Abstract
Energy systems monitoring is a key factor in reducing global energy consumption. Although intrusive approaches can be adopted, Non-Intrusive Load Monitoring (NILM), which targets estimation of individual-appliance consumption from aggregated electricity measurements, is the main choice for retrofitting the plethora of non-smart electrical installations. Contemporary NILM requirements include high-frequency power-line sampling, efficient disaggregation algorithms that can, potentially, run locally, on the monitoring device, to offload the remote fog/cloud infrastructure, and various open datasets to allow the development of such algorithms. Towards all these directions, in this paper, a versatile monitoring device to support NILM research is presented. It offers high sampling frequency, sufficiency of local resources, and has been designed to be used in different environments (residential, commercial, industrial). Actually, it was deployed in all these environments, with multiple copies of it being used in two different industrial facilities for over two years. The developed device’s cost has been kept low, its accuracy has been experimentally verified to be sufficiently high, while its key hardware characteristics, in the context of NILM research, compare favorably with those of other devices in the literature. Along with the device, a residential, high-frequency dataset is also presented and made publicly available.

Abstract
Non-Intrusive Load Monitoring (NILM) aims to determine individual-appliance energy consumption with minimum cost, by decomposing aggregated electricity measurements. Although important for achieving energy conservation and cost minimization, NILM requires high-frequency sampling rates to provide accurate results. This requirement significantly increases the need for storage and computational resources in the electric utility’s fog/ cloud infrastructure, and for bandwidth on the customer’s side. To resolve these issues, on-site disaggregation, i.e., on the monitoring device, can be employed. However, to keep device-cost low, lightweight NILM algorithms are needed. To this end, a lightweight event-detection algorithm designed to ease on-site implementation, on either software or hardware, is proposed. Event detection is the first, critical half of the well-established event-based NILM approach; it identifies appliance state changes (events). Although a few lightweight event-detection techniques, utilizing high-frequency data, have been presented in the literature, their performance is relatively low in complex-load cases. The proposed algorithm utilizes simple-to-compute features and employs multiple simple criteria to declare an event as detected, and slope-coefficient inspection to identify steady states. Moreover, it can detect events with very small time difference between them. Comparisons show that its performance is superior even against more complex event-detection approaches, while its low computational cost is also verified.

Abstract
This paper presents the progress of PREDIVIS project, on the development of a novel energy disaggregation hardware/software tool towards energy efficiency and predictive maintenance. The PREDIVIS project involves the use of an edge-cloud hybrid computing architecture, hardware accelerated algorithms, machine/deep learning, and big data approaches for the analysis of high frequency electrical loads. The project will deliver an advanced and innovative solution for energy disaggregation intended for industrial applications, commercial buildings and households. A main scope of the project is to address the problem of predictive maintenance by providing a cost-efficient solution with industry 4.0 features.

Abstract
Botnet armies constitute a major and continuous threat to the Internet. Their number, diversity, and power grows with each passing day, and the last years we are witnessing their rapid expansion to mobile and even IoT devices. The work at hand focuses on botnets which comprise mobile devices (e.g. smartphones), and aims to raise the alarm on a couple of advanced Command and Control (C&C) architectures that capitalize on Tor's hidden services (HS) and DNS protocol. Via the use of such architectures, the goal of the perpetrator is dual; first to further obfuscate their identity and minimize the botnet's forensic signal, and second to augment the resilience of their army. The novelty of the introduced architectures is that it does not rely on static C&C servers, but on rotating ones, which can be reached by other botnet members through their (varied) onion address. Also, we propose a scheme called "Tor fluxing", which opposite to legacy IP or DNS fluxing, does not rely on A type of DNS resource records but on TXT ones. We demonstrate the soundness and effectiveness of the introduced C&C constructions via a proof-of-concept implementation, and suggest possible remedies.