The Industrial Internet of Things (I-IoT) is a manifestation of an extensive industrial network that interconnects various sensors and wireless devices to integrate cyber and physical systems. While I-IoT provides a considerable advantage to large-scale industrial enterprises, it is prone to significant security challenges in the form of sophisticated attacks such as Advanced Persistent Threat (APT). APT is a serious security challenge to all kinds of networks, including I-IoT. It is a stealthy threat actor, characteristically a nation-state or state-sponsored group that launches a cyber attack intending to gain unauthorized access to a computer network and remain undetected for a longer period. The latest intrusion detection systems face several challenges in detecting such complex cyber attacks in multifarious networks of I-IoT, where unpredictable and unexpected cyber attacks of such sophistication can lead to catastrophic effects. Therefore, these attacks need to be accurately and promptly detected in I-IoT. This paper presents an intelligent APT detection and classification system to secure I-IoT. After pre-processing, several machine learning algorithms are applied to detect and classify complex APT signatures accurately. The algorithms include Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, Gaussian Naive Bayes, Bagging, Extreme Gradient Boosting and Adaboost, which are applied on a publicly available dataset KDDCup99. Moreover, a comparative analysis is conducted among ML algorithms to select the appropriate one for the targeted domain. The experimental results indicate that the Adaboost classifier outperforms the others with 99.9% accuracy with 0.012 s execution time for detecting APT attacks. Furthermore, results are compared with state-of-the-art techniques that depict the superiority of the proposed system. This system can be deployed in mission-critical scenarios in the I-IoT domain.
Purpose
Currently, several scoring systems for predicting mortality in severely ill children who require treatment in a pediatric intensive care unit (PICU) have been established. However, despite providing high-quality care, children might develop complications that can cause rapid deterioration in health status and can lead to death. Hence, this study aimed to establish a simple early predictive mortality (SEPM) model with high specificity in identifying severely ill children who would possibly benefit from extensive mechanical ventilation during PICU admission.
Patients and Methods
This is a retrospective longitudinal study that included pediatric patients aged older than two weeks who were on mechanical ventilation and were admitted to the PICU of King Fahd Hospital of the University from January 2015 to December 2019.
Results
In total, 400 pediatric patients were included in this study. The mortality rate of children on mechanical ventilation was 28.90%, and most deaths were associated with respiratory (n = 124 [31%]), cardiovascular (n = 76 [19%]), and neurological (n = 68 [17%]) causes. The SEPM model was reported to be effective in predicting mortality, with an accuracy, specificity, and sensitivity of 92.5%, 97.31%, and 66.15%, respectively. Moreover, the accuracy, specificity, and sensitivity of the Pediatric Risk of Mortality (PRISM) III score in predicting mortality was 95.25%, 98.51%, and 78.46%, respectively.
Conclusion
The SEPM model had a high specificity for mortality prediction. In this model, only six clinical predictors were used, which might be easily obtained in the early period of PICU admission. The ability of the SEPM model and the PRISM III score in predicting mortality in severely ill children was comparable. However, the accuracy of the newly established model in other settings should be validated, and a prospective longitudinal study that considers the effect of the treatment on the model’s predictive ability must be conducted.
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