Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 28, 2025

Internet of Things (IoT) is one the most important emerging technologies that supports Metaverse integrating process, by enabling smooth data transfer among physical and virtual domains. Integrating sensor devices, wearables, smart gadgets into environment enables IoT to deepen interactions enhance immersion, both crucial for a completely integrated, data-driven Metaverse. Nevertheless, because devices are often built with minimal hardware connected Internet, they highly susceptible different types cyberattacks, presenting significant security problem maintaining secure infrastructure. Conventional techniques have difficulty countering these evolving threats, highlighting need adaptive solutions powered artificial intelligence (AI). This work seeks improve trust in edge integrated study revolves around hybrid framework combines convolutional neural networks (CNN) machine learning (ML) classifying models, like categorical boosting (CatBoost) light gradient-boosting (LightGBM), further optimized through metaheuristics optimizers leveraged performance. A two-leveled architecture was designed manage intricate data, detection classification attacks within networks. thorough analysis utilizing real-world network dataset validates proposed architecture's efficacy identification specific variants malevolent assaults, classic multi-class challenge. Three experiments were executed open public, where top models attained supreme accuracy 99.83% classification. Additionally, explainable AI methods offered valuable supplementary insights model's decision-making supporting future collection efforts enhancing systems.

Язык: Английский

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Encrypted Network Traffic Classification Using Intelligent Techniques

Cureus Journal of Computer Science., Год журнала: 2025, Номер unknown

Опубликована: Янв. 16, 2025

Язык: Английский

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Pattern Shared Vision Refinement for Enhancing Collaboration and Decision-Making in Government Software Projects

Electronics, Год журнала: 2025, Номер 14(2), С. 334 - 334

Опубликована: Янв. 16, 2025

This study proposes a new approach and explores how pattern recognition enhances collaboration between users Agile teams in software development, focusing on shared resources decision-making efficiency. Using domain-specific modeling languages (DSMLs) within security-by-design framework, the research identifies patterns that support team selection, effort estimation, risk management for Afghanistan’s ministries. These align development with governmental needs by clarifying stakeholder roles fostering cooperation. The builds p-mart-Repository-Programs (P-MARt) repository, integrating data mining, algorithms, ETL (Extract, Transform, Load) processes to develop innovative methodologies. approaches enable dynamic knowledge management, refine documentation, improve project outcomes. Central this is our Pattern Shared Vision Refinement (PSVR) approach, which emphasizes robust collaboration, security, adaptability. By addressing challenges unique operations, PSVR strengthens practices ensures high-quality delivery. analyzing historical trends introducing strategies, underscores critical role of aligning organizational goals. It demonstrates systematic identification can optimize interaction secure consensus, ultimately enhancing engineering outcomes context.

Язык: Английский

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Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 19, 2025

Enhancement of security, personalization, and safety in advanced transportation systems depends on driver identification. In this context, work suggests a new method to find drivers by means Random Forest model optimized using the osprey optimization algorithm (OOA) for feature selection salp swarm (SSO) hyperparameter tuning based driving behavior. The proposed achieves an accuracy 92%, precision 91%, recall 93%, F1-score significantly outperforming traditional machine learning models such as XGBoost, CatBoost, Support Vector Machines. These findings show how strong successful our improved is precisely spotting drivers, thereby providing useful instrument safe quick systems.

Язык: Английский

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Cybersecurity of Automotive Wired Networking Systems: Evolution, Challenges, and Countermeasures

Electronics, Год журнала: 2025, Номер 14(3), С. 471 - 471

Опубликована: Янв. 24, 2025

The evolution of Electrical and Electronic (E/E) architectures in the automotive industry has been a significant factor transformation vehicles from traditional mechanical systems to sophisticated, software-defined machines. With increasing vehicle connectivity growing threats cyberattacks that could compromise safety violate user privacy, incorporation cybersecurity into development process is becoming imperative. As evolve sophisticated interconnected systems, understanding their vulnerabilities becomes essential improve cybersecurity. This paper also discusses role evolving standards regulations, such as ISO 26262 ISO/SAE 21434, ensuring both modern vehicles. offers comprehensive review current challenges cybersecurity, with focus on Controller Area Network (CAN) protocol. Additionally, we explore state-of-the-art countermeasures, focusing Intrusion Detection Systems (IDSs), which are increasingly leveraging artificial intelligence machine learning techniques detect anomalies prevent attacks real time. Through an analysis publicly available CAN datasets, evaluate effectiveness IDS frameworks mitigating these threats.

Язык: Английский

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Performance evaluation of metaheuristics-tuned recurrent networks with VMD decomposition for Amazon sales prediction

International Journal of Data Science and Analytics, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 25, 2024

Язык: Английский

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