Smart City Security Research Articles

A Vehicle Detection Model Based on 5G‐V2X for Smart City Security Perception

Security perception systems based on 5G‐V2X have become an indispensable part of smart city construction. However, the detection speed of traditional deep learning models is slow, and the low‐latency characteristics of 5G networks cannot be fully utilized. In order to improve the safety perception ability based on 5G‐V2X, increase the detection speed in vehicle perception. A vehicle perception model is proposed. First, an adaptive feature extraction method is adopted to enhance the expression of small‐scale features and improve the feature extraction ability of small‐scale targets. Then, by improving the feature fusion method, the shallow information is fused layer by layer to solve the problem of feature loss. Finally, the attention enhancement method is introduced to increase the center point prediction ability and solve the problem of target occlusion. The experimental results show that the UA‐DETRAC data set has a good detection effect. Compared with the vehicle detection capability before the improvement, the detection accuracy and speed have been greatly improved, which effectively improves the security perception capability based on the 5G‐V2X system, thereby promoting the construction of smart cities.

Cyber security in smart cities: A review of deep learning-based applications and case studies

On the one hand, smart cities have brought about various changes, aiming to revolutionize people's lives. On the other hand, while smart cities bring better life experiences and great convenience to people's lives, there are more hidden dangers of cyber security, including information leakage and malicious cyber attacks. The current cyber security development cannot keep up with the eager adoption of global smart city technologies so correct design based on deep learning methods is essential to protect smart city cyber. This paper summarizes the knowledge and interpretation of Smart Cities (SC), Cyber Security (CS), and Deep Learning (DL) concepts as well as discussed existing related work on IoT security in smart cities. Specifically, we briefly reviewed several deep learning models, including Boltzmann machines, restricted Boltzmann machines, deep belief networks, recurrent neural networks, convolutional neural networks, and generative adversarial networks. Then we introduced cyber security applications and use cases based on deep learning technology in smart cities. Finally, we describe the future development trend of smart city cyber security.

A YOLOv3 Based Smart City Application For Children’s Playgrounds

According to the reports of Public Health Institution, approximately 250,000 rabies-risk animal bites occur per year in Turkey. Most of these bites are caused by dogs and most of the victims are the children who play in playgrounds. With the development of deep learning-based computer vision technology, autonomous detection of dangerous objects (handguns, knives, dogs, etc.) in these children’s playgrounds has become a crucial security application. In this paper, a real-time dog detection model based on YOLOv3 deep learning algorithm is proposed as a new smart city security application and this model is applied to the selected children’s playground. Firstly, in view of the problem of insufficient stray dog image data in the original datasets, new images of stray dogs have been taken from an animal shelter and they have been added to the dataset. These new images have effectively enriched the diversity of training data and improved the training performance of the proposed model. The proposed model has been optimized by utilizing various hyperparameters and the results have been compared with each other. The model with the best evaluation scores is proposed and applied to detect dogs automatically by the fast emergency station located in the selected children’s playground. The real-time application has achieved 82.59% of AP with adjusted hyperparameters.

A provably secure and lightweight authentication scheme for Internet of Drones for smart city surveillance

Smart cities are made of different interconnected components that exchange data and facilitate the urban living for citizens. Unmanned Aerial Vehicles, known as drones, are components used in various civilian applications, such as agriculture, package delivery, and surveillance. To enhance drone’s flying safety and quality of service, a promising idea is to construct the Internet-of-Drones, where the drones are employed to collect the information, and citizens communicate with the drones of a particular flying zone via their mobile devices to obtain the data in real-time. However, due to the high sensitivity of the information, and the openness of communication media, privacy and security issues should be addressed. Specifically, an efficient and secure authentication scheme is needed to enable users and drones to authenticate each other and share a session key. Due to the drone’s limited resources and energy, the authentication scheme should be efficient regarding the computation overhead while providing high security. In this article, we devise a secure authentication scheme based on elliptic curve for drones to secure smart city surveillance. We demonstrate that our scheme is provably secure in the random oracle model, supporting the security requirements and resisting known attacks while incurring low computation and communication costs. • We devise an authentication scheme for IoD environments to provide a safe and smart-life for citizens in a smart city. • The proposed protocol is provably secure in the Random Oracle Model. • The security of the proposed protocol is validated formally through the Scyther tool. • The proposed protocol provides resistance against well-known potential security attacks. • The performance analysis showed that proposed protocol is more efficient than other competing protocols.

