Graph-Theoretic Approach for Manufacturing Cybersecurity Risk Modeling and Assessment

Identifying, analyzing, and evaluating cybersecurity risks are essential to assess the vulnerabilities of modern manufacturing infrastructures and to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. In response, this work proposes a graph-theoretic approach for risk modeling and assessment to address the lack of quantitative cybersecurity risk assessment frameworks for smart manufacturing systems. In doing so, first, threat attributes are represented using an attack graphical model derived from manufacturing cyberattack taxonomies. Attack taxonomies offer consistent structures to categorize threat attributes, and the graphical approach helps model their interdependence. Second, the graphs are analyzed to explore how threat events can propagate through the manufacturing value chain and identify the manufacturing assets that threat actors can access and compromise during a threat event. Third, the proposed method identifies the attack path that maximizes the likelihood of success and minimizes the attack detection probability, and then computes the associated cybersecurity risk. Finally, the proposed risk modeling and assessment framework is demonstrated via an interconnected smart manufacturing system illustrative example. Using the proposed approach, practitioners can identify critical connections and manufacturing assets requiring prioritized security controls and develop and deploy appropriate defense measures accordingly.Comment: 25 pages, 10 figure

The role of Cra in regulating acetate excretion and osmotic tolerance in E. coli K-12 and E. coli B at high density growth

<p>Abstract</p> <p>Background</p> <p><it>E. coli </it>B (BL21), unlike <it>E.coli </it>K-12 (JM109) is insensitive to glucose concentration and, therefore, grows faster and produces less acetate than <it>E. coli </it>K-12, especially when growing to high cell densities at high glucose concentration. By performing genomic analysis, it was demonstrated that the cause of this difference in sensitivity to the glucose concentration is the result of the differences in the central carbon metabolism activity. We hypothesized that the global transcription regulator Cra (FruR) is constitutively expressed in <it>E. coli </it>B and may be responsible for the different behaviour of the two strains. To investigate this possibility and better understand the function of Cra in the two strains, <it>cra </it>- negative <it>E. coli </it>B (BL21) and <it>E. coli </it>K-12 (JM109) were prepared and their growth behaviour and gene expression at high glucose were evaluated using microarray and real-time PCR.</p> <p>Results</p> <p>The deletion of the <it>cra </it>gene in <it>E. coli </it>B (BL21) minimally affected the growth and maximal acetate accumulation, while the deletion of the same gene in <it>E.coli </it>K-12 (JM109) caused the cells to stop growing as soon as acetate concentration reached 6.6 g/L and the media conductivity reached 21 mS/cm. <it>ppsA </it>(gluconeogenesis gene), <it>aceBA </it>(the glyoxylate shunt genes) and <it>poxB </it>(the acetate producing gene) were down-regulated in both strains, while <it>acs </it>(acetate uptake gene) was down-regulated only in <it>E.coli </it>B (BL21). These transcriptional differences had little effect on acetate and pyruvate production. Additionally, it was found that the lower growth of <it>E. coli </it>K-12 (JM109) strain was the result of transcription inhibition of the osmoprotectant producing <it>bet </it>operon (<it>betABT</it>).</p> <p>Conclusions</p> <p>The transcriptional changes caused by the deletion of <it>cra </it>gene did not affect the activity of the central carbon metabolism, suggesting that Cra does not act alone; rather it interacts with other pleiotropic regulators to create a network of metabolic effects. An unexpected outcome of this work is the finding that <it>cra </it>deletion caused transcription inhibition of the <it>bet </it>operon in <it>E. coli </it>K-12 (JM109) but did not affect this operon transcription in <it>E. coli </it>B (BL21). This property, together with the insensitivity to high glucose concentrations, makes this the <it>E. coli </it>B (BL21) strain more resistant to environmental changes.</p