Применение кибербезопасности в инновациях «умного города»: аспект искусственного интеллекта

DOI:

The early modern convergence of technological innovation and advancement resulted in the establishment of "smart cities." Advanced technology and meticulous design enable "smart cities" to improve financial stability, environmental sustainability, and quality of life. These advanced urban centers use state-of-the-art technology to provide individuals with efficient and tailored solutions. Smart cities tackle many essential issues of industrialization, making them significant. Smart cities enhance infrastructure, strategize transportation, ensure safety, and stimulate economic growth to improve livability, resilience, and accessibility [1]. They enhance urban living and alleviate congestion, conserving resources and infrastructure. Artificial Intelligence (AI), Machine Learning (ML), the Internet of Things (IoT), and sophisticated security algorithms define smart cities [2].

Smart cities collect data via IoT devices distributed across the urban environment. Neurons, AI, and ML identify patterns, derive insights, and facilitate automated decision-making within this extensive data repository. The IoT, AI, and ML have the potential to transform urban infrastructure into flexible networks [3]. The administration of smart cities and public engagement is at a pivotal juncture owing to the influence of IoT, AI, and ML. These technological advancements may improve urban living efficiency, infrastructure, and sustainability [4]. This article creates digital twins using IoT, AI, and ML to examine the opportunities and challenges of cybersecurity in smart cities [5]. Smart cities may use the IoT to integrate transportation systems, public services, and governmental entities, establishing a cohesive network of devices capable of communication and data sharing.

AI and ML facilitate intelligent decision-making, automate tasks, and enhance productivity. In addition to promoting sustainability, cooperative efforts may boost economic growth and improve individual wellbeing. Smart cities are vulnerable to cybersecurity risks due to their reliance on technology [6]. Risks include hacking, privacy breaches, and system failures. Malware compromises a multitude of IoT devices owing to inadequate security measures [7]. These gadgets transmit substantial data, potentially jeopardizing security and privacy. Bias and tipping may distort AI and ML forecasts. A solitary event may have extensive repercussions owing to the city's interconnected organizations [8].

This article examines the manner in which virtual smart cities mitigate cybersecurity issues devoid of context. Operational modeling and vulnerability assessments of Smart Cities (SC) are enhanced by digital replicas [9]. We can digitally replicate municipal infrastructure for problem identification, cyberattack simulation, and effect assessment. What incidents should we examine for cybersecurity concerns? Digital replicas might enhance internet security in smart towns. This study examines interconnected urban areas to tackle these challenges [10]. Smart cities are transforming our lives and workplaces via interconnected technologies, AI, and ML. Notwithstanding cybersecurity threats, certain technologies provide innovative solutions. Online safety may be enhanced with adaptive digital replication and prevention measures. Urban planners may identify issues, evaluate remedies, and enhance urban safety with computational techniques that simulate attacks. The expansion of smart cities presents novel cybersecurity threats and prospects [11].

Figure 1. Smart City.

This domain has progressed due to digital reproductions of occurrences, methodologies, and protocols. Digital replicas of urban areas may assist smart cities in evaluating laws, infrastructure maintenance, and other factors. Precise forecasting and planning enhance urban management and growth [12]. The intricacy and interconnectivity of smart communities present cybersecurity threats. Digital technology and the extensive data collected and processed jeopardize data privacy, security, and essential system safety [13]. Cybersecurity influences the financial aspects of the SC program and the trust of citizens. Robust cybersecurity legislation is necessary to safeguard our electronic infrastructure from rising dangers and ensure public safety and well-being. Digital twins, AI, ML, and the IoT provide a novel urban lifestyle. While these devices may enhance urban living, cybersecurity remains a concern. Establishing a smart city necessitates the prioritization of public safety and technology.

LITERATURE REVIEW

The IoT, AI, and ML are leading advancements in supply chain innovations that are swiftly transforming global communities. This innovative technology has the capacity to improve governance, resource use, and overall quality of life. Nonetheless, certain challenges arise due to the security vulnerabilities inherent in these interconnected structures. A detailed literature review is necessary to understand the applications of SC, cybersecurity issues, and viable solutions. This literature review combines and analyzes the latest research on the security of smart city virtual replicas to solve existing gaps. The objective of smart city initiatives, as indicated in the literature, is to use technology to enhance public engagement, sustainability, and urban efficacy [14]. IoT devices are essential to these initiatives since they provide the real-time capture and transmission of data across many cities [15]. Data analysis and method selection, together with the provision of creative solutions for savings, safety for everyone, and traffic management, are domains increasingly benefiting from the applications of AI and ML [16]. Numerous cybersecurity threats may emerge in municipal structures that use digital technologies [17].

