# Prospects of Cybersecurity in Smart Cities
## 2 Literature Review
### Information Security
In the context of smart cities, this study also confirms that a lack of availability can have profound and far-reaching impacts on various aspects of urban life. Smart cities rely on a network of interconnected devices, sensors, and systems to deliver critical services and real-time data. Service disruptions can lead to inconvenience for citizens, economic losses for businesses, and even public safety concerns. Information security, data science, and technology are interconnected domains. Data science involves the extraction of knowledge and insights from data. It encompasses a wide range of techniques, including data collection, data cleaning, data analysis, data visualization, machine learning, and statistical modeling [38; 39]. Technology serves as the foundation for both information security and data science. Technological advancements have enabled the rapid growth of digital data and the development of sophisticated tools and algorithms to process and analyze it. Key technological components that support information security and data science include big data platforms, cloud computing, artificial intelligence, machine learning, cryptography, and networking and communication. [PERSON] and [PERSON] [40] provide several examples of the use of the cloud in organizations and pointed out that cloud services have revolutionized data storage, accessibility, and processing. They provide scalable infrastructure to accommodate large datasets and host data science applications and security solutions. Cloud service providers maintain vast data centers with a wide array of servers, storage, and networking resources. They can allocate and reallocate these resources dynamically based on demand. According to [PERSON] et al. [41], this allows users to scale up (add more resources) or scale down (remove excess resources) as needed, without having to invest in physical hardware themselves. Furthermore, cloud platforms offer elasticity, which means they can automatically adjust resources in response to varying workloads. During periods of high demand, the cloud can rapidly provision additional resources, and during low-demand periods, it can release unneeded resources. [PERSON] et al. [42] state that dynamic scaling ensures efficient resource utilization and cost-effectiveness. Cloud computing and machine learning are two powerful technologies that can be effectively combined to leverage their respective strengths. The integration of machine learning with cloud computing enables scalable and flexible solutions for processing and analyzing large datasets, building sophisticated models, and deploying AI-powered applications [43]. Machine learning tasks often require significant computational resources, especially for training complex models. [PERSON] et al. [44] provide a vision regarding the parallel computing programming mode in the context of cloud computing and argue that it has enabled distributed computing, which can significantly speed up the training process by distributing the workload across multiple servers or nodes. This parallel processing capability can be relevant when training complex models on large datasets.