The rapid growth of digital communications and extensive data exchange have made computer networks integral to organizational operations. However, this increased connectivity has also expanded the attack surface, introducing significant security risks. This paper provides a comprehensive review of Intrusion Detection System (IDS) technologies for network security, examining both traditional methods and recent advancements. The review covers IDS architectures and types, key detection techniques, datasets and test environments, and implementations in modern network environments such as cloud computing, virtualized networks, Internet of Things (IoT), and industrial control systems. It also addresses current challenges, including scalability, performance, and the reduction of false positives and negatives. Special attention is given to the integration of advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML), and the potential of distributed technologies such as blockchain. By maintaining a broad-spectrum analysis, this review aims to offer a holistic view of the state-of-the-art in IDSs, support a diverse audience, and identify future research and development directions in this critical area of cybersecurity.

Artificial Neural Networks (ANNs) improve battery management in electric vehicles (EVs) by enhancing the safety, durability, and reliability of electrochemical batteries, particularly through improvements in the State of Charge (SOC) estimation. EV batteries operate under demanding conditions, which can affect performance and, in extreme cases, lead to critical failures such as thermal runaway—an exothermic chain reaction that may result in overheating, fires, and even explosions. Addressing these risks requires advanced diagnostic and management strategies, and machine learning presents a powerful solution due to its ability to adapt across multiple facets of battery management. The versatility of ML enables its application to material discovery, model development, quality control, real-time monitoring, charge optimization, and fault detection, positioning it as an essential technology for modern battery management systems. Specifically, ANN models excel at detecting subtle, complex patterns that reflect battery health and performance, crucial for accurate SOC estimation. The effectiveness of ML applications in this domain, however, is highly dependent on the selection of quality datasets, relevant features, and suitable algorithms. Advanced techniques such as active learning are being explored to enhance ANN model performance by improving the models’ responsiveness to diverse and nuanced battery behavior. This compact survey consolidates recent advances in machine learning for SOC estimation, analyzing the current state of the field and highlighting the challenges and opportunities that remain. By structuring insights from the extensive literature, this paper aims to establish ANNs as a foundational tool in next-generation battery management systems, ultimately supporting safer and more efficient EVs through real-time fault detection, accurate SOC estimation, and robust safety protocols. Future research directions include refining dataset quality, optimizing algorithm selection, and enhancing diagnostic precision, thereby broadening ANNs’ role in ensuring reliable battery management in electric vehicles.

Modern engineering systems require reliable and timely Fault Detection and Diagnosis (FDD) to ensure operational safety and resilience. Traditional model-based and rule-based approaches, although interpretable, exhibit limited scalability and adaptability in complex, data-intensive environments. This survey provides a systematic overview of recent studies exploring Machine Learning (ML) and Artificial Intelligence (AI) techniques for FDD across industrial, energy, Cyber-Physical Systems (CPS)/Internet of Things (IoT), and cybersecurity domains. Deep architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Graph Neural Networks (GNNs) are compared with unsupervised, hybrid, and physics-informed frameworks, emphasizing their respective strengths in adaptability, robustness, and interpretability. Quantitative synthesis and radar-based assessments suggest that AI-driven FDD approaches offer increased adaptability, scalability, and early fault detection capabilities compared to classical methods, while also introducing new challenges related to interpretability, robustness, and deployment. Emerging research directions include the development of foundation and multimodal models, federated learning (FL), and privacy-preserving learning, as well as physics-guided trustworthy AI. These trends indicate a paradigm shift toward self-adaptive, interpretable, and collaborative FDD systems capable of sustaining reliability, transparency, and autonomy across critical infrastructures.

This article aims to evaluate the runtime effectiveness of various one-class classification (OCC) techniques for anomaly detection in an industrial scenario reproduced in a laboratory setting. To address the limitations posed by restricted access to proprietary data, the study explores OCC methods that learn solely from legitimate network traffic, without requiring labeled malicious samples. After analyzing major publicly available datasets, such as KDD Cup 1999 and TON-IoT, as well as the most widely used OCC techniques, a lightweight and modular intrusion detection system (IDS) was developed in Python. The system was tested in real time on an experimental platform based on Raspberry Pi, within a simulated client–server environment using the NFSv4 protocol over TCP/UDP. Several OCC models were compared, including One-Class SVM, Autoencoder, VAE, and Isolation Forest. The results showed strong performance in terms of detection accuracy and low latency, with the best outcomes achieved using the UNSW-NB15 dataset. The article concludes with a discussion of additional strategies to enhance the runtime analysis of these algorithms, offering insights into potential future applications and improvement directions.

The luxury yacht market is increasingly driven by a demand for sustainability, emphasizing the need for enhanced monitoring and optimization of energy consumption, particularly in the yacht’s hotel sector. To address this demand, we propose a novel system that leverages artificial intelligence (AI) and digital twin (DT) technologies. This system integrates various network-connected sensors, smart plugs, and actuators, such as HVAC systems, lighting, and automated blinds, and is orchestrated by an advanced AI algorithm. Given the challenge of limited experimental data on energy consumption and passenger habits, our approach involves the development of a modular digital twin of the yacht. This DT enables the simulation of different configurations and operational scenarios, generating substantial virtual data for training AI models. Specifically, we employ a Long-Short-Term Memory (LSTM) neural network for time series analysis, which predicts future consumption based on current usage patterns and passenger behavior. This methodology offers a general framework applicable to various yacht designs, enhancing sustainability and operational efficiency across the industry.

Generative artificial intelligence and foundation models have changed machine learning by allowing systems to produce readable text, realistic images, and other multimodal content with little direct input from a user. Foundation models are large neural networks trained on very large and varied datasets, and they form the core of many current generative AI (GenAI) systems. Their rapid development has led to major advances in areas like natural language processing, computer vision, multimodal learning, and robotics. Examples include GPT, LLaMA, and diffusion-based architectures, such as models often used for image generation. Systems such as Stable Diffusion show this shift by illustrating how AI can interpret information, draw basic inferences, and produce new outputs using more than one type of data. This review surveys common foundation model architectures and examines what they can do in generative tasks. It reviews Transformer, diffusion, and multimodal architectures, focusing on methods that support scaling and transfer across domains. The paper also reviews key approaches to pretraining and fine-tuning, including self-supervised learning, instruction tuning, and parameter-efficient adaptation, which support these systems’ ability to generalize across tasks. In addition to the technical details, this review discusses how GenAI is being used for text generation, image synthesis, robotics, and biomedical research. The study also notes continuing challenges, such as the high computing and energy demands of large models, ethical concerns about data bias and misinformation, and worries about privacy, reliability, and responsible use of AI in real settings. This review brings together ideas about model design, training methods, and social implications to point future research toward GenAI systems that are efficient, easy to interpret, and reliable, while supporting scientific progress and ethical responsibility.