Demand side management is a critical issue in the energy sector. Recent events such as the global energy crisis, costs, the necessity to reduce greenhouse emissions, and extreme weather conditions have increased the need for energy efficiency. Thus, accurately predicting energy consumption is one of the key steps in addressing inefficiency in energy consumption and its optimization. In this regard, accurate predictions on a daily, hourly, and minute-by-minute basis would not only minimize wastage but would also help to save costs. In this article, we propose intelligent models using ensembles of convolutional neural network (CNN), long-short-term memory (LSTM), bi-directional LSTM and gated recurrent units (GRUs) neural network models for daily, hourly, and minute-by-minute predictions of energy consumptions in smart buildings. The proposed models outperform state-of-the-art deep neural network models for predicting minute-by-minute energy consumption, with a mean square error of 0.109. The evaluated hybrid models also capture more latent trends in the data than traditional single models. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.

Energy consumption is a critical issue in the energy sector, and recent events such as the global energy crisis, costs, the need to reduce greenhouse emissions, and extreme weather conditions have increased the demand for energy efficiency. Accurately predicting energy consumption is one of the key steps to addressing inefficiency in energy consumption, and its optimization. In this regard, accurate predictions of energy consumption would not only help to minimize wastage, but also to save cost. In this article, we propose intelligent models using an ensemble of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) neural networks for predicting energy consumption in smart buildings. The proposed model outperforms other state-of-the-art deep learning models for predicting minute energy consumption, with a mean square error of 0.109. The proposed model also accurately captures latent trends in the data that other models struggle to capture. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.

The increasing integration of digital technologies in healthcare has expanded the attack surface for privacy violations in critical systems such as electronic health records (EHRs), telehealth platforms, and medical device software. However, current vulnerability detection datasets lack domain-specific privacy annotations essential for compliance with healthcare regulations like HIPAA and GDPR. This study presents C3-VULMAP, a novel and large-scale dataset explicitly designed for privacy-aware vulnerability detection in healthcare software. The dataset comprises over 30,000 vulnerable and 7.8 million non-vulnerable C/C++ functions, annotated with CWE categories and systematically mapped to LINDDUN privacy threat types. The objective is to support the development of automated, privacy-focused detection systems that can identify fine-grained software vulnerabilities in healthcare environments. To achieve this, we developed a hybrid construction methodology combining manual threat modeling, LLM-assisted synthetic generation, and multi-source aggregation. We then conducted comprehensive evaluations using traditional machine learning algorithms (Support Vector Machines, XGBoost), graph neural networks (Devign, Reveal), and transformer-based models (CodeBERT, RoBERTa, CodeT5). The results demonstrate that transformer models, such as RoBERTa, achieve high detection performance (F1 = 0.987), while Reveal leads GNN-based methods (F1 = 0.993), with different models excelling across specific privacy threat categories. These findings validate C3-VULMAP as a powerful benchmarking resource and show its potential to guide the development of privacy-preserving, secure-by-design software in embedded and electronic healthcare systems. The dataset fills a critical gap in privacy threat modeling and vulnerability detection and is positioned to support future research in cybersecurity and intelligent electronic systems for healthcare.

We introduce C3-VULMAP, a novel dataset designed to advance privacy-aware vulnerability detection in healthcare systems, addressing the critical need for secure software amid increasing cyber threats to sensitive patient data. By integrating the LINDDUN privacy threat modelling framework with Common Weakness Enumeration (CWE) classifications, we systematically map privacy-specific vulnerabilities in C/C++ code, focusing on healthcare applications such as electronic health records and medical devices. We aggregate real-world and synthetic vulnerability data from diverse sources, creating a comprehensive dataset of vulnerable and non-vulnerable functions across 776 CWE types. We evaluate our dataset using graph neural networks, transformer-based models, and traditional machine learning approaches, achieving high precision and recall, with RoBERTa and Reveal models excelling in detecting Linkability and Identifiability threats (F1-scores up to 0.9968). Our results demonstrate a superior generalization of the dataset for healthcare contexts compared to generic datasets, enabling robust, compliance-driven vulnerability detection. This dataset bridges the gap between privacy and security engineering, offering a foundational resource for developing trustworthy healthcare software and fostering collaborative advancements in cybersecurity research.

Distributed ledger technology (DLT), decentralized finance (DeFi), blockchain – these are terms that have been trending especially in technology circles. Today, blockchain has gained more acceptance, but sceptics continue to raise concerns about the technology's scalability, security, and long-term viability. There are also concerns regarding its being associated with crime and the dark web, which might imply it will have negative consequences if adopted. It has its own peculiar cyber security loopholes, but these can be addressed. That notwithstanding, organizations stand to gain from the blockchain in terms of cyber security; especially its qualities of decentralization and immutability. It has been successfully implemented in some sectors with positive results. This chapter seeks to illustrate how the blockchain can be used to boost and optimize cyber security for organizations. Request access from your librarian to read this chapter's full text.