An architectural framework for information integration using machine learning approaches for smart city security profiling

In the past few decades, the whole world has been badly affected by terrorism and other law-and-order situations. The newspapers have been covering terrorism and other law-and-order issues with relevant details. However, to the best of our knowledge, there is no existing information system that is capable of accumulating and analyzing these events to help in devising strategies to avoid and minimize such incidents in future. This research aims to provide a generic architectural framework to semi-automatically accumulate law-and-order-related news through different news portals and classify them using machine learning approaches. The proposed architectural framework discusses all the important components that include data ingestion, preprocessor, reporting and visualization, and pattern recognition. The information extractor and news classifier have been implemented, whereby the classification sub-component employs widely used text classifiers for a news data set comprising almost 5000 news manually compiled for this purpose. The results reveal that both support vector machine and multinomial Naïve Bayes classifiers exhibit almost 90% accuracy. Finally, a generic method for calculating security profile of a city or a region has been developed, which is augmented by visualization and reporting components that maps this information onto maps using geographical information system.

Ensemble sparse representation-based cyber threat hunting for security of smart cities

The ever-growing expansion of smart cities and the Internet of Things (IoT) offer a promising solution to many contemporary urban challenges. However, this digital transformation also results in cyber-security loopholes which can be exploited by malicious hackers to wreak substantial digital and physical damage. Malware is the primary tool of cyber-criminals for attacking digital systems. In this paper, a multi-view ensemble threat hunting model based on Sparse Representation based Classifier (SRC) is proposed to use in IoT systems that are finding domain space in the advent of Smart Cities. An ensemble of SRCs is considered where every individual SRC classifies malware by Opcode, Bytecode and system call views of several standard IoT and Ransomware datasets. The final decision is made through weighted majority voting. SRC is employed to alleviate the complexity of the base classifiers. Experimental results verify the efficiency and robustness of the proposed model in different balanced and imbalanced environments. The proposed model outperforms all base classifiers and several well-known works in current literature.

Confused-Modulo-Projection-Based Somewhat Homomorphic Encryption—Cryptosystem, Library, and Applications on Secure Smart Cities

With the development of cloud computing, the storage and processing of massive visual media data has gradually transferred to the cloud server. For example, if the intelligent video monitoring system cannot process a large amount of data locally, the data will be uploaded to the cloud. Therefore, how to process data in the cloud without exposing the original data has become an important research topic. We propose a single-server version of somewhat homomorphic encryption cryptosystem based on confused modulo projection theorem named CMP-SWHE, which allows the server to complete blind data processing without <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">seeing</i> the effective information of user data. On the client side, the original data is encrypted by amplification, randomization, and setting confusing redundancy. Operating on the encrypted data on the server side is equivalent to operating on the original data. As an extension, we designed and implemented a blind computing scheme of accelerated version based on batch processing technology to improve efficiency. To make this algorithm easy to use, we also designed and implemented an efficient general blind computing library based on CMP-SWHE. We have applied this library to foreground extraction, optical flow tracking, and object detection with satisfactory results, which are helpful for building smart cities. We also discuss how to extend the algorithm to deep learning applications. Compared with other homomorphic encryption cryptosystems and libraries, the results show that our method has obvious advantages in computing efficiency. Although our algorithm has some tiny errors ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{-6}$ </tex-math></inline-formula> ) when the data is too large, it is very efficient and practical, especially suitable for blind image and video processing.

Smart City Crime Prevention Services: The Incheon Free Economic Zone Case

This study explores ways to improve the security systems of emerging smart cities by conducting a case study of the smart crime prevention service of the Incheon Free Economic Zone (IFEZ) in South Korea. Data from the IFEZ were collected between January 2017 and December 2018 across the smart system’s four functional areas (intelligent video surveillance, suspicious vehicle surveillance, emergency alerts, and abnormal sound sources) and 10 types of situations (emergency, violence, civil complaints, intrusion, kidnapping, loitering, throwing, suspicious vehicle, collision explosion, and sudden event). Descriptive statistics were analyzed to show the limitation of the smart crime prevention service. The results revealed three significant insights into the best practices for smart crime prevention services in smart cities: first, smart crime prevention services are required to verify the accuracy and consistency of collected data; second, the government must establish a consistent process to link all crime prevention services and to secure data linkages; and third, the government must urgently foster and secure experts in specialized institutions to carry out these advised functions. Ultimately, these findings suggest that in-depth discussions of data collection and sharing are required to ensure the optimal development of smart city security services.

