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Rama Soni is an Assistant Professor in the Department of Computer Science and Information Technology at Maharishi University of Management and Technology, India. Her contribution focuses on Artificial Intelligence, quantum computing, intelligent communication networks, and emerging 6G technologies.
Artificial Intelligence (AI), quantum computing, and advanced communication networks are swiftly merging into a revolutionary technological framework. Artificial Intelligence offers educational, predictive, optimisation, and self-governing decision-making functionalities, whilst quantum computing presents novel computational paradigms for addressing certain intricate issues. Future networks will progressively incorporate AI-native frameworks, quantum-resistant security, quantum-enhanced optimisation, edge intelligence, digital replicas, and decentralised learning. This section explores the nascent trends, their technological underpinnings, integration frameworks, applications, obstacles, and avenues for research. Special emphasis is placed on AI-native 6G networks, quantum machine learning, quantum communication, federated intelligence, intelligent edge computing, and autonomous network administration. The section additionally offers a theoretical foundation for amalgamating AI, quantum computing, edge/cloud infrastructure, and intelligent communications into a cohesive next-generation computing and networking environment.
The swift expansion of digital infrastructure, cloud computing, the Internet of Things (IoT), and interconnected communication systems has markedly heightened the intricacy of cybersecurity threats. Conventional security measures, such as signature-based intrusion detection systems, frequently fail to recognise novel attacks, advanced malware, and changing network incursion trends. Deep learning presents novel prospects for cybersecurity by autonomously acquiring intricate representations from network traffic, system logs, authentication records, and various security-related information. The section additionally addresses benchmark datasets, assessment metrics, adversarial resilience, interpretable artificial intelligence, privacy safeguards, and deployment factors. Special emphasis is placed on class imbalance, minimising false positives, real-time processing, idea drift, and the ability to generalise to novel threats. The chapter wraps off by outlining prospective research avenues, such as federated learning, self-supervised learning, multimodal security analytics, and collaborative threat intelligence.
The swift advancement of artificial intelligence (AI), blockchain technology, and the Internet of Things (IoT) is revolutionising the architecture of digital ecosystems in sectors such as healthcare, smart cities, industrial automation, agriculture, transportation, and financial services. IoT devices incessantly gather environmental and operational data, AI algorithms scrutinise this information to produce forecasts and facilitate decision-making, while blockchain offers decentralised records, transaction traceability, and programmable methods for implementing designated security protocols. Incorporating these technologies can bolster data integrity, promote automation, facilitate decentralised coordination, and foster confidence among participants who do not depend entirely on a singular authority. This chapter explores the essential concepts, architectural tenets, integration methodologies, and real-world applications of AI, blockchain, and IoT inside secure digital environments. The section additionally addresses assessment criteria, obstacles in implementation, privacy-conscious artificial intelligence, distributed learning, and other research avenues. The suggested framework aims to serve as a conceptual basis for creating interoperable, robust, and reliable digital systems.