The growth of IoT devices expands the potential attack surface for cyber threats, hence exposing urban regions to risks of security breaches, data theft, and privacy issues [18]. Emphasize the challenges associated with safeguarding AI and ML algorithms, since these technologies are vulnerable to adversaries who may manipulate or obliterate their decisionmaking mechanisms [19, 20]. Studies indicate that rigorous security protocols are essential for safeguarding individual privacy and maintaining the integrity of SC operations consistently [21]. Study about cybersecurity measures for SC has examined several techniques to reduce these concerns. Ensuring the security of data transfers across urban connections is a persistent concern, especially for IoT devices [22]. Furthermore, by providing a decentralized framework for safe data transfer among participants, blockchain technology has the capacity to enhance information transparency and overall integrity [23]. The use of AI and ML algorithms for prompt threat detection and mitigation underscores their potential as security tools as well as the challenges they may pose [24]. The incorporation of information technology for safety has been unexpectedly under-researched, considering the substantial literature on protective strategies in intelligent communities. Urban assaults may be analyzed and modeled using divergences, which serve as computational models of real systems [25, 26]. This feature enables municipal authorities to evaluate the efficacy of preventive measures, anticipate future weaknesses, and rehearse for crises in a secure online setting. The field is primed for more investigation, since there is less literature on the actual use of digital techniques to enhance smart city safety [27, 28]. Ultimately, the preceding study underscores the essential need for cybersecurity in smart city implementations [30].

METHODOLOGY

The incorporation of the IoT, AI, and ML into urban settings establishes the foundation for smart city advancement, hence enhancing productivity, sustainability, as well as value of life. Technical study elucidates the core ideas behind these developments, including the foundational architecture of SC, the increasing significance modeling of digital twin, the critical cybersecurity requirements applicable in such intricate environments.

Core Concepts of IoT, AI, and ML

The IoT is a network of linked physical entities ("things") capable of communicating and exchanging data with one another and various equipment and platforms across an online network. The IoT offers several advantages to municipal administrations, ranging from high-capacity refuse vehicles to surveillance cameras for wastewater management, facilitating a more responsive and integrated law enforcement system. Robots may acquire the ability to recognize and analyze human features, sounds, and behaviors using various AI technologies.

Smart city algorithms provide several opportunities and cost reductions, including autonomous evacuation alerts, proactive traffic management, and flow analysis [31, 33]. ML is a subset of AI that utilizes data to teach and build machines without preprogrammed knowledge. ML has the potential to enhance supply chain services and municipal administration by identifying trends such as traffic congestion, alterations in use, and fluctuations in weather conditions. Supply chain planning integrates several developments into a cohesive framework [34, 35]. Essentially, internet-enabled devices relay data from a city to distant server farms or cloud platforms. ML and AI offer practical insights that can improve urban services and infrastructure. Prioritizing efficiency in municipal networks, this design facilitates expansion and the integration of emerging technologies and applications [36, 37].

Figure 2. Cybersecurity Risk Algorithm of Smart City.

Employing computational intelligence, ML, and AI facilitates the processing, transformation, and analysis of data, which results in the extraction of useful insights. AI and ML analyze data gathered by the IoT’s computational core.

Figure 3. Core Concepts of IoT, AI, and ML.

Cybersecurity Principles for Smart City

The fundamental concepts that underpin SC cybersecurity are communication openness, confidentiality, and accessibility. The interconnectivity of supply chain facilities means that an issue in one component might jeopardize the whole system [38]. The security of data and the integrity of essential application processes rely on dependable signature systems, secure transmission protocols, and regular safety evaluations. Furthermore, contemporary security metrics based on AI and ML, in conjunction with the idea of equal treatment, may augment SC protection [39].

Figure 4. Cybersecurity Principles for Smart City.

