Enhancing Security in Cloud Computing Using Artificial Intelligence (AI)

Cloud computing (CC) technologies (viz artificial intelligence (AI), data science, blockchain, “big data” (BD), etc.) are progressively widespread and practically applied. Researchers face the biggest challenges regarding efficient data access and acquisition. The CPS's efficiency will help researchers advance in CC, cyber‐attacks (CAs), “cyber threats” (CTs), and “CT intelligence” (CTI). Alongside optimally storing the data, one major caveat is security. The best possible protection can enhance data storage/handling to protect end systems from illegal attacks and thefts. Smart environments (SEs) are collections of sensors, actuators, and numerous computing pieces that ease human life. With the booming SEs, data have notably increased, thus demanding smart, optimal management. Sometimes, data processing is inadequate due to risks and resource costs. Some characteristic security difficulties are “advanced persistent threats” (APTs), denial‐of‐service (DoS), eclipse, double‐spending attacks, and other malware. Thus, advanced AI‐reliant anomaly discovery and mitigation tactics can address and disentangle the issues above. Cybersecurity (CS) in SEs, sub‐CPss, and CPSs relates to working well with AI‐based anomaly detection techniques. This chapter investigates challenges, trends, and prospective design pathways for securely taming networks and CPSs via AI‐centered, robust, real‐time, and CC‐reliant data storage to inhibit unauthorized access and innumerable CAs (e.g., ransomware, spyware, and phishing. This taxonomy bestows readers with an ample AI potential overview to improve CS in different contexts. The chapter inspects future opportunities in emerging CS applications, advanced AI tactics, data representation, and new infrastructures' development for successfully adopting AI‐based CS (AICS) in today's digital transformation era and syndemic circumstances.