scholarly journals CAKE: Compatible Authentication and Key Exchange Protocol for a Smart City in 5G Networks

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 698
Author(s):  
Yun-Hsin Chuang ◽  
Yuh-Min Tseng
Keyword(s):  
Smart City ◽  
Service Providers ◽  
Smart Cities ◽  
Key Exchange ◽  
Third Party ◽  
Security Model ◽  
Session Key ◽  
Diffie Hellman ◽  
Iot Devices ◽  
And Storage

In a smart city, there are different types of entities, such as nature persons, IoT devices, and service providers, which have different computational limitations and storage limitations. Unfortunately, all of the existing authentication and key exchange (AKE) protocols are designed for either client–server or client–client authentication, including the ones designed for smart cities. In this paper, we present the idea of a compatible authentication and key exchange (CAKE) protocol which provides cross-species authentication. We propose the first CAKE protocol for a smart city that any two valid entities can authenticate with each other and create a secure session key without the help of any third party, while there is also no password table and no public key issuing problem. The entity can be a natural person having biometrics, an IoT device embedded with a physical unclonable function (PUF), or a service provider. Moreover, we extend the CAKE protocol to an anonymous CAKE (ACAKE) protocol, which provides natural persons an anonymous option to protect their privacy. In addition, both the proposed CAKE and ACAKE protocols can deal with the entity revocation problem. We define the framework and the security model of CAKE and ACAKE protocols. Under the security model, we formally prove that the proposed protocols are secure under the elliptic curve computational Diffie–Hellman (ECCDH) problem, the decisional bilinear Diffie–Hellman (DBDH) problem, and hash function assumptions. Comparisons with the related protocols are conducted to demonstrate the benefits of our protocols. Performance analysis is conducted and the experience results show that the proposed protocols are practical in a smart city.

scholarly journals Ciphertext-policy attribute-based encryption with hidden sensitive policy from keyword search techniques in smart city

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Fei Meng ◽  
Leixiao Cheng ◽  
Mingqiang Wang
Keyword(s):  
Smart City ◽  
Keyword Search ◽  
Sensitive Information ◽  
Security Model ◽  
Computational Overhead ◽  
Diffie Hellman ◽  
Attribute Based Encryption ◽  
Iot Devices ◽  
Ciphertext Policy ◽  
Access Policies

AbstractCountless data generated in Smart city may contain private and sensitive information and should be protected from unauthorized users. The data can be encrypted by Attribute-based encryption (CP-ABE), which allows encrypter to specify access policies in the ciphertext. But, traditional CP-ABE schemes are limited because of two shortages: the access policy is public i.e., privacy exposed; the decryption time is linear with the complexity of policy, i.e., huge computational overheads. In this work, we introduce a novel method to protect the privacy of CP-ABE scheme by keyword search (KS) techniques. In detail, we define a new security model called chosen sensitive policy security: two access policies embedded in the ciphertext, one is public and the other is sensitive and hidden. If user's attributes don't satisfy the public policy, he/she cannot get any information (attribute name and its values) of the hidden one. Previous CP-ABE schemes with hidden policy only work on the “AND-gate” access structure or their ciphertext size or decryption time maybe super-polynomial. Our scheme is more expressive and compact. Since, IoT devices spread all over the smart city, so the computational overhead of encryption and decryption can be shifted to third parties. Therefore, our scheme is more applicable to resource-constrained users. We prove our scheme to be selective secure under the decisional bilinear Diffie-Hellman (DBDH) assumption.

scholarly journals Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques

2020 ◽  
Vol 17 (24) ◽  
pp. 9347 ◽  
Author(s):  
Md Mamunur Rashid ◽  
Joarder Kamruzzaman ◽  
Mohammad Mehedi Hassan ◽  
Tasadduq Imam ◽  
Steven Gordon
Keyword(s):  
Machine Learning ◽  
Smart City ◽  
Service Providers ◽  
Detection System ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Increased Risk ◽  
Iot Devices

In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.

scholarly journals Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

2020 ◽  
Vol 12 (14) ◽  
pp. 5595 ◽  
Author(s):  
Ana Lavalle ◽  
Miguel A. Teruel ◽  
Alejandro Maté ◽  
Juan Trujillo
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Cities ◽  
Fire Department ◽  
Heterogeneous Data ◽  
Fire Extinguishing ◽  
Heterogeneous Data Sources ◽  
Iot Devices ◽  
Visualization Techniques ◽  
The Impact