Data Collection and Sources

Analytical information concerning incidents involving cybersecurity in modern cities is going to be derived from research papers, industrial papers, and security issues repositories. It will involve specifics concerning the regularity of attacks via the internet, the categories from risks affecting solutions, and the material and operational consequences of these incidents. Analysis of qualitative information will be collected via scholarly research, expert discussions, as well as case analyses. The findings may improve the understanding of cybersecurity strategies in intelligent cities, the acceptance patterns of internet-based opinions, and the efficacy of these initiatives. Guidelines for choosing information will consider relevance to study issues, source trustworthiness, and data reliability. To truly represent today's state of cybersecurity, the quantitative data will focus on studies and papers published over the last five years. The selection of qualitative data from case studies and expert interviews will depend on the depth of data available regarding solving of cybersecurity issues via IoT, AI, ML, and virtual reality.

Data Analysis Techniques

Numerical Analysis: We will use statistical tools like MATLAB and Python to conduct inductive and descriptive analyses of the collected data. This project will calculate means, standard deviations, and frequencies to enhance comprehension of data from cybersecurity incidents. Inverse statistics, such as regression analysis, may analyze the correlation between digital technology use and cybersecurity concerns [40]. We will use thematic analysis to identify similarities and patterns in cybersecurity activities and digital analytic applications. Python software may assist data coding and theme classification, allowing for a more comprehensive analysis of qualitative information [41, 42]. Collaborating on Statistical Analysis, Ultimately, the two datasets are merged to suggest a comprehensive overview of cybersecurity in SC and the prospective contributions of technological pairs to alleviating these threats. This integrated study will be essential for establishing a robust platform for future research and practice in supply chain cybersecurity [43, 44]. This research uses a thorough review of existing studies, data analysis, and theme analysis to explain how supply chain technologies, cybersecurity issues, and the role of the IoT work together to enhance safety in cities. Ultimately, the integration of IoT, AI, and ML into SC, along with the use of virtual replicas to imitate privacy, offers a holistic strategy to improve urban living while safeguarding the privacy and resilience of fragile ecosystems. This study uses different research methods to look at how smart cities use the IoT, AI, and ML, focusing on security problems and how digital copies can help reduce these risks. This methodology encompasses both quantitative and qualitative research; the former aims to comprehend the intricacies of smart city initiatives and cybersecurity efforts, while the latter uses analytical techniques to assess the efficacy of these strategies.

Analysis: Cybersecurity Challenges in Smart Cities

Urban management and services are greatly enhanced when smart cities use current technology like the IoT, AI, and ML [45]. Several cybersecurity issues, however, accompany this digital revolution. In this part, we will examine the main cybersecurity issues that smart cities face, provide data on cybercrime and their effects, and delve into the vulnerabilities that IoT devices and networks in town naturally have [46]. The Smart City Challenge (Figure 5) The security concerns surrounding smart cities are substantial, calling for comprehensive and cautious techniques for safeguarding technology and guarantee the accessibility of city services. IoT threats highlight the critical need for SC users and administrators to be aware of cybersecurity standards, establish robust security safeguards, and update codes. Resolving these vulnerabilities is essential to safeguard the scientific foundation of contemporary cities from the ever-evolving landscape of cyberattacks [47]. Smart cities, by integrating the IoT, AI, and ML, are revolutionizing the development of better, more sustainable, and more hospitable cityscapes. Data on adoption rates, benefits gained, and challenges encountered are all part of the case studies examined in this area, demonstrating the actual use of different technologies in smart city (SC) efforts. Digital twins have had a revolutionary impact on city management, and this article illustrates their effective applications.

Figure 5. Smart City Challenge.

Major Security Risks to Smart Cities

Data Theft: Smart cities are vulnerable to data breaches because of the substantial volumes of data they gather and manage. These instances may lead to the revelation of private information, thereby eroding confidence and jeopardizing privacy [48]. Ransomware attacks occur when cybercriminals encrypt crucial government data and services and demand payment for their decryption. Such attacks may hinder municipal services, including emergency response and traffic management. Attacks like Distributed Denial of Service (DDoS) and Denial of Service (DoS) may incapacitate SC networks completely [49]. A comprehensive analysis of municipal services, including utilities, transportation, and communication networks, would follow. Instrument Theft in the IoT, Cybercriminals may exploit weaknesses in Internet of Things equipment to get hacked. Such theft not only jeopardizes the equipment but may also precipitate more assaults on neighboring communities [50].

Intelligent Community Computer Incidents and Implications

Cyberattacks against SC have rised by 450% in last five years, according to the Global Security Council. After including direct monetary losses, poor reputation, and restoration costs, the typical cyber event for an SC is estimated to cost $3.4 million. Notable examples include the 2018 ransomware attack on Atlanta, which caused service delays of several days and exceeded $5 million in immediate initiatives to rectify along with safeguard the municipality's IT assets [51].

