AI Enhanced Blockchain Security for IoT Networks | Machine Learning, Bio Inspired Optimization, and Trust Driven Architectures for Next Generation Networks

Shubhangi Rathkanthiwar (u. a.)

ISBN 10: 663014516X / ISBN 13: 9786630145168

Published by LAP LAMBERT Academic Publishing, 2026

About this Item

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.

Seller Inventory # 136373079

Bibliographic Details

• Title: AI Enhanced Blockchain Security for IoT Networks | Machine Learning, Bio Inspired Optimization, and Trust Driven Architectures for Next Generation Networks
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2026
• Language: English
• ISBN 10: 663014516X
• ISBN 13: 9786630145168
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 11D millimeters 220 millimeters width by 150 millimeters height by 11 millimeters depth
• Weight: 268 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.