Forecasting peak electrical energy consumption is important because it allows utilities to properly plan for the production and distribution of electrical energy. This reduces operating costs and avoids power outages. In addition, it can help reduce environmental impact by allowing for more efficient power generation and reducing the need for additional fossil fuels during periods of high demand. In the current work, electric power consumption data from “Compagnie Electrique du Benin (CEB)” was used to deduce the peak electric power consumption at peak hours. The peak consumption of electric power was predicted using hybrid approaches based on traditional time series prediction methods (autoregressive integrated moving average (ARIMA)) and deep learning methods (long short-term memory (LSTM), gated recurrent unit (GRU)). The ARIMA approach was used to model the trend term, while deep learning approaches were employed to interpret the fluctuation term, and the outputs from these models were combined to provide the final result. The hybrid approach, ARIMA-LSTM, provided the best prediction performance with root mean square error (RMSE) of 7.35, while for the ARIMA-GRU hybrid approach, the RMSE was 9.60. Overall, the hybrid approaches outperformed the single approaches, such as GRU, LSTM, and ARIMA, which exhibited RMSE values of 18.11, 18.74, and 49.90, respectively.

Recent events show a significant increase in the number of disasters with natural and/or technological causes, which could have potentially serious consequences for critical infrastructures. Were these infrastructures to fail or be destroyed, the resulting cascade effect (chain of accidents) could lead to catastrophic damage and affect not only the plants, but also people, the environment and the economy. This rise in the number of disasters over the years is due to industrial and human activity as well as society’s sensitivity to major events (hazards). The construction of industrial complexes brings with it stocks of hazardous substances, increased transport infrastructure (road, railways, shipping and pipelines), a rise in population and its concentration, malicious behavior and human error. This paper presents a methodology for carrying out risk assessments on critical infrastructure through the analysis of cascade effects. The proposed methodology can be used to provide critical infrastructures with greater protection and to limit/mitigate the extent of the damage caused by their failure. The paper ends with a conclusion and as series of proposal for improving such an analysis.

The combined success of social networking sites and smartphones has changed the way people communicate. It is now possible to publish and track contents in real time at any time and from anywhere. The large number of users on social platforms constitutes an unprecedented opportunity for attack for malicious users. Social engineering techniques, spammers, phishing and malicious attacks are examples of threats that can lead to data loss, data theft, identity theft, etc. The detection of suspicious messages or profiles is mainly covered in the literature as a binary and static classification problem. In this paper, we propose a dynamic behavioral framework for identifying suspicious profiles on social networking sites. This approach is based on three indicators: balance, energy and anomaly, synthesized from daily activities of users. We demonstrate that sensing users regularly, even on few indicators, enables suspicious behaviour to be predicted with a high level of accuracy. The low calculation costs of the approach makes it embeddable into smartphones of social networking users for inferring trust scores to their contacts.

Everyday more than fifty million messages are generated by about two hundred million profiles on Twitter. Some users attempt to exploit the success of this micro logging platform and its relative freedom to perform malicious actions that can lead to identity or data theft. This work aims to propose a framework to assess suspicious behavior on Twitter. We present a tool developed for Scoring Suspicious Profiles On Twitter (SPOT 1.0) through a three-dimensional indicator that involves the degree of aggressiveness, the visibility and the level of danger.