracted …

… attack detection which has provided highest accuracy as 97.16% also proposed models are
compared with machine learning … for usage of deep learning algorithm for IoT cyber security. …

… cyber-intrusion (Biringer, Vugrin, & Warren, 2016). This research uses a Bayesian network
(BN) to address a range of possible cyber … to mitigate the consequences of a cyber-disruption. …

… between the smart grids sub-systems through dynamic Bayesian networks. To capture
the patterns in system behavior, the authors proposed the use of a restricted Boltzmann …

… key machine learning techniques applied in cyber security … of using machine learning
techniques for cyber security. We … of machine learning techniques, and how machine learning …