Biblio

Functional dependencies (FDs) are widely applied in data management tasks. Since FDs on data are usually unknown, FD discovery techniques are studied for automatically finding hidden FDs from data. In this paper, we develop techniques to dynamically discover FDs in response to changes on data. Formally, given the complete set Σ of minimal and valid FDs on a relational instance r, we aim to find the complete set Σ$^\textrm\textbackslashprime$ of minimal and valid FDs on røplus\textbackslashDelta r, where \textbackslashDelta r is a set of tuple insertions and deletions. Different from the batch approaches that compute Σ$^\textrm\textbackslashprime$ on røplus\textbackslashDelta r from scratch, our dynamic method computes Σ$^\textrm\textbackslashprime$ in response to \textbackslashtriangle\textbackslashuparrow. by leveraging the known Σ on r, and avoids processing the whole of r for each update from \textbackslashDelta r. We tackle dynamic FD discovery on røplus\textbackslashDelta r by dynamic hitting set enumeration on the difference-set of røplus\textbackslashDelta r. Specifically, (1) leveraging auxiliary structures built on r, we first present an efficient algorithm to update the difference-set of r to that of røplus\textbackslashDelta r. (2) We then compute Σ$^\textrm\textbackslashprime$, by recasting dynamic FD discovery as dynamic hitting set enumeration on the difference-set of røplus\textbackslashDelta r and developing novel techniques for dynamic hitting set enumeration. (3) We finally experimentally verify the effectiveness and efficiency of our approaches, using real-life and synthetic data. The results show that our dynamic FD discovery method outperforms the batch counterparts on most tested data, even when \textbackslashDelta r is up to 30 % of r.

Quasi-identifier is an attribute combined with other attributes to identify specific tuples or partial tuples. Improper selection of quasi-identifiers will lead to the failure of current privacy protection anonymization technology. Therefore, in this paper, we propose a method to solve single structured relational data quasi-identifiers based on functional dependency and determines the attribute classification standard. Firstly, the solution scope of quasi-identifier is determined to be all attributes except identity attributes and critical attributes. Secondly, the real data set is used to evaluate the dependency relationship between the indefinite attribute subset and the identity attribute to solve the quasi-identifiers set. Finally, we propose an algorithm to find all quasi-identifiers and experiment on real data sets of different sizes. The results show that our method can achieve better performance on the same dataset.

Cyber threats directly affect the critical reliability and availability of modern Industry Control Systems (ICS) in respects of operations and processes. Where there are a variety of vulnerabilities and cyber threats, it is necessary to effectively evaluate cyber security risks, and control uncertainties of cyber environments, and quantitative evaluation can be helpful. To effectively and timely control the spread and impact produced by attacks on ICS networks, a probabilistic Multi-Attribute Vulnerability Criticality Analysis (MAVCA) model for impact estimation and prioritised remediation is presented. This offer a new approach for combining three major attributes: vulnerability severities influenced by environmental factors, the attack probabilities relative to the vulnerabilities, and functional dependencies attributed to vulnerability host components. A miniature ICS testbed evaluation illustrates the usability of the model for determining the weakest link and setting security priority in the ICS. This work can help create speedy and proactive security response. The metrics derived in this work can serve as sub-metrics inputs to a larger quantitative security metrics taxonomy; and can be integrated into the security risk assessment scheme of a larger distributed system.

The term "Cyber Physical System" (CPS) has been used in the recent years to describe a system type, which makes use of powerful communication networks to functionally combine systems that were previously thought of as independent. The common theme of CPSs is that through communication, CPSs can make decisions together and achieve common goals. Yet, in contrast to traditional system types such as embedded systems, the functional dependence between CPSs can change dynamically at runtime. Hence, their functional dependence may cause unforeseen runtime behavior, e.g., when a CPS becomes unavailable, but others depend on its correct operation. During development of any individual CPS, this runtime behavior must hence be predicted, and the system must be developed with the appropriate level of robustness. Since at present, research is mainly concerned with the impact of functional dependence in CPS on development, the impact on runtime behavior is mere conjecture. In this paper, we present AirborneCPS, a simulation tool for functionally dependent CPSs which simulates runtime behavior and aids in the identification of undesired functional interaction.