Isbn: 9786630145168 - ai enhanced blockchain security for iot networks: machine learning, bio inspired optimization, and trust driven architectures for next generation networks (7 resultados)

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Paperback. Condición: new. Paperback. The rapid expansion of IoT and cloud-enabled wireless systems has created significant security challenges, particularly for resource-constrained devices with limited energy, computational capacity, and network resources. These constraints make IoT nodes highly susceptible to cyber threats, including Distributed Denial-of-Service (DDoS) attacks, Sybil attacks, packet tampering, data manipulation, and unauthorized access. To address these challenges, this research proposes an intelligent and integrated security framework that combines machine learning-based anomaly detection, blockchain-enabled authentication, and bio-inspired optimization techniques to enhance security, trustworthiness, scalability, and overall network performance. The framework utilizes supervised multiclass and one-class machine learning classifiers to identify both known and previously unseen anomalies in near real time using measurable network and communication parameters. To ensure data integrity, transparency, and decentralized trust management, a blockchain architecture incorporating a novel Proof of Iterative Trust (PoIT) consensus mechanism is employed. Furthermore, cloud-scale efficiency is improved. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 168 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The rapid expansion of IoT and cloud-enabled wireless systems has created significant security challenges, particularly for resource-constrained devices with limited energy, computational capacity, and network resources. These constraints make IoT nodes highly susceptible to cyber threats, including Distributed Denial-of-Service (DDoS) attacks, Sybil attacks, packet tampering, data manipulation, and unauthorized access. To address these challenges, this research proposes an intelligent and integrated security framework that combines machine learning-based anomaly detection, blockchain-enabled authentication, and bio-inspired optimization techniques to enhance security, trustworthiness, scalability, and overall network performance. The framework utilizes supervised multiclass and one-class machine learning classifiers to identify both known and previously unseen anomalies in near real time using measurable network and communication parameters. To ensure data integrity, transparency, and decentralized trust management, a blockchain architecture incorporating a novel Proof of Iterative Trust (PoIT) consensus mechanism is employed. Furthermore, cloud-scale efficiency is improved.…

Taschenbuch. Condición: Neu. AI Enhanced Blockchain Security for IoT Networks | Machine Learning, Bio Inspired Optimization, and Trust Driven Architectures for Next Generation Networks | Shubhangi Rathkanthiwar (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630145168 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…

Paperback. Condición: new. Paperback. The rapid expansion of IoT and cloud-enabled wireless systems has created significant security challenges, particularly for resource-constrained devices with limited energy, computational capacity, and network resources. These constraints make IoT nodes highly susceptible to cyber threats, including Distributed Denial-of-Service (DDoS) attacks, Sybil attacks, packet tampering, data manipulation, and unauthorized access. To address these challenges, this research proposes an intelligent and integrated security framework that combines machine learning-based anomaly detection, blockchain-enabled authentication, and bio-inspired optimization techniques to enhance security, trustworthiness, scalability, and overall network performance. The framework utilizes supervised multiclass and one-class machine learning classifiers to identify both known and previously unseen anomalies in near real time using measurable network and communication parameters. To ensure data integrity, transparency, and decentralized trust management, a blockchain architecture incorporating a novel Proof of Iterative Trust (PoIT) consensus mechanism is employed. Furthermore, cloud-scale efficiency is improved. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…