A UML-based static verification framework for security

Secure software engineering is a new research area that has been proposed to address security issues during the development of software systems. This new area of research advocates that security characteristics should be considered from the early stages of the software development life cycle and should not be added as another layer in the system on an ad-hoc basis after the system is built. In this paper, we describe a UML-based Static Verification Framework (USVF) to support the design and verification of secure software systems in early stages of the software development life-cycle taking into consideration security and general requirements of the software system. USVF performs static verification on UML models consisting of UML class and state machine diagrams extended by an action language. We present an operational semantics of UML models, define a property specification language designed to reason about temporal and general properties of UML state machines using the semantic domains of the former, and implement the model checking process by translating models and properties into Promela, the input language of the SPIN model checker. We show that the methodology can be applied to the verification of security properties by representing the main aspects of security, namely availability, integrity and confidentiality, in the USVF property specification language

Property specification and static verification of UML models

We present a static verification tool (SVT), a system that performs static verification on UML models composed of UML class and state machine diagrams. Additionally, the SVT allows the user to add extra behavior specification in the form of guards and effects by defining a small action language. UML models are checked against properties written in a special-purpose property language that allows the user to specify linear temporal logic formulas that explicitly reason about UML components. Thus, the SVT provides a strong foundation for the design of reliable systems and a step towards model-driven security

Proactive and reactive runtime service discovery: a framework and its evaluation

The identification of services during the execution of service-based applications to replace services in them that are no longer available and/or fail to satisfy certain requirements is an important issue. In this paper we present a framework to support runtime service discovery. This framework can execute service discovery queries in pull and push mode. In pull mode, it executes queries when a need for finding a replacement service arises. In push mode, queries are subscribed to the framework to be executed proactively, and in parallel with the operation of the application, in order to identify adequate services that could be used if the need for replacing a service arises. Hence, the proactive (push) mode of query execution makes it more likely to avoid interruptions in the operation of service-based applications when a service in them needs to be replaced at runtime. In both modes of query execution, the identification of services relies on distance-based matching of structural, behavioural, quality, and contextual characteristics of services and applications. A prototype implementation of the framework has been developed and an evaluation was carried out to assess the performance of the framework. This evaluation has shown positive results, which are discussed in the paper

Genetic evidence for the association between COVID-19 epidemic severity and timing of non-pharmaceutical interventions

Unprecedented public health interventions including travel restrictions and national lockdowns have been implemented to stem the COVID-19 epidemic, but the effectiveness of non- pharmaceutical interventions is still debated. We carried out a phylogenetic analysis of more than 29,000 publicly available whole genome SARS-CoV-2 sequences from 57 locations to estimate the time that the epidemic originated in different places. These estimates were examined in relation to the dates of the most stringent interventions in each location as well as to the number of cumulative COVID-19 deaths and phylodynamic estimates of epidemic size. Here we report that the time elapsed between epidemic origin and maximum intervention is associated with different measures of epidemic severity and explains 11% of the variance in reported deaths one month after the most stringent intervention. Locations where strong non-pharmaceutical interventions were implemented earlier experienced 30 much less severe COVID-19 morbidity and mortality during the period of study

Report 11: Evidence of initial success for China exiting COVID-19 social distancing policy after achieving containment

The COVID-19 epidemic was declared a Global Pandemic by WHO on 11 March 2020. As of 20 March 2020, over 254,000 cases and 10,000 deaths had been reported worldwide. The outbreak began in the Chinese city of Wuhan in December 2019. In response to the fast-growing epidemic, China imposed strict social distancing in Wuhan on 23 January 2020 followed closely by similar measures in other provinces. At the peak of the outbreak in China (early February), there were between 2,000 and 4,000 new confirmed cases per day. For the first time since the outbreak began there have been no new confirmed cases caused by local transmission in China reported for five consecutive days up to 23 March 2020. This is an indication that the social distancing measures enacted in China have led to control of COVID-19 in China. These interventions have also impacted economic productivity in China, and the ability of the Chinese economy to resume without restarting the epidemic is not yet clear. Here, we estimate transmissibility from reported cases and compare those estimates with daily data on within-city movement, as a proxy for economic activity. Initially, within-city movement and transmission were very strongly correlated in the 5 provinces most affected by the epidemic and Beijing. However, that correlation is no longer apparent even though within-city movement has started to increase. A similar analysis for Hong Kong shows that intermediate levels of local activity can be maintained while avoiding a large outbreak. These results do not preclude future epidemics in China, nor do they allow us to estimate the maximum proportion of previous within-city activity that will be recovered in the medium term. However, they do suggest that after very intense social distancing which resulted in containment, China has successfully exited their stringent social distancing policy to some degree. Globally, China is at a more advanced stage of the pandemic. Policies implemented to reduce the spread of COVID-19 in China and the exiting strategies that followed can inform decision making processes for countries once containment is achieved

Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries

Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national lockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number – a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of lockdown (11th March), although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented all interventions considered in our analysis. This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March [95% credible interval 21,000-120,000]. Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-2 up to 28th March, representing between 1.88% and 11.43% of the population. The proportion of the population infected to date – the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-2 is slowing

State-level tracking of COVID-19 in the United States

As of 1st June 2020, the US Centers for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available deathdata within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on therate of transmission of SARS-CoV-2. We estimate thatRtwas only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%-4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant