Name
Abstract | ||
|---|---|---|
Cyber-physical Systems of Systems (SoSs) are large-scale systems made of independent and autonomous cyber-physical Constituent Systems (CSs) which may interoperate to achieve high-level goals also with the intervention of humans. Providing security in such SoSs means, among other features, forecasting and anticipating evolving SoS functionalities, ultimately identifying possible detrimental phenomena that may result from the interactions of CSs and humans. Such phenomena, usually called emergent phenomena, are often complex and difficult to capture: the first appearance of an emergent phenomenon in a cyber-physical SoS is often a surprise to the observers. Adequate support to understand emergent phenomena will assist in reducing both the likelihood of design or operational flaws, and the time needed to analyze the relations amongst the CSs, which always has a key economic significance. This article presents a threat analysis methodology and a supporting tool aimed at (i) identifying (emerging) threats in evolving SoSs, (ii) reducing the cognitive load required to understand an SoS and the relations among CSs, and (iii) facilitating SoS risk management by proposing mitigation strategies for SoS administrators. The proposed methodology, as well as the tool, is empirically validated on Smart Grid case studies by submitting questionnaires to a user base composed of 3 stakeholders and 18 BSc and MSc students.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3234513 | ACM Transactions on Cyber-Physical Systems |
Keywords | Field | DocType |
Emergent properties, cyber-physical systems, evolution, security, systems-of-systems, threat analysis, user assessment | Smart grid,Interoperability,Computer security,System of systems,Risk analysis (engineering),Risk management,Surprise,Engineering,Phenomenon,Cognitive load | Journal |
Volume | Issue | ISSN |
3 | 2 | 2378-962X |
Citations | PageRank | References |
1 | 0.40 | 6 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Ceccarelli | 1 | 172 | 32.69 |
| Tommaso Zoppi | 2 | 25 | 8.74 |
| A. Vasenev | 3 | 15 | 4.45 |
| Marco Mori | 4 | 57 | 5.87 |
| Dan Ionita | 5 | 6 | 3.12 |
| Lorena Montoya | 6 | 19 | 5.24 |
| Andrea Bondavalli | 7 | 886 | 133.06 |