AI and Machine Learning (ML) are powerful and rapidly evolving technologies, reshaping intelligent decision-making, automation, and data-driven problem-solving in various industries. In recent years, the potential of intelligent systems to manage complex data, to learn adaptive patterns, and to assist in autonomous decision-making has been greatly improved by the emergence of new technologies, such as Deep Learning, Transformer-based Models, Generative AI, Large Language Models (LLMs), Explainable AI (XAI), Federated Learning, Edge AI, and Digital Twin. These advancements have empowered the healthcare, manufacturing, agriculture, finance, transportation, education, cybersecurity, and smart city sectors with enhanced efficiency, productivity, and service quality, driving faster AI adoption across these industries. The innovations have contributed to improved efficiency, productivity, and service quality, leading to increased adoption of AI across the healthcare, manufacturing, agriculture, finance, transportation, education, cybersecurity, and smart city sectors. The fundamentals of intelligent AI systems, key learning paradigms, notable technological advances, and applications are discussed in this chapter, providing a comprehensive review of intelligent AI systems and advanced ML. It also explores the potential of AI to solve real-world problems and discusses some of the critical challenges associated with data privacy, model interpretability, computational complexity, algorithmic bias, and ethical considerations. Finally, the chapter proposes new research directions towards the development of trustworthy, explainable, sustainable, and human-centric AI systems. This review offers a brief overview of current research progress and prospects on the development of intelligent AI systems and advanced machine learning, which will facilitate the future generation of trustworthy and ethical AI-based solutions.
Dr.S. Gopi, D. Kumar, Dr Vontela Neelima et al.· Journal of Intelligent Decis...· 0 citations
Rapidly increasing numbers of devices connected to the Internet of Things have greatly changed how modern digital ecosystems operate by facilitating interoperability among industries such as healthcare, intelligent transportation systems, smart city technology and industrial automation. The increasing number of Internet connected devices has created new vulnerabilities for cyber-attacks on these devices, such as DDoS, Botnets, unauthorized access to devices and data breaches. Traditional IoT protection approaches generally fail to address these types of threats due to their limited resources, heterogeneity, and constant changes. In this regard, artificial intelligence has been proposed as a viable method to enhance cybersecurity for IoTs via AI-based systems capable of intelligent threat detection, adaptive learning, and real-time responses.
To provide a clear understanding of AI-based cybersecurity methods for IoTs, this paper presents a systematic literature review (SLR) using a structured review process that follows the PRISMA guidelines. A total of 3072 original research papers on AI-based cybersecurity methods for IoTs published in top indexing databases were obtained. Each selected paper was analysed using a systematic screening process. The studies were grouped into six distinct theme areas, including machine learning-based intrusion detection, deep learning-based anomaly detection, hybrid/ensemble security models, federated learning frameworks, explainable AI, and blockchain-enabled IoT security mechanisms.
Machine learning algorithms demonstrated high computational efficiency in detecting intrusions but lacked the flexibility to adapt to new threats. On the other hand, deep learning algorithms demonstrated a high intrusion detection capability but at a high computational cost. Hybrid/federated learning algorithms represent an emerging class that can deliver both the required accuracy and scalability while preserving user privacy. While several important advancements exist in AI-based cybersecurity methods for IoTs, many additional areas will require significant work before widespread adoption. Some examples include continued reliance on outdated benchmarks to evaluate algorithmic performance; the absence of validation testing for algorithms running in real-time environments; difficulty interpreting results generated by black-box AI-based algorithms; and a lack of collaboration among researchers studying different layers of the edge-cloud architecture.
As a result of synthesising the current state of the art in AI-based cybersecurity methods for IoTs, this paper proposes a conceptual model of an AI-based adaptive cybersecurity system for IoTs, designed to utilise edge intelligence, federated learning, and continuous feedback mechanisms to detect and respond dynamically to potential threats. This paper makes contributions to the current body of knowledge regarding the rapidly changing landscape of IoT cybersecurity by providing an organised, analytical summary of the current state of the art in AI-based cybersecurity methods for IoTs; defining specific research gaps; and providing recommendations for future research to develop scalable, understandable, and adaptable security systems for IoT environments.
M. Prasad, D. Kumar, Debasish Paul· Journal of Intelligent Decis...· 0 citations
