Bayesian network model to distinguish between intentional attacks and accidental technical failures:a case study of floodgates

Water management infrastructures such as floodgates are critical and increasingly operated by Industrial Control Systems(ICS).These systems are becoming more connected to the internet,either directly or through the corporate networks.This makes them vulnerable to cyber-attacks.Abnormal behaviour in floodgates operated by ICS could be caused by both(intentional)attacks and(accidental)technical failures.When operators notice abnormal behaviour,they should be able to distinguish between those two causes to take appropriate measures,because for example replacing a sensor in case of intentional incorrect sensor measurements would be ineffective and would not block corresponding the attack vector.In the previous work,we developed the attack-failure distinguisher framework for constructing Bayesian Network(BN)models to enable operators to distinguish between those two causes,including the knowledge elicitation method to construct the directed acyclic graph and conditional probability tables of BN models.As a full case study of the attack-failure distinguisher framework,this paper presents a BN model constructed to distinguish between attacks and technical failures for the problem of incorrect sensor measurements in floodgates,addressing the problem of floodgate operators.We utilised experts who associate themselves with the safety and/or security community to construct the BN model and validate the qualitative part of constructed BN model.The constructed BN model is usable in water management infrastructures to distinguish between intentional attacks and accidental technical failures in case of incorrect sensor measurements.This could help to decide on appropriate response strategies and avoid further complications in case of incorrect sensor measurements.

Comparison of clinical features and outcomes in peritoneal dialysisassociated peritonitis patients with and without diabetes:A multicenter retrospective cohort study

BACKGROUND The number of end-stage renal disease patients with diabetes mellitus(DM)who are undergoing peritoneal dialysis is increasing.Peritoneal dialysis-associated peritonitis(PDAP)is a serious complication of peritoneal dialysis leading to technical failure and increased mortality in patients undergoing peritoneal dialysis.The profile of clinical symptoms,distribution of pathogenic organisms,and response of PDAP to medical management in the subset of end-stage renal disease patients with DM have not been reported previously.Discrepant results have been found in long-term prognostic outcomes of PDAP in patients with DM.We inferred that DM is associated with bad outcomes in PDAP patients.AIM To compare the clinical features and outcomes of PDAP between patients with DM and those without.METHODS In this multicenter retrospective cohort study,we enrolled patients who had at least one episode of PDAP during the study period.The patients were followed for a median of 31.1 mo.They were divided into a DM group and a non-DM group.Clinical features,therapeutic outcomes,and long-term prognostic outcomes were compared between the two groups.Risk factors associated with therapeutic outcomes of PDAP were analyzed using multivariable logistic regression.A Cox proportional hazards model was constructed to examine the influence of DM on patient survival and incidence of technical failure.RESULTS Overall,373 episodes occurred in the DM group(n=214)and 692 episodes occurred in the non-DM group(n=395).The rates of abdominal pain and fever were similar in the two groups(P>0.05).The DM group had more infections with coagulase-negative Staphylococcus and less infections with Escherichia coli(E.coli)as compared to the non-DM group(P<0.05).Multivariate logistic regression analysis revealed no association between the presence of diabetes and rates of complete cure,catheter removal,PDAP-related death,or relapse of PDAP(P>0.05).Patients in the DM group were older and had a higher burden of cardiovascular disease,with lower level of serum albumin,but a higher estimated glomerular filtration rate(P<0.05).Cox proportional hazards model confirmed that the presence of diabetes was a significant predictor of all-cause mortality(hazard ratio=1.531,95%confidence interval:1.091-2.148,P<0.05),but did not predict the occurrence of technical failure(P>0.05).CONCLUSION PDAP patients with diabetes have similar symptomology and are predisposed to coagulase-negative Staphylococcus but not E.coli infection compared those without.Diabetes is associated with higher all-cause mortality but not therapeutic outcomes of PDAP.