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.

scholarly journals Sustainable Virtual Reality Patient Rehabilitation Systems with IoT Sensors Using Virtual Smart Cities

2021 ◽  
Vol 13 (9) ◽  
pp. 4716
Author(s):  
Moustafa M. Nasralla
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Virtual Reality ◽  
Time Series Analysis ◽  
Smart City ◽  
Smart Cities ◽  
Rehabilitation Services ◽  
Simulation Scenario ◽  
Common Problems ◽  
Iot Devices

To develop sustainable rehabilitation systems, these should consider common problems on IoT devices such as low battery, connection issues and hardware damages. These should be able to rapidly detect any kind of problem incorporating the capacity of warning users about failures without interrupting rehabilitation services. A novel methodology is presented to guide the design and development of sustainable rehabilitation systems focusing on communication and networking among IoT devices in rehabilitation systems with virtual smart cities by using time series analysis for identifying malfunctioning IoT devices. This work is illustrated in a realistic rehabilitation simulation scenario in a virtual smart city using machine learning on time series for identifying and anticipating failures for supporting sustainability.

ILSSPP: Intelligent location sharing system with privacy preserving features for smart cities

2021 ◽  
pp. 1-12
Author(s):  
Gokay Saldamli ◽  
Richard Chow ◽  
Hongxia Jin
Keyword(s):  
Service Providers ◽  
Smart Cities ◽  
User Interaction ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Location Information ◽  
Fine Grained ◽  
Location Sharing ◽  
Social Network Structure

Social networking services are increasingly accessed through mobile devices. This trend has prompted services such as Facebook and Google+to incorporate location as a de facto feature of user interaction. At the same time, services based on location such as Foursquare and Shopkick are also growing as smartphone market penetration increases. In fact, this growth is happening despite concerns (growing at a similar pace) about security and third-party use of private location information (e.g., for advertising). Nevertheless, service providers have been unwilling to build truly private systems in which they do not have access to location information. In this paper, we describe an architecture and a trial implementation of a privacy-preserving location sharing system called ILSSPP. The system protects location information from the service provider and yet enables fine grained location-sharing. One main feature of the system is to protect an individual’s social network structure. The pattern of location sharing preferences towards contacts can reveal this structure without any knowledge of the locations themselves. ILSSPP protects locations sharing preferences through protocol unification and masking. ILSSPP has been implemented as a standalone solution, but the technology can also be integrated into location-based services to enhance privacy.

scholarly journals A Parallel Meta-Heuristic Approach to Reduce Vehicle Travel Time in Smart Cities

2020 ◽  
Author(s):  
Hector Rico-Garcia ◽  
Jose-Luis Sanchez-Romero ◽  
Antonio Jimeno-Morenilla ◽  
Hector Migallon-Gomis
Keyword(s):  
Travel Time ◽  
Smart City ◽  
Smart Cities ◽  
Travelling Salesman Problem ◽  
Urban Environments ◽  
Departure Point ◽  
Classic Problem ◽  
Iot Devices ◽  
Vehicle Travel Time ◽  
Gpu Architecture

The development of the smart city concept and the inhabitants’ need to reduce travel time, as well as society’s awareness of the reduction of fuel consumption and respect for the environment, lead to a new approach to the classic problem of the Travelling Salesman Problem (TSP) applied to urban environments. This problem can be formulated as “Given a list of geographic points and the distances between each pair of points, what is the shortest possible route that visits each point and returns to the departure point?” Nowadays, with the development of IoT devices and the high sensoring capabilities, a large amount of data and measurements are available, allowing researchers to model accurately the routes to choose. In this work, the purpose is to give solution to the TSP in smart city environments using a modified version of the metaheuristic optimization algorithm TLBO (Teacher Learner Based Optimization). In addition, to improve performance, the solution is implemented using a parallel GPU architecture, specifically a CUDA implementation.

Security Aspects in Cloud Computing

2018 ◽  
pp. 54-76
Author(s):  
Tabassum N. Mujawar ◽  
Ashok V. Sutagundar ◽  
Lata L. Ragha
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Cloud Service ◽  
Third Party ◽  
Storage Security ◽  
Security Issues ◽  
Access Control Mechanism ◽  
And Storage ◽  
Privacy Issues ◽  
Different Levels

Cloud computing is recently emerging technology, which provides a way to access computing resources over Internet on demand and pay per use basis. Cloud computing is a paradigm that enable access to shared pool of resources efficiently, which are managed by third party cloud service providers. Despite of various advantages of cloud computing security is the biggest threat. This chapter describes various security concerns in cloud computing. The clouds are subject to traditional data confidentiality, integrity, availability and various privacy issues. This chapter comprises various security issues at different levels in environment that includes infrastructure level security, data level and storage security. It also deals with the concept of Identity and Access Control mechanism.

