Anomaly detection in network traffic is a critical aspect of network security, particularly in defending against the increasing sophistication of cyber threats. This study investigates the application of various machine learning models for detecting anomalies in network traffic, specifically focusing on their effectiveness in addressing challenges such as class imbalance and feature complexity. The models assessed include Isolation Forest, Naive Bayes, XGBoost, LightGBM, and SVM classification. Through comprehensive evaluation, this research explores both supervised and unsupervised approaches, comparing their performance across key metrics like accuracy, F1-score, and recall. The results reveal that while models like XGBoost and LightGBM exhibit impressive performance, with LightGBM achieving near-perfect training accuracy (1.0) and solid test accuracy (0.85), others like Isolation Forest show limitations with low accuracy. The study highlights the strengths and weaknesses of each model, providing valuable insights into their practical application for network anomaly detection. By comparing different algorithms, this research contributes to advancing the application of machine learning in network security, offering guidance on model selection and optimization for improved detection of cyber threats.

The verdict in the case of Skatteverket v. David Hedqvist (Kokott, Advocate General (2015) Opinion delivered on 16 July 2015, Case C-264/14. ECLI:EU:C:2015:498. Available via TandF Online. https://doi.org/10.1080/20488432.2015.1096631.) is crucial for understanding how the EU treats virtual currencies, such as Bitcoin, in terms of Value-added Tax (VAT). This case involved the Swedish citizen David Hedqist who was seeking clarity from the Swedish Tax Authority Skatteverket on exchanging money for Bitcoins. The case set a precedent exempting such services from VAT under the EU’s VAT Directive (Council Directive 2006/112/EC (2006) On the common system of value added tax. OJ L347. Available via EUR-Lex. https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:32006L0112. Accessed 3 January 2024.). Specifically, Article 135(1)(e) of the EU’s VAT Directive excludes those transactions from VAT that include money-related transactions, that include deals or negotiations about different kinds of money, including cash and coins that are officially legal tender, i.e., used for buying things, except for collectable items like special coins or notes that people collect but do not use as a means of payment. Skatteverket (Kokott, Advocate General (2015) Opinion delivered on 16 July 2015, Case C-264/14. ECLI:EU:C:2015:498. Available via TandF Online. https://doi.org/10.1080/20488432.2015.1096631.) clarified that cash transactions are not subject to VAT, even though they are considered services for VAT purposes.Despite this clarity, the evolving landscape of digital assets’ uniqueness, including Non-Fungible Tokens (Alawadhi KM, Alshamali N (2022) NFTs Emergence in Financial Markets and their Correlation with DeFis and Cryptocurrencies. Applied Economics and Finance 9:108. https://doi.org/10.11114/aef.v9i1.5444. Available at CORE. https://core.ac.uk/download/pdf/524752899.pdf. Accessed 3 January 2024.), continues to challenge VAT frameworks across member states.Using insights from the European Commission’s Working Paper 1060, this article advocates for a unified approach tailored to digital and crypto services, addressing complexities in NFT taxation to reduce uncertainty and foster market cohesion. The findings highlight the importance of legislative changes and increased cross-border collaboration, as well as provide recommendations for policymakers and stakeholders in the digital finance and platform sector (European Commission (2024) Working Paper 1060. Available at: https://ec.europa.eu/info/publications/working-paper-1060_en. Accessed 3 March 2024.). By proposing strategic harmonisation of VAT enforcement, the research helps to improve tax compliance and support long-term growth in the EU’s digital market (Cappai M (2023) The role of private and public regulation in the case study of crypto-assets: The Italian move towards participatory regulation. Computer Law & Security Review 49:105831. Available at: https://www.sciencedirect.com/journal/computer-law-and-security-review/vol/49/suppl/C.; Hasa J (2021) Digitaalisten palvelujen rajat ylittävä kuluttajakauppa ja laajeneva arvonlisäveron erityisjärjestelmä. Licentiate thesis. University of Lapland, Faculty of Law. Available at: https://lauda.ulapland.fi/bitstream/handle/10024/64771/Hasa_Juho.pdf?sequence=1. Accessed 1 March 2024.).

