scholarly journals Group signature with time-bound keys and unforgeability of expiry time for smart cities

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Junli Fang ◽  
Tao Feng
Keyword(s):  
Smart Cities ◽  
Computational Cost ◽  
Life Cycle Management ◽  
Group Signature ◽  
Resource Constrained ◽  
Security Risks ◽  
Weakest Link ◽  
Identity Privacy ◽  
Iot Devices ◽  
Dynamic Time

AbstractInternet of Things (IoT) lays the foundation for the various applications in smart cities, yet resource-constrained IoT devices are prone to suffer from devastating cyberattacks and privacy leak threats, thus are inevitability supposed as the weakest link of the systems in smart cities. Mitigating the security risks of data and the computing limitation of edge devices, especially identity authentication and key validity management of group devices are essential for IoT system security. In order to tackle the issues of anonymity, traceability, unforgeability of expiry time as well as efficient membership revocation for life-cycle management of devices in IoT setting, we presented a dynamic time-bound group signature with unforgeability of expiry time. Unforgeability of expiry time disables a revoked signer to create a valid signature by means of associating the signing key with an expiry time. The anonymity and traceability of the proposed scheme contribute to the identity privacy of the entities and supervision for authority agency. Moreover, our proposal is feasible in the resource-constrained setting for efficient computational cost of signing and verification algorithms.

scholarly journals State-of-the-Art Software-Based Remote Attestation: Opportunities and Open Issues for Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1598
Author(s):  
Sigurd Frej Joel Jørgensen Ankergård ◽  
Edlira Dushku ◽  
Nicola Dragoni
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Cyber Attacks ◽  
Resource Constrained ◽  
Remote Attestation ◽  
Comprehensive Overview ◽  
Research Issues ◽  
Advantages And Disadvantages ◽  
Iot Devices ◽  
Specialized Hardware

The Internet of Things (IoT) ecosystem comprises billions of heterogeneous Internet-connected devices which are revolutionizing many domains, such as healthcare, transportation, smart cities, to mention only a few. Along with the unprecedented new opportunities, the IoT revolution is creating an enormous attack surface for potential sophisticated cyber attacks. In this context, Remote Attestation (RA) has gained wide interest as an important security technique to remotely detect adversarial presence and assure the legitimate state of an IoT device. While many RA approaches proposed in the literature make different assumptions regarding the architecture of IoT devices and adversary capabilities, most typical RA schemes rely on minimal Root of Trust by leveraging hardware that guarantees code and memory isolation. However, the presence of a specialized hardware is not always a realistic assumption, for instance, in the context of legacy IoT devices and resource-constrained IoT devices. In this paper, we survey and analyze existing software-based RA schemes (i.e., RA schemes not relying on specialized hardware components) through the lens of IoT. In particular, we provide a comprehensive overview of their design characteristics and security capabilities, analyzing their advantages and disadvantages. Finally, we discuss the opportunities that these RA schemes bring in attesting legacy and resource-constrained IoT devices, along with open research issues.

scholarly journals Cybersecurity Model Based on Hardening for Secure Internet of Things Implementation

2021 ◽  
Vol 11 (7) ◽  
pp. 3260
Author(s):  
Aarón Echeverría ◽  
Cristhian Cevallos ◽  
Ivan Ortiz-Garces ◽  
Roberto O. Andrade
Keyword(s):  
Internet Of Things ◽  
Smart Grids ◽  
Smart Cities ◽  
Data Driven ◽  
Security Level ◽  
Risk Matrix ◽  
Security Risks ◽  
Attack Surface ◽  
Iot Devices ◽  
Meta Analyses

The inclusion of Internet of Things (IoT) for building smart cities, smart health, smart grids, and other smart concepts has driven data-driven decision making by managers and automation in each domain. However, the hyper-connectivity generated by IoT networks coupled with limited default security in IoT devices increases security risks that can jeopardize the operations of cities, hospitals, and organizations. Strengthening the security aspects of IoT devices prior to their use in different systems can contribute to minimize the attack surface. This study aimed to model a sequence of seven steps to minimize the attack surface by executing hardening processes. Conducted a systematic literature review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) techniques. In this way, we were able to define a proposed methodology to evaluate the security level of an IoT solution by means of a checklist that considers the security aspects in the three layers of the IoT architecture. A risk matrix adapted to IoT is established to evaluate the attack surface. Finally, a process of hardening and vulnerability analysis is proposed to reduce the attack surface and improve the security level of the IoT solution.

scholarly journals New Method of Prime Factorisation-Based Attacks on RSA Authentication in IoT

Cryptography ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 20 ◽  
Author(s):  
Venkatraman ◽  
Overmars
Keyword(s):  
Smart Cities ◽  
Security Requirements ◽  
Resource Constrained ◽  
Euler’S Method ◽  
Binary Arithmetic ◽  
Cryptographic Keys ◽  
Authentication And Authorization ◽  
Potential Benefits ◽  
Iot Devices ◽  
And Performance