The Role of AI-Based Integrated Physical Security Governance for Optimizing IoT Devices Connectivity in Smart Cities

2021 ◽  
pp. 289-309
Author(s):  
Rajan R. ◽  
Venkata Subramanian Dayanandan ◽  
Shankar P. ◽  
Ranganath Tngk
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Security Threats ◽  
Security Measures ◽  
Physical Security ◽  
Security Technology ◽  
Security Controls ◽  
World Class ◽  
Iot Devices

A smart city aims at developing an ecosystem wherein the citizens will have instant access to amenities required for a healthy and safe living. Since the mission of smart city is to develop and integrate many facilities, it is envisaged that there is a need for making the information available instantly for right use of such infrastructure. So, there exists a need to design and implement a world-class physical security measures which acts as a bellwether to protect people life from physical security threats. It is a myth that if placing adequate number of cameras alone would enhance physical security controls in smart cities. There is a need for designing and building comprehensive physical security controls, based on the principles of “layered defense-in-depth,” which integrates all aspects of physical security controls. This chapter will review presence of existing physical security technology controls for smart cities in line with the known security threats and propose the need for an AI-enabled physical security premise.

scholarly journals A Hyperconnected Smart City Framework

2020 ◽  
Vol 24 ◽  
Author(s):  
Naureen Naqvi ◽  
Sabih Ur Rehman ◽  
Zahidul Islam
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Security And Privacy ◽  
Directed Network ◽  
Web Based ◽  
Sustainable Environment ◽  
Processing And Storage ◽  
The Impact ◽  
And Storage

Recent technological advancements have given rise to the concept of hyper-connected smart cities being adopted around the world. These cities aspire to achieve better outcomes for citizens by improving the quality of service delivery, information sharing, and creating a sustainable environment. A smart city comprises of a network of interconnected devices also known as IoT (Internet of Things), which captures data and transmits it to a platform for analysis. This data covers a variety of information produced in large volumes also known as Big Data. From data capture to processing and storage, there are several stages where a breach in security and privacy could result in catastrophic impacts. Presently there is a gap in the centralization of knowledge to implement smart city services with a secure architecture. To bridge this gap, we present a framework that highlights challenges within the smart city applications and synthesizes the techniques feasible to solve them. Additionally, we analyze the impact of a potential breach on smart city applications and state-of-the-art architectures available. Furthermore, we identify the stakeholders who may have an interest in learning about the relationships between the significant aspects of a smart city. We demonstrate these relationships through force-directed network diagrams. They will help raise the awareness amongst the stakeholders for planning the development of a smart city. To complement our framework, we designed web-based interactive resources that are available from http://ausdigitech.com/smartcity/.

scholarly journals Study and Research on IoT and Big Data Analysis for Smart City Development

2021 ◽  
Vol 22 (2) ◽  
Author(s):  
Haixia Yu ◽  
Ion Cosmin Mihai ◽  
Anand Srivastava
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Cities ◽  
Data Generation ◽  
City Development ◽  
Smart Meters ◽  
Smart Systems ◽  
Smart Parking ◽  
Iot Devices ◽  
Time Required

With the development of smart meters, like Internet of Things (IoT), various kinds of electronic devices are equipped with each smart city. The several aspects of smart cities are accessible and these technologies enable us to be smarter. The utilization of the smart systems is very quick and valuable source to fulfill the requirement of city development. There are interconnection between various IoT devices and huge amount of data is generated when they communicate each other over the internet. It is very challenging task to effectively integrate the IoT services and processing big data. Therefore, a system for smart city development is proposed in this paper which is based on the IoT utilizing the analytics of big data. A complete system is proposed which includes various types of IoT-based smart systems like smart home, vehicular networking, and smart parking etc., for data generation. The Hadoop ecosystem is utilized for the implementation of the proposed system. The evaluation of the system is done in terms of throughput and processing time. The proposed technique is 20% to 65% better than the existing techniques in terms of time required for processing. In terms of obtained throughput, the proposed technique outperforms the existing technique by 20% to 60%.

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