Vulnerability Analysis and Defense for the Internet: 37 (Advances in Information Security)
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Following the selection of a smaller set of specific scenarios S 1 , … , S h , we estimate the indirect customers I C 1 , … , I C h for different sectors dependent upon electricity. We can also measure the wider macroeconomic impacts in terms of different metrics such as private consumption P C , investments I n , capital stock C S , gross value added GVA , or gross domestic product GDP. The initial development of this framework involved a set of stakeholder interviews conducted with representative organizations number of in parentheses from energy 13 , security 6 , insurance 20 , defense 2 , government 9 , and academia 4 , to assess current understanding, potential exposure, and analytics which could aid resilience building activities.
Research initially began in July when defining a hypothetical event similar to the Ukrainian attack, at a set of scenario development workshops, consisting of U. Rather than regional control rooms being of greatest risk, which have significant cyber and physical security procedures, it was local substations that were identified as vulnerable assets. Traditionally, these local substations are less protected than those parts of the network which are higher up the distribution or transmission hierarchy. While stakeholders identified that pinpointing vulnerable substations was highly challenging, there was specific interest in understanding the potential direct and indirect impacts of different events based on differentiated a priori levels of cyber vulnerability.
In December , as this research was being written up into an industry white paper Cambridge Centre for Risk Studies, , the Ukrainian electricity substation attack took place, turning a hypothetical scenario into an actual event.
A threat is defined as any potential hazard implemented for malicious intent, which could interfere with normal operational conditions, causing a blackout event. These systems are found in many industrial applications including CNI systems. The attacker may specifically aim to spoof sensors with false data, disconnect key devices required for normal operations, and control physical components such as actuators. Having defined the threat, we will now discuss how it manifests. The Ukrainian attack has been documented by the U.
Department for Homeland Security The attackers managed to firstly deliver BlackEnergy Malware via spear phishing emails using malicious Microsoft Office attachments. Secondly, intruders conducted comprehensive reconnaissance of critical systems in advance. Finally, to commence the attack, substation breakers were disconnected using legitimate credentials with either remote administration tools or remote ICS client software via a Virtual Private Network connection.
Little about this attack was specific to Ukrainian technology or critical infrastructure, and therefore it could be replicated in similar ways in other nations. However, more substations could have been affected. All substation assets in this electricity region are owned and operated by the same DNO. To capture both upward and downward counterfactual events, we select both half and double the number of substations affected by the Ukrainian attack. As consequently explained in this method, each of these three potential events is investigated separately utilizing Monte Carlo simulation.
The number of substations is used to represent different scales of attack. Background information on the U. Network topologies for electricity distribution and all other infrastructure systems were meticulously constructed and validated, as detailed in the associated Supporting Information. Classical scenario analysis is a tool frequently used for risk management purposes; however, it often is used in a very deterministic manner. We stochastically explore the event space by randomly sampling four, seven, or 14 substations per event, leading to a total number of 1.
Each substation has an equal likelihood of being selected. This simple approach, in which we sample subsets of affected substations and calculate the population impacted in each case, is justified on two grounds. First, we lack the technical design and operational details of individual components within each substation e. This is partly reflective of the variation in the state of knowledge of CNI operators.https://hukusyuu-mobile.com/wp-content/bluetooth/3661-spy-xiaomi-mi.php
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But it is safe to assume that all substations of the same DNO function at similar design and operational standards U. Power Networks, Event simulation results are illustrated in Fig. For a given stochastic event, for example, where seven substations are attacked, the difference in the population affected by the substation selection relates to changes in the spatial attack footprint and the network topology.
In contrast, differences between stochastic events relate to the scale of attack across the network and the number of infected substations.
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We select cumulative frequencies that relate to the mean impact 50th percentile, or median and the tail risk 10th and 1st percentile , as these were identified as being important to the scenario stakeholders at the development workshops. For example, the 1st percentile of the event with seven infected substations affects power supply for 0.
The implication for the management of power distribution networks is evident: Preventing or recovering from cyberattacks, which can scale up from a few substations to many is the priority. By implication, protecting larger substations with greater investment of resources may be a less effective means of prevention according to the metric of consumer disruption.
