Security patterns

Security patterns are solutions to recurring information security problems [120]. Several security patterns exist in the literature. However, not all of them are well supported [121]. The most common security properties are: confidentiality, integrity, availability and accountability [122, 123].

Yoder et al. [124] already presented architectural patterns for application security in 1997. Seven security patterns are discussed in the work: single access point, check point, roles, session, full view with errors, limited view and secure access layer.

The security pattern repository of Kienzle et al. [120] describes two groups of security patterns. Structural patterns are comparable to design patterns [125]. They provide a simple and elegant solutions to security problems in the form of diagrams of structure and descriptions of interactions. Procedural patterns on the other hand are designed to improve the process of developing secure software.

A more extensive overview of security patterns is given by Yskout et al. [126]. Three categories are distinguished: application architecture, application design and system. 35 security patterns are described in total.

Heyman et al. [121] give an overview of the number of security patterns, the domains they operate in and the quality of the patterns. They state that there are too many patterns, whilst not every pattern fits the definition of a security pattern. The quality of the documentation of security patterns is often lacklustre and privacy and non-repudiation aren’t supported well by security patterns.

Yoshioka et al. [122] gave a survey on security patterns. Security patterns are categorised according to software development phases: the requirement phase, the design phase and the implementation phase. The ease of use, the effectiveness and the sufficiency of security patterns of patterns are discussed as well.

to measure how well an architecture is protected against relevant security threats. The severity sev of a threat is computed as the normalised sum or the reproducibility, the exploitability and the discoverability. The threat protection is calculated as one minus the multiplication of the coverage of the implemented patterns. The protection against an attack is the summation over all threats of the multiplication of the severity of the threat by the protection against the threat.

4.6

Conclusions

The literature related is presented in this chapter. Topics are description of electricity demand of households and appliances; the possibilities to detect appliances in load profiles and the potential privacy issues; ways to model electricity demand; driving factors for demand response, studies and field tests related to demand response, the attitude of customers towards active demand and control strategies for active demand; and software to perform data analyses on electrical load data.

Electricity demand description

The development of smart grid integration strategies requires knowledge about electricity demand. Aggregated demand as total electricity demand [127] and average load profiles [128] is sufficient when focus is on global results. Detailed electricity demand profiles are required for the simulation of voltage problems caused by vehicle charging [129] and micro-grids [130] or to estimate the potential of battery storage in a distribution grid [131]. Two ways exist to deliver input for simulations: data selection and data generation.

Data selection requires a thorough description of demand, both in terms of the distribution of annual electricity demand as well as demographic parameters related. Annual demand only expresses the total demand, while demographic properties have an influence on the timing of demand and allow for the construction of districts. The demographic properties found are compared to those described in the literature.

Customers are clustered into groups based on timing and magnitude of demand to allow for load profile generation (Section 7.2). The number of groups is limited to double the number of customer types defined by the regulator, to reduce the risk of outliers. The group or cluster centre found after analysis, represents the average consumption pattern of similar customers and allows for data up-scaling.

Electricity demand of wet appliances is described according to the found groups. The measurement data of Linear is scaled up for the description, given the limited number of households with measurements at both connection point and of appliances. The scaling up process consists of spreading the data over the customer groups.

5.1

Demographic description

Knowledge of the total annual electricity demand and the correlated demographic parameters make it easier to understand trend and volume patterns, not being the focus of this section but described in Sections 5.2 and 5.3 and Chapters 7 and 8.

The distribution of the total annual electricity demand for Flanders is explained in Section 5.1.1. Customer types described by the Flemish regulator for Electricity and Gas (VREG) are listed and with measurement data from distribution network operators. Probability density functions are to parametrise the histogram.

An expert is needed to find patterns in large amounts of data. Machine learning algorithms are expert systems which are able to decide which parameters are relevant. Here, they are used to determine the demographic parameters for Belgium/Flanders from responses to a questionnaire and measurement data from distribution system operators. The common supervised machine learning algorithms are explained in Section 3.2.1.

The algorithms are applied to measurement data and questionnaire to find the demographic parameters related to the total electricity demand (Section 5.1.2). Evaluation criteria to find the best performing machine learning algorithm are explained as well. Different demographic parameters are the input for the different machine learning algorithms. The input parameters for the best performing algorithms are regarded the relevant demographic parameters. The algorithm with the highest receiver operating curve area under curve, true positive rate and precision and with the lowest false positive rate for each class to predict is regarded best performing.