The potential benefits of the Internet of Things (IoT) are hampered by malicious interventions of attackers when the fundamental security requirements such as authentication and authorization are not sufficiently met and existing measures are unable to protect the IoT environment from data breaches. With the spectrum of IoT application domains increasing to include mobile health, smart homes and smart cities in everyday life, the consequences of an attack in the IoT network connecting billions of devices will become critical. Due to the challenges in applying existing cryptographic standards to resource constrained IoT devices, new security solutions being proposed come with a tradeoff between security and performance. While much research has focused on developing lightweight cryptographic solutions that predominantly adopt RSA (Rivest–Shamir–Adleman) authentication methods, there is a need to identify the limitations in the usage of such measures. This research paper discusses the importance of a better understanding of RSA-based lightweight cryptography and the associated vulnerabilities of the cryptographic keys that are generated using semi-primes. In this paper, we employ mathematical operations on the sum of four squares to obtain one of the prime factors of a semi-prime that could lead to the attack of the RSA keys. We consider the even sum of squares and show how a modified binary greatest common divisor (GCD) can be used to quickly recover one of the factors of a semi-prime. The method presented in this paper only uses binary arithmetic shifts that are more suitable for the resource-constrained IoT landscape. This is a further improvement on previous work based on Euler’s method which is demonstrated using an illustration that allows for the faster testing of multiple sums of squares solutions more quickly.

scholarly journals IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5541 ◽  
Author(s):  
Tom Lawrence ◽  
Li Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Computational Cost ◽  
Resource Constrained ◽  
Traffic Sign ◽  
Constrained Environments ◽  
Scale Down ◽  
Art Performance ◽  
Iot Devices

Two main approaches exist when deploying a Convolutional Neural Network (CNN) on resource-constrained IoT devices: either scale a large model down or use a small model designed specifically for resource-constrained environments. Small architectures typically trade accuracy for computational cost by performing convolutions as depth-wise convolutions rather than standard convolutions like in large networks. Large models focus primarily on state-of-the-art performance and often struggle to scale down sufficiently. We propose a new model, namely IoTNet, designed for resource-constrained environments which achieves state-of-the-art performance within the domain of small efficient models. IoTNet trades accuracy with computational cost differently from existing methods by factorizing standard 3 × 3 convolutions into pairs of 1 × 3 and 3 × 1 standard convolutions, rather than performing depth-wise convolutions. We benchmark IoTNet against state-of-the-art efficiency-focused models and scaled-down large architectures on data sets which best match the complexity of problems faced in resource-constrained environments. We compare model accuracy and the number of floating-point operations (FLOPs) performed as a measure of efficiency. We report state-of-the-art accuracy improvement over MobileNetV2 on CIFAR-10 of 13.43% with 39% fewer FLOPs, over ShuffleNet on Street View House Numbers (SVHN) of 6.49% with 31.8% fewer FLOPs and over MobileNet on German Traffic Sign Recognition Benchmark (GTSRB) of 5% with 0.38% fewer FLOPs.

scholarly journals Location-Sharing Protocol for Privacy Protection in Mobile Online Social Networks

2020 ◽  
Author(s):  
Ou Ruan ◽  
Lixiao Zhang ◽  
Yuanyuan Zhang
Keyword(s):  
Private Information ◽  
Online Social Networks ◽  
Smart Cities ◽  
Location Based Services ◽  
Broadcast Encryption ◽  
Security Risks ◽  
Location Sharing ◽  
Identity Privacy ◽  
And Performance ◽  
Network Server

Abstract Location-based services are becoming more and more popular in mobile online so-cial networks(mOSNs) for smart cities, but users' privacy also has aroused wide concern, such as locations, friend sets and other private information. At present, many location sharing protocols in mOSNs have been proposed, but these pro-tocols are inecient and ignore some security risks. In this paper, we propose a new location sharing protocol, which solves these two issues by using symmetric encryption and asymmetric encryption properly. We adopt the following methods to reduce the communication and computation costs: only setting up one loca-tion server; connecting social network server and location server directly instead of through celluar towers; avoiding broadcast encryption. We introduce dummy iden-tities to protect users' identity privacy, and prevent location server from inferring users' activity tracks by updating dummy identities in time. The details of security and performance analysis with the related protocols show that our protocol enjoys two advantages: (1) it's more ecient than the related protocols, which greatly re-duces the computation and communication costs; (2) it satises all security goals, however, most previous protocols only meet some security goals.

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

scholarly journals Location-sharing protocol for privacy protection in mobile online social networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ou Ruan ◽  
Lixiao Zhang ◽  
Yuanyuan Zhang
Keyword(s):  
Social Networks ◽  
Private Information ◽  
Online Social Networks ◽  
Smart Cities ◽  
Location Based Services ◽  
Broadcast Encryption ◽  
Location Sharing ◽  
Identity Privacy ◽  
And Performance ◽  
Network Server

AbstractLocation-based services are becoming more and more popular in mobile online social networks (mOSNs) for smart cities, but users’ privacy also has aroused widespread concern, such as locations, friend sets and other private information. At present, many protocols have been proposed, but these protocols are inefficient and ignore some security risks. In the paper, we present a new location-sharing protocol, which solves two issues by using symmetric/asymmetric encryption properly. We adopt the following methods to reduce the communication and computation costs: only setting up one location server; connecting social network server and location server directly instead of through cellular towers; avoiding broadcast encryption. We introduce dummy identities to protect users’ identity privacy, and prevent location server from inferring users’ activity tracks by updating dummy identities in time. The details of security and performance analysis with related protocols show that our protocol enjoys two advantages: (1) it’s more efficient than related protocols, which greatly reduces the computation and communication costs; (2) it satisfies all security goals; however, most previous protocols only meet some security goals.

scholarly journals An efficient pruning scheme of deep neural networks for Internet of Things applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chen Qi ◽  
Shibo Shen ◽  
Rongpeng Li ◽  
Zhifeng Zhao ◽  
Qing Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Internet Of Things ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Superior Performance ◽  
Compact Structure ◽  
Resource Limited ◽  
Benchmark Datasets ◽  
Iot Devices

AbstractNowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (IoT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2% and 94.1%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework and establish distributed training of neural networks in both cloud and edge.

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.

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