We also examine other critical infrastructures, including rail, fresh water, wastewater, and telecommunications, which typically derive their electrical power needs from a direct connection to the distribution network. The dependent infrastructures include water towers, wastewater treatment works, macrocellular basestations as point assets, along with a railway network.
The dependencies are derived based on multiple criteria including i existing data on the physical connections between networked assets; ii geographic proximity of assets to their nearest electricity substations of appropriate voltage, and iii functional understanding of the flow of electricity from substations to other infrastructures. The exact voltage level to which individual CNI assets are connected may vary. However, the majority of each asset type connects to the same voltage level the same substation type.
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We define failure as a condition of the network node or edge asset such that it is no longer able to perform its functional purpose. In our description, this means that the service demand satisfied by the affected node is lost and all its connections are interrupted.
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Based on the selected scenarios, it is assumed that all the included electricity substations have failed and subsequently the number of disrupted electricity customers is estimated. In parallel there are also cascading failures toward the dependent assets of other sectors. To estimate disruption, we first model customer assignments to different types of infrastructures.
For all infrastructures, we create average daily customer estimates.
Assigning customer values to assets is based on a spatial union of its asset footprint with census derived population estimates. For each threat scenario, the infected electricity substations lead to failure propagating along the whole network path where the flow of electricity via the failed substations takes place as illustrated in Fig. For other critical infrastructures, such as telecoms masts, water towers, and waste water treatment works, customer disruptions are estimated based on whether the connected electricity substation has failed.
For the railway network disruptions, we first consider the stations disrupted due to connection to failed electricity substations. The aggregated number of customers affected by each critical infrastructure sector provides the disruption estimates reported. Private consumption is affected as consumers are unable to complete daily economic transactions. The Oxford Economics Global Economic Model is utilized, which is a widely employed macroeconomic model with users including the International Monetary Fund and World Bank Oxford Economics, , and consists of over 26, interlinked equations based on historical correlations and economic theory.
Multivariate forecasts are produced for many economies, but here we focus only on the United Kingdom. The modeling approach adopts Keynesian principles in the short run, where shocks to demand generate economic cycles that can be influenced by fiscal and monetary policy. We use the model to see the effect of a shock directly applied to private consumption to understand the impact on GDP. Additionally, we report a set of intermediate macroeconomic indicators including lost investment, capital stock formation, manufacturing GVA, and services GVA, to quantify the economic impact by scenario.
On one hand, this may overestimate the impact due to the potential rescheduling of consumption purchases. The modeled electricity distribution network serves just under a third of the total U. In this section, the results are reported for the nine scenarios tested within the assessment framework with a spatial resolution of Local Authority Districts.
First, the spatial direct disruption to electricity users will be reported, followed by the indirect impacts. In the selected scenarios, the population affected ranged from 0.
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A larger proportion was affected as the number of compromised substations increased. In general, as the number of substations affected increased, the matter of which specific substations are attacked has a marginally diminishing impact on the direct level of population disruption.
This pattern is illustrated within Fig. Review the whole column for each bulletin identifier that is listed to verify the updates that you have to install, based on the programs or components that you have installed on your system. NET Framework 2. NET Framework 3.
See the other tables in this section for additional affected software. Microsoft Baseline Security Analyzer MBSA lets administrators scan local and remote systems for missing security updates and common security misconfigurations.
The Update Compatibility Evaluator components included with Application Compatibility Toolkit aid in streamlining the testing and validation of Windows updates against installed applications. Microsoft thanks the following for working with us to help protect customers:. No updated version of the Microsoft Windows Malicious Software Removal Tool is available for out-of-band security bulletin releases. To improve security protections for customers, Microsoft provides vulnerability information to major security software providers in advance of each monthly security update release.
Security software providers can then use this vulnerability information to provide updated protections to customers via their security software or devices, such as antivirus, network-based intrusion detection systems, or host-based intrusion prevention systems. To determine whether active protections are available from security software providers, please visit the active protections websites provided by program partners listed in Microsoft Active Protections Program MAPP Partners.
The affected software listed has been tested to determine which versions are affected. Other versions are past their support life cycle. To determine the support life cycle for your software version, visit Microsoft Support Lifecycle. Help protect your computer that is running Windows from viruses and malware: Virus Solution and Security Center.
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Profile Bookmarks Collections Sign out. Executive Summaries The following table summarizes the security bulletins for this month in order of severity.