Fraud

Figure 6. ML and DL analysis Architecture.

Smart city IoT access and networks

Often, the design of IoT devices prioritizes functionality and efficiency over safety features. There are a number of reasons why this layout design leaves companies open to cyberattacks:Default passwords and usernames are pre-installed on many IoT devices, making them easy targets for hackers if not changed after deployment.

• •    Inadequate Security: Detectors may access private information due to the absence of appropriate data encoding in IoT devices and their connections.

• •    Application Patches: The IoT is vulnerable to hacking because security gaps are left unpatched and unupgraded.

• •    Interconnection: Hackers may be able to get access to and manipulate other connected devices by stealing only one appliance in a smart city's ecosystem.

Evaluations of Smart City Projects

The "Smart Nation" initiative in Singapore integrates IoT, AI, and ML to improve public services, healthcare, and transportation. By optimizing traffic flow and alleviating congestion, AI-driven mobility solutions in the city-state will decrease travel durations. Enhanced patient care is facilitated by predictive analytics and remote monitoring enabled by Singapore's smart health technology [46]. Barcelona has established an extensive IoT sensor network to enhance public services and resource management. Smart city lighting, with occupancy sensors, has the potential to reduce energy use by 30%. An IoT-enabled water management system has achieved a 25% decrease in water use, illustrating the economic and environmental advantages of smart technology. Copenhagen leads in sustainable energy management and waste collection via the use of the IoT and AI for sustainable urban alternatives for smart communities. The town's smart garbage control receptacles now include real-time fill levels, enhancing collection routes and frequency, lowering operating costs, and diminishing carbon emissions. Investment Expenditures: Approximately 65% of cities have implemented IoT technology, whilst 35% are exploring AI and ML for urban management, as reported by the Smart Cities Coalition [52]. Energy consumption has reduced by 20%, transportation congestion by 15%, and emergency response times by 25% due to technology developments. Notwithstanding these advantages, urban areas express concerns over security (55%) and implementation expenses (40%). Integrating modern urban planning with technology systems presents intrinsic technical and economic challenges.

Figure 7. Data training and Apply from ELM Model.

Figure 8. ELM Model apply to smart city analysis.

RESULTS AND DISCUSSION

Comprehensive Strategic Solutions

Privacy is crucial for intelligent townships using IoT, AI, and ML. Cities, towns, and regions may benefit from smart city strategies and concepts via the Smart City EcosystemTM and Smart City MandalaTM. A Smart City Mandala™ consists of five components: population, economy, Governance, Environment, and Quality of Life. We formulate

"smart city" plans and use digital technologies for the development of your town, city, or neighborhood. Our partners include Integrated Transport Planning, Urban Tide, SMEC, Cogility, Cred Consulting, Hitachi, and Newcastle City. Numerous factors contribute to the security challenges faced by local governments. A city's needs and resources should guide solutions for managing cybersecurity risks. Given the city's limited control over its infrastructure, mitigating risk will require public-private partnerships.

Table 1. Comprehensive Strategic Analysis by Place and Mode.

	Smart Mobility 1	Smart Environment	Smart Government	Smart Economy	Smart People	Smart Living	SmartCity Index
Smart Mobility		-0.19	-0.04	-0.26	0.35	0.14	0.34
Smart Environment	-0.19	1	0.28	0.31	-0.05	0.04	0.4
Smart Government	-0.04	0.28	1	0.17	0.26	0.44	0.6
Smart Economy	-0.26	0.31	0.17	1	-0.12	0.05	0.32
Smart People	0.35	-0.05	0.26	-0.12	1	0.33	0.56
Smart Living	0.14	0.04	0.44	0.05	0.33	1	0.72
SmartCity Index	0.34	0.4	0.6	0.32	0.56	0.72	1

Table 2. Comprehensive Statistic Analysis.

