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Rapid spread and effective integration of Internet of Things (IoT) devices across various sectors has brought unparalleled connectivity and efficiency, but has also introduced amplified vulnerabilities with respect to data breaches. This article offers an in-depth examination of the economic impacts of IoT data breaches, focusing on the associated costs and emerging statistical trends. Through comprehensive analysis, we explore the direct and indirect financial burdens, including immediate response expenses, long-term reputational damage, legal liabilities, and regulatory penalties. Utilizing recent case studies and statistical data, we highlight the economic magnitude of IoT breaches and the factors that amplify their costs. Our research emphasizes the critical need for enhanced IoT security measures and for strategic risk management to protect organizations from substantial financial losses. The findings may serve as a crucial resource for businesses, policymakers, and cybersecurity professionals, advocating for proactive steps to fortify IoT ecosystems against the escalating threat of data breaches. [ABSTRACT FROM AUTHOR]

This paper analyses the recent evolution of the electricity price of one of the East-European countries' Balancing Markets (BM) - Romania, aiming to understand the prices trend and predict them in the current economic and geopolitical context. This is especially important as the electricity producers have to allocate their output between wholesale electricity market, ancillary services markets and BM targeting to maximize value and achieve a sustainable economic development. Therefore, in this paper, we propose an AI-powered electricity price forecast using several types of standout Machine Learning (ML) algorithms such as classifiers and regressors to predict the electricity price on BM. This approach, consisting of two steps, identifies the imbalance sign and significantly enhances the performance of the price forecast. The proposed method offers valuable insights into the market participants' trading opportunities using two prediction solutions. The first prediction solution consists of averaging the results of five ensemble ML algorithms. The second one consists in weighting the results of the five forecasting ML algorithms using either a linear regression or a decision tree algorithm. Thus, we propose to combine supervised and unsupervised ML algorithms and find the fundamentals for creating optimal bidding strategies for electricity market players. [ABSTRACT FROM AUTHOR]