Security in smart cities: A brief review of digital forensic schemes for biometric data

A smart city is engineered to be a self-sustained ecosystem driven by Internet-of-Things (IoT) devices. Smooth functioning of smart cities is conditioned on seamless communication between users and devices. Smart devices equipped with biometric authentication can offer security as well as personalized experience to the end users. Currently, a number of smart devices employ face, fingerprint, and voice modalities for user verification. However, the biometric data acquired by these devices can be digitally manipulated or tampered with, that can compromise the security of the smart environment. Further, the preponderance of biometric data such as face and voice in social media applications, necessitates the validation of their integrity. In this work, we review state-of-the-art digital forensic schemes for audio-visual biometric data that can be leveraged by applications designed for smart cities.

Securing Cyberspace of Future Smart Cities with 5G Technologies

Future smart cities promise to dramatically improve the quality of life and have been attracting the attention of many researchers in recent years. The integration of IoT with their corresponding service delivery models to manage a city's asset securely remains a significant challenge. The deployment of diverse IoT technologies and several architectural components and novel entities of emerging ICT solutions opens up new security threats and vulnerabilities. Large-scale, seamless communication among multiple IoT technologies is highly dependent on the operations of the underlying wireless access technologies such as WSNs, SDR, CR and RFID. We present thematic layered taxonomies to highlight the potential security vulnerabilities, attacks, and challenges of key IoT enabling technologies which underpin the development of smart cities. We also identify potential requirements and key enablers that play a vital role in the development of secure smart cities. Finally, we discuss various open issues that need to be addressed to unlock the full potential of 5G for future smart cities.

Kitchengam’ Criteria on the Use of Algorithms in a Person’s Pattern Detection, which Contribute to Safety, Surveillance and Energy Efficiency: Study of Art

Among the technological evolution is the application of algorithms in cameras for the detection and recognition of people, being a contribution to the security and surveillance in commercial, home areas, and smart cities. The objective of this research is to know and identify algorithms in the detection of patterns of a person, considering the criteria of Kitchengam. For this purpose, the following research questions were asked: Q1) How many studies refer to algorithms in pattern recognition? Q2: What types of algorithm models exist in an environment related to pattern recognition? and Q3: What types of pattern recognition algorithms currently exist? The search process was carried out in the digital libraries IEEE Xplore, ACM Digital Library, Springer Link and Science Direct (Elsevier). Obtained 1402 potentially eligible studies and obtained a final sample of 28 papers considered as main research studies. The results obtained allow us to consider the Support Vector Machines model with 92% recognition and the Viola-Jones algorithm with effective detection of 97,53%, are a contribution to the surveillance and safety of people within the recognition and detection of a person’s pattern, considering also as a challenge its feasibility focused on energy efficiency, in domestic, business and smart cities.

Developing Secure Smart Cities: Overviews and Challenges

Developing Secure Smart Cities: Overviews and Challenges

A Lightweight Cryptography Algorithm for Secure Smart Cities and IOT

A Lightweight Cryptography Algorithm for Secure Smart Cities and IOT

Online Detection of Action Start via Soft Computing for Smart City

Soft computing is facing a rapid evolution thanks to the development of artificial intelligence especially the deep learning. With video surveillance technologies of soft computing, such as image processing, computer vision, and pattern recognition combined with cloud computing, the construction of smart cities could be maintained and greatly enhanced. In this article, we focus on the online detection of action start task in video understanding and analysis, which is critical to the multimedia security in smart cities. We propose a novel model to tackle this problem and achieves state-of-the-art results on the benchmark THUMOS14 data set.

On the Security of a Smart City SDN-Based Network

Our research work focuses on the security techniques applied on a software-defined network (SDN), designed for a smart city. Information security is essential for the good functionality of the applications used in a smart city. Information should be correct and complete otherwise the ‘life’ of the city can be compromised by various cyberattacks targeting, for example, the public transportation system, the traffic information on highways or location services for emergency crews and many others. Securing all devices connected to the SDN, including end-user devices, wireless sensors, smart home equipment is essential because various cyberattacks may be launched and the smart city may be disturbed. This paper presents a risk analysis, the ways to choose the security solutions according to the smart city security policy and our proposal for the SDN security system. We compare different security techniques and present some recommended solutions.

