Purpose -Cybercrime is a serious threat for most modern societies as it hinders the necessary adoption of information and communication technologies (ICT). This paper seeks to analyze a large number of real cases related to the cybercrime in Greece. Design/methodology/approach -All cases were voluntarily submitted from Greek users, who asked for assistance to a special task force, composed of public sector employees and servants. By analyzing more than 1,000 similar cases, the paper derives a taxonomy of security-related incidents that the Greek users encountered during the past years. Greece only recently embraced broadband technologies and, therefore, this paper is able to observe the corresponding increase of cybercriminal activities.Findings -An analysis of the reports received by the task force indicate a clear shift in the type of reported content. A large number of recent reports from Greek users concern financial fraud schemes and phenomena of cyberbullying and cyber-extortions, activities related primarily to the newly emerged social networking sites. Originality/value -The paper offers insights into cybercrime in Greece via real case studies.

Peak demand is a major challenge for power utilities across the world. Demand Response (DR) is considered to be effective in addressing peak demand by altering consumption of end consumers, so as to match supply capability. However, an efficient DR system needs to respect end consumer convenience and understand their propensity of participating in a particular DR event, while altering the consumer demand. Understanding such preferences is non-trivial due to the large-scale and variability of consumers and the infrastructure changes required for collecting essential (smart meter and/or appliance specific) data.In this paper, we propose an inclusive DR system, iDR, that helps an electricity provider to design an effective demand response event by analyzing its consumers' houselevel consumption (smart meter) data and external context (weather conditions, seasonality etc.) data. iDR combines analytics and optimization to determine optimal power consumption schedules that satisfy an electricity provider's DR objectives -such as reduction in peak load -while minimizing the inconvenience caused to consumers associated with alteration in their consumption patterns. iDR uses a novel context-specific approach for determining end consumer baseline consumptions and user convenience models. Using these consumer specific models and past DR experience, iDR optimization engine identifies -(i) when to execute a DR event, (ii) who are the consumers to be targeted for the DR, and (iii) what signals to be sent. Some of iDR's capabilities are demonstrated using real-world house-level as well as appliance-level data.

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Demand response (DR) has received significant attention in recent years and several DR programs are being deployed and evaluated worldwide. DR systems provide a wide range of economic and operational benefits to different stakeholders of the electrical power system including consumers, generators and distributors. DR can be achieved through a number of different mechanisms such as direct-load-control, incentives, pricing signals, or a combination of these schemes. Due to the remarkable variation in demand response systems, it becomes a challenge to evaluate and compare the effectiveness of different DR programs holistically. In this work, we define a number of different performance metrics that could be used to evaluate DR programs based on peak reduction, demand variation and reshaping, and economic benefits.

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