In an era defined by digital interconnectivity, securing information in the cloud is paramount. By harnessing the power of present advanced technologies, organizations can fortify their defenses against evolving cyber threats while simultaneously embracing environmentally conscious practices. The model begins by integrating machine learning (ML) algorithms into fabric of cyber security. Anomaly detection, threat prediction, and adaptive response mechanisms enable a proactive defense, continually evolving to thwart emerging threats. Beyond the realm of cyber security efficacy, ML optimizes resource utilization, contributing to the sustainability of cloud operations. Complementing this adaptive intelligence, cloud cryptography emerges as a cornerstone for securing data at rest and in transit. From traditional encryption to quantum-resistant cryptographic techniques, the model ensures confidentiality and integrity of information. Sustainable cryptographic practices, coupled with efficient key management, further mitigate the environmental impact associated with cryptographic operations.
Top1.0 Introduction
In an era defined by the rapid digitization of data and the exponential growth of information technology, cloud computing has emerged as a fundamental paradigm shift in how organizations store, access, and manage their data and services. The convenience, scalability, and cost-efficiency cloud computing offers have made it an indispensable tool for businesses and individuals. However, as the adoption of cloud services continues to surge, so do the challenges associated with securing sensitive data and ensuring the privacy and integrity of digital assets.
The concept of ML in cloud computing began to gain traction in the mid to late 2000s. Cloud providers started offering basic services for data storage and processing. As cloud computing services expanded, ML tools and frameworks, such as Tensor Flow and PyTorch, became available on cloud platforms. Major cloud providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) started offering specialized ML services. The emergence of AI as a Service (AIaaS) allowed users to access pre-built ML models and services without needing extensive expertise in the field. This lowered the barrier for businesses to adopt ML (Géron, 2022). Cloud providers integrated ML capabilities into their existing services, making it easier for developers to incorporate ML into their applications. Improved hardware accelerators, such as Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs), provided significant speed-ups for training and inference tasks in ML, and cloud providers incorporated these technologies into their offerings (Campesato, 2023).
The intersection of cloud computing and cyber security presents a dynamic landscape where evolving threats and vulnerabilities demand innovative solutions (Dupont, 2013). This research explores the critical role of ML and cloud cryptography in bolstering the security of cloud-based systems. As cyber threats become increasingly sophisticated and prevalent, harnessing the power of these advanced technologies is essential to safeguarding data in the cloud and fortifying the integrity of digital infrastructure.
The changing world of connectivity has been greatly transformed by the widespread use of cloud computing (Kolb, 2018). This technology has completely revolutionized how organizations handle and analyze their data. While the benefits of cloud technology are undeniable, the escalating frequency and sophistication of cyber threats pose significant challenges to the security of sensitive information stored and processed in the cloud.
However, the widespread adoption of cloud computing faces obstacles in the form of security concerns. Indeed, ensuring the security and protection of distributed computing services from unauthorized access or misuse poses a significant challenge, which can be mitigated through shared responsibility (Dey, 2019). Infrastructure as a Service (IaaS) manages hardware resources, offering users low- and high-level flexibility. Platform as a Service (PaaS) provides a robust platform for enhanced service delivery, while Software as a Service (SaaS) offers diverse software solutions for efficient data access and utilization.