The medical device sector adheres to strict regulatory frameworks, requiring precise adherence to quality assurance (QA) processes during the production process. Conventional quality assurance (QA) approaches, although successful, sometimes require substantial time and resource allocations, resulting in possible obstacles and higher expenses. The emergence of Artificial Intelligence (AI) in recent years has completely transformed quality assurance (QA) methods in different sectors, providing unparalleled prospects for improved productivity, precision, and scalability. This research examines the possibility of using AI technologies to enhance quality assurance processes in the manufacturing of medical devices. Manufacturers may improve product quality and streamline production workflows by utilising AI techniques like machine learning, computer vision, and natural language processing to automate and optimize important QA procedures. Artificial intelligence systems can analyse large amounts of data to find abnormalities, uncover flaws, and anticipate any problems in real-time. This allows for proactive intervention and reduces the chances of non-compliance hazards. In addition, AI-powered QA systems provide adaptive learning capabilities, constantly enhancing performance through feedback and adapting to changing regulatory needs. The incorporation of artificial intelligence (AI) into current quality management systems enables smooth and efficient sharing of data and compatibility, promoting a comprehensive approach to quality control throughout the whole production process.

Smart grids themselves have emerged as vital structures of the up-to-date practical power systems or electricity networks that incorporate high technologies and information handling. Yet, they are more susceptible to an adversarial attack that can interfere with the critical functions like energy distribution and faults detection. This paper therefore proposes a new alternative to developing a DL and ML framework for identifying adversarial attacks on smart grids. After analyses of the performances of Logistic Regression, Perceptron, Gaussian Naive Bayes and Multi-Layer Perceptron, LSTM network has better results with an accuracy of 99.81%. The suggested framework strengthens smart grid immunity to cyber threats such as DoS attacks, back door injections, and adversarial perturbations while increasing energy distribution stability and security. For enhancing smart grid security, our results emphasize the importance of integration of ML and DL techniques and provide such an understanding of threat environment for future research and development on threat identification.

Stable operations of modern power networks depend on the reliability of smart grids. The expansion of modern power systems combined with renewable energy applications and decentralized networks creates difficulties for traditional fault detection methods because of high-dimensional data combined with measurement noise and delayed fault identification processes. A deep learning solution based on Phasor Measurement Unit (PMU) image data detection for smart grid faults is presented in this work. The proposed method achieves accurate and efficient fault classification through the conversion of PMU data to images and then utilization of advanced deep learning models VGG16 combined with Convolutional Neural Networks (CNNs). The experimental findings show that VGG16 reaches 98.75% accuracy in fault classification while surpassing CNNs that delivered 94.44% accuracy. In addition to reporting high classification accuracy, the study proposes a new hybrid deep learning framework that combines transfer learning and adaptive thresholding techniques. The image-based deep learning system extracts features automatically, reducing the need for human preprocessing to effectively detect fault patterns. The methodology enables immediate fault detection, allowing operators to make critical decisions to avoid major power outages. According to the research, artificial intelligence has the potential to significantly improve smart grid resilience, reduce economic losses, and improve power system operational efficiency. The research advances AI developments in energy management by developing advanced, powerful fault detection methods that will guide future changes to power system networks.

Continuous operations in extreme environments require a reliable energy supply for autonomous systems. The proposed research presents a novel AI-driven system that averts power system blackouts by utilizing autonomous robots trained in artificial intelligence. The robots monitor power system signals in real-time to detect anomalies while employing their intelligence to select optimal distribution methods. This research combined multiple machine learning models utilizing power system attack data from Mississippi State University and Oak Ridge National Laboratory to ascertain their efficacy in anomaly detection and enhancing energy resilience. In power system threat detection, the ExtraTree Classifier and Random Forest Classifier demonstrate optimal accuracy at 95%, confirming their ability to ensure energy stability. In addition to XGBoost (88%), Bagging (91%), and Decision Tree (86%), the ExtraTree Classifier demonstrates superior performance; however, Gradient Boosting (77%) and Logistic Regression (63%) need improvement to ensure reliable performance in critical applications. The proposed method implements XAI techniques to make the system explainable while improving decision-making reliability. Future research will build on this work by augmenting data availability, integrating renewable energy systems into the research framework, and enhancing self-healing capabilities and robotic mobility to further improve power grid resilience. The developed findings on AI-driven energy automation of this mathematical applicability exploration created scalable intelligent solutions to tackle power stability challenges in unpredictable environmental conditions.