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The ubiquity of Android smartphones makes them targets of sophisticated malware, which maintain long-term stealth, particularly by offloading attack steps to benign apps. Such malware leaves little to no trace in logs, and the attack steps become difficult to discern from benign app functionality. Endpoint detection and response (EDR) systems provide live forensic capabilities that enable anomaly detection techniques to detect anomalous behavior in application logs after an app hijack. However, this presents a challenge, as state-of-the-art EDRs rely on device and third-party application logs, which may not include evidence of attack steps, thus prohibiting anomaly detection techniques from exposing anomalous behavior. While, theoretically, all the evidence resides in volatile memory, its ephemerality necessitates timely collection, and its extraction requires device rooting or app repackaging. We present VEDRANDO, an enhanced EDR for Android that accomplishes (i) the challenge of timely collection of volatile memory artefacts and (ii) the detection of a class of stealthy attacks that hijack benign applications. VEDRANDO leverages memory forensics and app virtualization techniques to collect timely evidence from memory, which allows uncovering attack steps currently uncollected by the state-of-the-art EDRs. The results showed that, with less than 5% CPU overhead compared to normal usage, VEDRANDO could uniquely collect and fully reconstruct the stealthy attack steps of ten realistic messaging hijack attacks using standard anomaly detection techniques, without requiring device or app modification. [ABSTRACT FROM AUTHOR]

The phenomenon of population decline—with all its associated economic, environmental and social consequences—characterizes many small rural municipalities. This study aims to discuss the role that small rural municipalities can have in addressing this phenomenon by promoting rural regeneration and attracting newcomers. The framework of the analysis is based on the neo-endogenous theories of rural development, which consider the interaction between endogenous and exogenous resources and forces. To analyse the role of small rural municipalities, this paper refers to the findings of a case study on the Italian rural village of Castel del Giudice and of the "confrontation" activities carried out in another village, Malito. The research suggests that a participatory process activated by a municipality can produce positive social and economic benefits for the local community. This process can enhance territorial cohesion, contribute to halting emigration, and favour the establishment of newcomers. Nevertheless, the research highlights that this approach requires adequate organizational knowledge, including political and administrative functionaries with appropriate skills who are also capable of experimenting with new modalities of fundraising. Moreover, it emerges that non-local resources and forces can support a solid participatory process focused on valorising local resources rather than having an extractive approach. [ABSTRACT FROM AUTHOR]

This paper addresses sustainable development processes based on biocultural heritage, sense of place, and socio-cultural innovation and inclusiveness in the rural context, particularly focusing different scales of endogenous/neo-endogenous rural regeneration processes. Ethnographic and grounded case studies allow a critical analysis of different forms of rural development from a multidisciplinary perspective based on old and new rurality, rural and local communities' participation, resilience and regeneration processes, sense of place, belongingness, and "restanza". The three cases are situated in three areas of Italy: the ecomuseum of pastoralism in Pontebernardo in the northern region of Piedmont as a driver of local shared development pathways; the municipality of Castel del Giudice, in the Central-Southern region of Molise, enabling different and integrated local regeneration actions; and the Association "Casa delle AgriCulture"/Green Night Festival in Castiglione d'Otranto in the Southern region of Puglia as a performative opportunity to define innovative and transversal as well as community-oriented activities. In these contexts, different local regeneration programs were applied in order to provide a critical evaluation of the knowledge and practices of sustainability in terms of their entanglements with biocultural heritage and socio-cultural innovation. The present analysis insists on the impact of biocultural heritage in regeneration processes in rural regions and endogenous/neo-endogenous factors in rural sustainable development. [ABSTRACT FROM AUTHOR]

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand management. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the most significant application opportunities and outline the challenges in real-time and energy-efficient management of the distributed resources available in mobile devices and at the Internet-to-Data Center. We also present an energy-efficient adaptive scheduler for Vehicular Fog Computing (VFC) that operates at the edge of a vehicular network, connected to the served Vehicular Clients (VCs) through an Infrastructure-to-Vehicular (I2V) over multiple Foglets (Fls). The scheduler optimizes the energy by leveraging the heterogeneity of Fls, where the Fl provider shapes the system workload by maximizing the task admission rate over data transfer and computation. The presented scheduling algorithm demonstrates that the resulting adaptive scheduler allows scalable and distributed implementation. [ABSTRACT FROM AUTHOR]

Wind generators are exposed to numerous destructive forces such as lightning and are therefore vulnerable to these phenomena. To evaluate the transient behavior of a wind power plant during direct and indirect strikes, modeling of all relevant components is required. Among the protective and control components of wind turbines, the grounding system is the most important element for protection against lightning strikes. This paper examines the impact of nonlinear soil ionization behavior and frequency dependency on a wind turbine in order to model a sufficient protection scheme to reduce overvoltage and make the system tolerable against transitions. The high frequency models of other equipment such as transformers, horizontal conductors, vertical rods, surge arresters and underground cables must also be taken into account to design the grounding system. Our Proposed Modified Grounding Scheme (PMGS) is to reduce the maximum transient overvoltages. We simulate the model in a restructured version of the Electromagnetic Transient Program (EMTP-RV) software to examine the effectiveness of the system. We then apply the simulated results to pair of turbines that are interconnected with a frequency-dependent cable. We carry out the simulation for direct and indirect lightning strikes. The results indicate that the MGS can lead to considerably more than a 50% reduction in transient voltages for lightning and thus leads to more reliable networks. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of minimizing the total daily energy cost in a smart residential building composed of multiple smart homes with the aim of reducing the cost of energy bills and the greenhouse gas emissions under different system constraints and user preferences. As the household appliances contribute significantly to the energy consumption of the smart houses, it is possible to decrease electricity cost in buildings by scheduling the operation of domestic appliances. In this paper, we propose an optimization model for jointly minimizing electricity costs and CO2 emissions by considering consumer preferences in smart buildings that are equipped with distributed energy resources (DERs). Both controllable and uncontrollable tasks and DER operations are scheduled according to the real-time price of electricity and a peak demand charge to reduce the peak demand on the grid. We formulate the daily energy consumption scheduling problem in multiple smart homes from economic and environmental perspectives and exploit a mixed integer linear programming technique to solve it. We validated the proposed approach through extensive experimental analysis. The results of the experiment show that the proposed approach can decrease both CO2 emissions and the daily energy cost. [ABSTRACT FROM AUTHOR]