Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset

Surveillance of active human cytomegalovirus infection in hematopoietic stem cell transplantation (HLA sibling identical donor): search for optimal cutoff value by real-time PCR

<p>Abstract</p> <p>Background</p> <p>Human cytomegalovirus (CMV) infection still causes significant morbidity and mortality after allogeneic hematopoietic stem cell transplantation (HSCT). Therefore, it is extremely important to diagnosis and monitor active CMV infection in HSCT patients, defining the CMV DNA levels of virus replication that warrant intervention with antiviral agents in order to accurately prevent CMV disease and further related complications.</p> <p>Methods</p> <p>During the first 150 days after allogeneic HSTC, thirty patients were monitored weekly for active CMV infection by <it>pp65 </it>antigenemia, nested-PCR and real-time PCR assays. Receiver operating characteristic (ROC) plot analysis was performed to determine a threshold value of the CMV DNA load by real-time PCR.</p> <p>Results</p> <p>Using ROC curves, the optimal cutoff value by real-time PCR was 418.4 copies/10⁴PBL (sensitivity, 71.4%; specificity, 89.7%). Twenty seven (90%) of the 30 analyzed patients had active CMV infection and two (6.7%) developed CMV disease. Eleven (40.7%) of these 27 patients had acute GVHD, 18 (66.7%) had opportunistic infection, 5 (18.5%) had chronic rejection and 11 (40.7%) died - one died of CMV disease associated with GVHD and bacterial infection.</p> <p>Conclusions</p> <p>The low incidence of CMV disease in HSCT recipients in our study attests to the efficacy of CMV surveillance based on clinical routine assay. The quantification of CMV DNA load using real-time PCR appears to be applicable to the clinical practice and an optimal cutoff value for guiding timely preemptive therapy should be clinically validated in future studies.</p

Electroweak parameters of the z0 resonance and the standard model

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