	Smart Mobility	Smart Environment	Smart Government	Smart Economy	Smart People	Smart Living	SmartCity Index
count	102	102	102	102	102	102	102
mean	5759.40	5943.5	5893.80	6131.80	5874.05	6377.04	5991.91
std	1214.03	1724.03	1153.37	1801.56	1449.10	2286.341	852.78
min	3175	1850	2806	1490	2825	1980	4191
25%	4809.75	4530.5	5143	5007.5	4724.75	4385	5366.25
50%	5651.5	6495	5911	6432.5	5747.5	6485	6261.5
75%	6763.75	7310	6581.5	7492.5	7061.25	8710	6672.5
max	8110	8844	8726	9225	9695	10000	7353

Table 3. Comprehensive Strategic Analysis by Place and Mode (adding untrained data).

Country	Smart Mobility	Smart Environment	Smart Government	Smart Economy	Smart People	Smart Living	SmartCity Index
Netherlands	7540	5558	8528	8095	7098	7280	7311
Norway	6486	6989	7018	4925	7822	9090	7088
Canada	6727	4780	6510	6782	6930	9920	6866
Singapore	5790	4344	5560	5535	9695	10000	6813
Denmark	5876	8207	7540	5182	6386	7200	6803
Austria	5683	7608	6232	5415	8580	7500	6771
Sweden	4683	8296	7840	5980	6743	7730	6771
Switzerland	5326	8775	5591	6265	6425	7960	6707
Finland	5124	6519	6121	8155	5944	8710	6689
US	7607	4800	5356	7265	6610	6220	6437

Departments' IT resources may create potential problems if they fail to collaborate. To avert manipulation, theft, vandalism, and environmental risks, it is essential to safeguard sensors and monitors with both physical and logical safeguards. Enhancing the security of vulnerable devices, the internet, and software and operating system updates is imperative. The proliferation of IoT devices is increasing the incidence of assaults on municipal networks. When designing and executing real-time traffic management systems, managers must prioritize security owing to the heightened risk of threats. Only 15 of the 49 Smart City concepts in the 2021 Safe Communities Index dealt with network and data security.

Figure 9. Smart city overview and Risk.

Figure 10. Population and Threads.

Figure 11. Risk analysis.

Table 4. Challenging possibility in future in 10 years.

Country	Smart Mobility	Smart Environment	Smart Government	Smart Economy	Smart People	Smart Living	SmartCity Index
Netherlands	7533.86	7581.25	7628.64	7676.04	7723.43	7770.82	7818.21
Norway	8032.86	8276.14	8519.43	8762.71	9006	9249.29	9492.57
Canada	8518.86	8915.89	9312.93	9709.96	10107	10504.04	10901.07
Singapore	9464.71	10126	10787.29	11448.57	12109.86	12771.14	13432.43
Denmark	6686.71	6672.89	6659.07	6645.25	6631.43	6617.61	6603.79
Austria	7597.86	7790.57	7983.29	8176	8368.71	8561.43	8754.14
Sweden	7439.71	7583.82	7727.93	7872.04	8016.14	8160.25	8304.36
Switzerland	7199.43	7318.97	7438.5	7558.04	7677.57	7797.11	7916.64
Finland	8023.143	8341	8658.86	8976.71	9294.57	9612.43	9930.29
US	6411.29	6432.14	6453	6473.86	6494.71	6515.57	6536.43

Figure 12. Simulation of Index.

Figure 13. Simulation of Index, Threads and Possibility.

Figure 14. Simulation of Traverser comparison of sites.

Figure 15. Simulation of Traverser comparison of sites (weekly).

CONCLUSION

IoT, AI, and ML in smart cities may boost productivity, sustainability, and quality of life. Mixing expands the cyber-attack perimeter, emphasizing the necessity for good protection. The study found data breaches, ransomware, DoS, and IoT device hijacking. Authorities and hackers may duplicate, examine, and improve smart city operations, including security, using virtual city infrastructures and systems. The article emphasizes reinventing cybersecurity methods including forecasting, real-time monitoring, and automated action systems. Digital replications of the smart city ecosystem address cybersecurity. Technology strengthens smart societies against cyberattacks and creates safe, viable, and livable communities.

IoT, AI, and ML in intelligent towns are mysterious, bringing creative cities tremendous prospects but major safety risks. A robust and trustworthy environment and smart cities may benefit from planned virtual copy distribution. Finally, ethically integrating IoT, AI, and ML into intelligent cities and employing virtual counterparts to model privacy enhances urban living while protecting fragile ecosystems. This research examines smart city IoT, AI, and ML usage and how digital copies improve safety using many methods. The effectiveness of intelligent city designs and cybersecurity activities is assessed using qualitative and quantitative methods.