A framework for Operational Security Metrics Development for industrial control environment

Security metrics are very crucial towards providing insights when measuring security states and susceptibilities in industrial operational environments. Obtaining practical security metrics depend on effective security metrics development approaches. To be effective, a security metrics development framework should be scope-definitive, objective-oriented, reliable, simple, adaptable, and repeatable (SORSAR). A framework for Operational Security Metrics Development (OSMD) for industry control environments is presented, which combines concepts and characteristics from existing approaches. It also adds the new characteristic of adaptability. The OSMD framework is broken down into three phases of: target definition, objective definition, and metrics synthesis. A case study scenario is used to demonstrate an instance of how to implement and apply the proposed framework to demonstrate its usability and workability. Expert elicitation has also be used to consolidate the validity of the proposed framework. Both validation approaches have helped to show that the proposed framework can help create effective and efficient ICS-centric security metrics taxonomy that can be used to evaluate capabilities or vulnerabilities. The understanding from this can help enhance security assurance within industrial operational environments

Effects of acute substance use and pre-injury substance abuse on traumatic brain injury severity in adults admitted to a trauma centre

<p>Abstract</p> <p>Background</p> <p>The aims of this study were to describe the occurrence of substance use at the time of injury and pre-injury substance abuse in patients with moderate-to-severe traumatic brain injury (TBI). Effects of acute substance use and pre-injury substance abuse on TBI severity were also investigated.</p> <p>Methods</p> <p>A prospective study of 111 patients, aged 16-55 years, injured from May 2005 to May 2007 and hospitalised at the Trauma Referral Centre in Eastern Norway with acute TBI (Glasgow Coma Scale 3-12). Based on structural brain damages shown on a computed tomography (CT) scan, TBI severity was defined by modified Marshall classification as less severe (score <3) and more severe (score ≥3). Clinical definition of substance use (alcohol and/or other psychoactive substances) was applied when hospital admission records reflected blood alcohol levels or a positive drug screen, or when a physician verified influence by examining the patient. Pre-injury substance abuse (alcohol and drug problems) was screened by using the CAGE questionnaire.</p> <p>Results</p> <p>Forty-seven percent of patients were positive for substance use on admission to hospital. Significant pre-injury substance abuse was reported by 26% of patients. Substance use at the time of injury was more frequent in the less severe group (p = 0.01). The frequency of pre-injury substance abuse was higher in the more severe group (30% vs. 23%). In a logistic regression model, acute substance use at time of injury tended to decrease the probability of more severe intracranial injury, but the effect was not statistically significant after adjusting for age, gender, education, cause of injury and substance abuse, OR = 0.39; 95% CI 0.11-1.35, p = 0.14. Patients with positive screens for pre-injury substance abuse (CAGE ≥2) were more likely to have more severe TBI in the adjusted regression analyses, OR = 4.05; 95% CI 1.10-15.64, p = 0.04.</p> <p>Conclusions</p> <p>Acute <b>s</b>ubstance use was more frequent in patients with less severe TBI caused by low-energy events such as falls, violence and sport accidents. Pre-injury substance abuse increased the probability of more severe TBI caused by high-energy trauma such as motor vehicle accidents and falls from higher levels. Preventive efforts to reduce substance consumption and abuse in at-risk populations are needed.</p

An Exposition of Performance-Security Trade-offs in RANETs Based on Quantitative Network Models

NoSecurity mechanisms, such as encryption and authentication protocols, require extra computing resources and therefore, have an adverse effect upon the performance of robotic mobile wireless ad hoc networks (RANETs). Thus, an optimal performance and security trade-off should be one of the main aspects that should be taken into consideration during the design, development, tuning and upgrading of such networks. In this context, an exposition is initially undertaken on the applicability of Petri nets (PNs) and queueing networks (QNs) in conjunction with their generalisations and hybrid integrations as robust quantitative modelling tools for the performance analysis of discrete flow systems, such as computer systems, communication networks and manufacturing systems. To overcome some of the inherent limitations of these models, a novel hybrid modelling framework is explored for the quantitative evaluation of RANETs, where each robotic node is represented by an abstract open hybrid G-GSPN_QN model with head-of-line priorities, subject to combined performance and security metrics (CPSMs). The proposed model focuses on security processing and state-based control and it is based on an open generalised stochastic PN (GSPN) with a gated multi-class 'On-Off' traffic and mobility model. Moreover, it employs a power consumption model and is linked in tandem with an arbitrary QN consisting of finite capacity channel queues with blocking for 'intra' robot component-to-component communication and 'inter' robot-to-robot transmission. Conclusions and future research directions are included