A deep learning-based IoT-oriented infrastructure for secure smart City

In recent years, the Internet of Things (IoT) infrastructures are developing in various industrial applications in sustainable smart cities and societies such as smart manufacturing, smart industries. The Cyber-Physical System (CPS) is also part of IoT-oriented infrastructure. CPS has gained considerable success in industrial applications and critical infrastructure with a distributed environment. This system aims to integrate the physical world to computational facilities as cyberspace. However, there are many challenges, such as security and privacy, centralization, communication latency, scalability in such an environment. To mitigate these challenges, we propose a Deep Learning-based IoT-oriented infrastructure for a secure smart city where Blockchain provides a distributed environment at the communication phase of CPS, and Software-Defined Networking (SDN) establishes the protocols for data forwarding in the network. A deep learning-based cloud is utilized at the application layer of the proposed infrastructure to resolve communication latency and centralization, scalability. It enables cost-effective, high-performance computing resources for smart city applications such as the smart industry, smart transportation. Finally, we evaluated the performance of our proposed infrastructure. We compared it with existing methods using quantitative analysis and security and privacy analysis with different measures such as scalability and latency. The evaluation of our implementation results shows that performance is improved.

Security for smart cities

Smart cities are evolving globally and many governments have invested large sums of monies to develop smart cities. This development is not a result of an overnight decision but rather, smart cities have evolved through a period of time, directly from earlier work on the digital city to ubiquitous city, green city, connected city, sustainable city, eco-city etc. The present age sees the arrival of very high-speed wireless 5G connectivity, fast GPU multi-core-based servers, big data, cloud computing, artificial intelligence, and data analytics. Many of these new technologies have supported the development and realisation of smart cities. In this study, the authors present an outline of security for smart cities and provide a deeper understanding of what we meant by securing smart cities. They discuss the applicability of existing security methods of authentication, access control, encryption, firewalls, and their appropriateness to defending a smart city. Specifically, we cover the security of data, internet, water supply, electricity supply, city brain, and other critical city services and present the possible malicious attacks on a smart city and consequences. Finally, they discuss security best practices for smart cities.

Toward Integrated Virtual Emotion System with AI Applicability for Secure CPS-Enabled Smart Cities: AI-Based Research Challenges and Security Issues

Cyber-physical systems (CPS) basically pursue a new form of integrated interaction with humans through computation and physical capabilities covering complex, intelligent, autonomous systems. Also, artificial intelligence (AI) is considered as a promising technology that will be applicable to numerous combined next generation applications including CPS, security, and communication in smart cities. However, cyber security based on AI technologies is still in its infancy and, in particular, the differential challenges or issues should be addressed for various AI-enabled applications and systems. In this article, we introduce a new integrated virtual emotion system with AI applicability, called as I-VEmoSYS, toward secure CPS-enabled smart cities. The integrated virtual emotion system covers several subsystems such as virtual emotion barrier, virtual emotion map, and virtual emotion block. We describe their system settings, concepts, components, and operations, and also deal with AI applicability to those subsystems. Furthermore, we discuss future challenges and security issues that must be met to achieve secure advanced smart cities using the AI-enabled virtual emotion system.

Smart City Development in Taiwan: From the Perspective of the Information Security Policy

A smart city is developed through the Internet of Things (IoT), cloud computing, big data, mobile Internet, and other new generation technologies regarding information and communication, and data resources in various fields are integrated and applied. The issue of information security in the network era is the strategic focus, as well as the focus of people’s attention, during Taiwan’s smart city construction. Information security policies are the information security guidelines for organizations, and are key to the organization’s information security performance; moreover, such policies show the organization’s support and commitment to the information security of smart cities. This paper discusses the model of information security policy in Taiwan’s smart cities, uses Path Analysis to explore the characteristics of information security policy in smart cities, and examines the relationship between the formulation, implementation, maintenance, and effectiveness of information security policies. Furthermore, this study examines the impact on the effectiveness of organizational information security policies and information security performance from the following aspects: The length of information security policy publication time, policy review, policy advocacy, employee compliance, fair law enforcement, etc., which are all concrete manifestations of the formulation, implementation, and maintenance of information security policy models. Through a questionnaire survey, the correlation between various assumptions, as well as the relationship between organizational information security characteristics, information security policies, and the effectiveness of information security, are verified one by one during the implementation of information security policies. Finally, conclusions and implications are put forward.

Special issue on security, usability and sustainability of smart cities

Special issue on security, usability and sustainability of smart cities