Now-a-days, Cybersecurity attacks are becoming increasingly sophisticated and presenting a growing threat to individuals, private and public sectors, especially the Denial Of Service attack (DOS) and its variant Distributed Denial Of Service (DDOS). Dealing with these dangerous threats by using traditional mitigation solutions suffers from several limits and performance issues. To overcome these limitations, Machine Learning (ML) has become one of the key techniques to enrich, complement and enhance the traditional security experiences. In this context, we focus on one of the key processes that improve and optimize Machine Learning DOS-DDOS predicting models: DOS-DDOS feature selection process, particularly the wrapper process. By studying different DOS-DDOS datasets, algorithms and results of several research projects, we have reviewed and evaluated the impact on used wrapper strategies, number of DOS-DDOS features, and many commonly used metrics to evaluate DOS-DDOS prediction models based on the optimized DOS-DDOS features. In this paper, we present three important dashboards that are essential to understand the performance of three wrapper strategies commonly used in DOS-DDOS ML systems: heuristic search algorithms, meta-heuristic search and random search methods. Based on this review and evaluation study, we can observe some of wrapper strategies, algorithms, DOS-DDOS features with a relevant impact can be selected to improve the DOS-DDOS ML existing solutions.

As a range of daily phenomena, Fake News is quickly becoming a longstanding issue affecting individuals, public and private sectors. This major challenge of the connected and modern world can cause many severe and real damages such as manipulating public opinion, damaging reputations, contributing to the loss in stock market value and representing many risks to the global health. With the fast spreading of online misinformation, checking manually Fake News becomes ineffective solution (not obvious, difficult and takes a long time). The improvement of Deep Learning Networks (DLN) can support with high degree of accuracy and efficiency the classical processes of Fake News spotting. One of the keys improvement strategies are optimizing the Word Embedding Layer (WEL) and finding relevant Fake News predicting features. In this context, and based on six DLN architectures, FastText process as WEL and Inverted Pyramid as News Articles Pattern (IPP), the present paper focuses on the assessment of the first news article feature that is hypothesized as affecting the performances of fake news predicting: News Title. By assessing the impact that the Embedding Vector Size (EVS), Window Size (WS) and Minimum Frequency of Words (MFW) in News Titles corpus can have on DLN, the experiments carried out in this paper showed that the News Title feature and FastText process can have a significant improvement on DLN fake news detection with accuracy rates exceeding 98%.

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The election decision-making process is very complex, whether to voters or to political parties. The integration of business intelligence into this process may become one of the key elements that support and improve the election decision-making process. This research paid special attention to the integration of business intelligence into such a process. It is inspired by the IMC model and based on cloud computing. This structural concept offers a definition to the four important levels of decision. These levels take into account the maximum number of factors that may influence the success of an election process.Keywords: Business Intelligence System, election process, Cloud RESUMÉELe processus de prise de décision électorale est un processus complexe. Que ce soit pour les électeurs, ou bien pour les partis politiques. L'intégration de l'informatique décisionnelle dans ce processus peut devenir une des clés importantes, qui peut offrir un appui et apporter une amélioration dans les prises de décisions électorales. La présente investigation a porté une attention particulière sur l'intégration de l'informatique décisionnelle dans le processus électoral, en s'inspirant du modèle IMC, et sur la base de l'informatique nuageuse. La présente conception architecturale propose la définition de quatre importants niveaux décisionnels. Ces niveaux prennent en compte le maximum de facteurs qui peuvent influencer la réussite du processus décisionnel électoral.

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