CoralMatrix: A Scalable and Robust Secure Framework for Enhancing IoT Cybersecurity

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 7, 2025

In the current age of digital transformation, Internet Things (IoT) has revolutionized everyday objects, and IoT gateways play a critical role in managing data flow within these networks. However, dynamic extensive nature networks presents significant cybersecurity challenges that necessitate development adaptive security systems to protect against evolving threats. This paper proposes CoralMatrix Security framework, novel approach employs advanced machine learning algorithms. framework incorporates AdaptiNet Intelligence Model, which integrates deep reinforcement for effective real-time threat detection response. To comprehensively evaluate performance this study utilized N-BaIoT dataset, facilitating quantitative analysis provided valuable insights into model's capabilities. The results demonstrate robustness across various dimensions cybersecurity. Notably, achieved high accuracy rate approximately 83.33%, highlighting its effectiveness identifying responding threats real-time. Additionally, research examined framework's scalability, adaptability, resource efficiency, diverse cyber-attack types, all were quantitatively assessed provide comprehensive understanding suggests future work optimize larger adapt continuously emerging threats, aiming expand application scenarios. With proposed algorithms, emerged as promising, efficient, effective, scalable solution Cyber Security.

Language: Английский

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Adaptive Computational Intelligence Algorithms for Efficient Resource Management in Smart Systems

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 9, 2025

The rapid evolution of smart systems, including Internet Things (IoT) devices, grids, and autonomous vehicles, has led to the need for efficient resource management optimize performance, reduce energy consumption, enhance system reliability. This paper presents adaptive computational intelligence (CI) algorithms as an effective solution addressing dynamic challenges in systems. Specifically, we explore application techniques such fuzzy logic, genetic algorithms, particle swarm optimization, neural networks adaptively manage resources like energy, bandwidth, processing power, storage real-time. These CI offer robust decision-making capabilities, enabling systems efficiently allocate based on environmental changes, demands, user preferences. discusses integration these with real-time data acquisition providing a framework scalable management. Additionally, evaluate performance various environments, highlighting their ability efficiency, operational costs, improve overall experience. proposed approach demonstrates significant improvements over traditional techniques, making it promising next-generation

Language: Английский

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IoT and Blockchain in Supply Chain Management for Advancing Sustainability and Operational Optimization

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 18, 2025

The rapid advancement of IoT technologies has emerged as a key driver sustainable development, reshaping industries and societal structures. This study critically examines the intersection sustainability by analyzing contemporary literature on subject. A comprehensive review IoT-driven innovations highlights their transformative impact across sectors such agriculture, smart cities, resource management. research investigates how digitalization, particularly within supply chains, redefines operational strategies enhances metrics. With integration like RFID, blockchain, under Industry 4.0, organizations are revolutionizing process efficiency, transparency, environmental responsibility. To assess these implications, conducts two comparative simulation experiments involving three-party chain in cheese production—one utilizing traditional methods other leveraging IoT-based innovations. Results reveal significant improvements order management efficiency compliance handling, underscoring critical role emerging fostering practices. proposed framework provides valuable insights into broader implications adoption, reinforcing its potential catalyst for global initiatives.

Language: Английский

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Exploring Artificial Intelligence and Data Science-Based Security and its Scope in IoT Use Cases

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 6, 2025

The fast growth of IO networks has resulted in a security crisis besides the development decentralized-based innovations, and such decentralized bases or technologies also made challenges terms speed, performance, scalability. Traditional machine learning-based intrusion detection systems (IDS) are unable to manage intricate non-linear correlations seen massive amounts IoT data. They produce relatively low rates, especially multi-class classification, where many attack types must be addressed. Overcoming these hurdles calls for frameworks: innovative enough accommodate challenge whilst using wealth data produced by devices. Abstract In this paper, we introduce unique MLP-based deep learning architecture settings. This framework includes preprocessing pipeline that optimally normalizes applies one-hot-encoding prepare it classification. We tested algorithms on UNSW-NB15 dataset, commonly used IDS. Mere quantitative results show MLP surpasses classical models like Logistic Regression, SVM, Random Forests, giving precision 97.53%, recall 97.23%, accuracy 97.73% classification task. is undoubtedly scalable provides sufficient mechanism whole ecosystem; hence, can various actual use cases. performance shows could solve new threats developing environments.

Language: Английский

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Hybrid Swarm Intelligence-Based Neural Framework for Optimizing Real-Time Computational Models in Engineering Systems

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 16, 2025

In modern engineering systems, real-time computational models are essential for optimizing performance, enhancing decision-making, and reducing latency in complex environments. This research presents a Hybrid Swarm Intelligence-Based Neural Framework (HSIN-F) to improve the efficiency, accuracy, adaptability of computations. The proposed framework integrates Particle Optimization (PSO), Grey Wolf Optimizer (GWO), Ant Colony (ACO) with Deep Network (DNN) achieve balance between exploration exploitation, enabling optimal model parameter selection overhead. To validate efficiency HSIN-F, experiments were conducted across various applications, including industrial automation, smart grids, IoT-based systems. outperformed conventional optimization techniques terms processing speed, predictive system adaptability. Key performance metrics include: Prediction Accuracy: 98.2% (compared 93.5% traditional models), Computational Latency Reduction: 34.7%, Energy Efficiency Improvement: 27.5%, Error Rate 32.1%. hybrid swarm-based approach effectively adapts dynamic changes scenarios, making it highly suitable applications requiring continuous optimization. Future will explore metaheuristic strategies federated learning-based decentralization further enhance robustness.

Language: Английский

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Material selection and performance analysis of RF-MEMS switch for MM-WAVE applications

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 12, 2025

This paper presents the design, simulation, and investigation of a fundamental structure for capacitive MEMS switches in shunt configuration. The main objective is to select materials that achieve low actuation voltage while maintaining RF dynamic performance, especially mm-wave applications. proposed design consists Fixed-Fixed flexure beam with dimensions 260 μm length, 100 width, 0.5 thickness. Considering impact squeeze film, 60 holes are integrated into membrane, each measuring 64 μm² (8µm x 8µm), final gap 1.9 implemented. suitability membrane dielectric layer has been thoroughly examined through combination theoretical analysis software simulations. Aluminum (Al) emerged as ideal choice preference defensible by its simulated results offer pull-in 4V, quality factor 1.18, switching time 67 microseconds. Similarly, Si3N4 identified appropriate material, offering upstate capacitance 91fF downstate 7.1pF.

Language: Английский

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Security of IoT Device and its Data Transmission on AWS Cloud by Using Hybrid Cryptosystem of ECC and AES

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 12, 2025

The expanding prevalence of the Internet Things (IoT)and its devices presents significant security challenges mostly a lack multi-factor authentication, light encryption, etc. This study uses Elliptical Curve Cryptography (ECC) and Advanced Encryption Standards (AES) to create hybrid method with multiple features for Raspberry Pi data transmission on cloud named Hybrid Cryptosystem ECC +AES. Data gathered transferred offers faster safer encryption mechanism Pi. technique provides notable gain in performance over other previous algorithms by utilizing speed AES secure key exchange ECC. author developed web application implemented algorithm generating sample data, decryption processes, uploading files an Amazon Web Services (AWS) S3 bucket using Python programming which will benefit IoT limited memory computational power.

Language: Английский

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