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Cybersecurity in local governments: A systematic review and framework of key challenges

Urban Governance 2025年第1期5卷 1-19页

作者： Hossain, Sk Tahsin Yigitcanlar, Tan Nguyen, Kien Xu, Yue City 4.0 Lab School of Architecture and Built Environment Queensland University of Technology 2 George Street Brisbane 4000 QLD Australia School of Electrical Engineering and Robotics Queensland University of Technology 2 George Street Brisbane 4000 QLD Australia School of Computer Science Queensland University of Technology 2 George Street Brisbane 4000 QLD Australia

Cybersecurity has emerged as a critical concern in modern urban governance, as local governments now extend their responsibilities beyond traditional infrastructure management to safeguard digital systems and citizen data in increasingly digitised smart city environments. Despite the importance of robust cybersecurity measures, there is a concerning gap—i.e., local governments often exhibit a cybersecurity posture that is inadequately prepared to counter the rising cyber threats. This discrepancy underscores the urgency for a comprehensive understanding of the challenges restricting effective cybersecurity in local governmental settings. This paper aims to elucidate these challenges of local governments in a structured framework utilising a systematic literature review method named the PRISMA protocol. The findings reveal that the stakes are high when it comes to cybersecurity for local governments, with the key challenges being financial resource constraints, technological vulnerabilities, human factors, and the regulatory framework. This study recommends improving interdepartmental coordination, adopting relevant policies, and executing targeted cybersecurity training to address these challenges. The study also highlights unconventional approaches to mitigate financial and skill resource limitations, including the utilisation of open-source security tools, outsourcing specific cybersecurity functions, and establishing collaborations with local cybersecurity agencies, academic institutions, and the private sector to obtain expertise and advanced resources. Overall, this study offers valuable insights into the critical cybersecurity challenges faced by local governments and provides specific recommendations, equipping urban policymakers, administrators, and researchers with valuable guidance to enhance cybersecurity resilience within this critical yet understudied research domain. © 2024 The Author(s)

关键词： Cyber threat Cybersecurity Local government Municipality Smart city Urban governance

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Secured Blockchain-Based Energy Trading Framework for Smart Microgrid Networks

Secured Blockchain-Based Energy Trading Framework for Smart ...

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International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2023

作者： Essardi, Hafida Oukhatar, Ayoub Najib, Mehdi Bakhouya, Mohamed Ouadghiri, Driss El Department of Computer Sciences IA Laboratory Faculty of Sciences Moulay Ismail University Meknes Morocco Department of Computer Sciences Higher School of Technology of Ouarzazate Ibn Zohr University Agadir80000 Morocco College of Engineering and Architecture International University of Rabat LERMA Lab & TICLab Sala Al Jadida Morocco

ISBN: (纸本)9783031542879

In order to find a practical solution to integrate green and renewable energy technologies into the existing power system, the smart grid approach was recently developed. In this way, the Internet-connected smart microgrids, commonly referred to the Internet of Energy, is emerging as an innovative process for securing energy at any time and from any location. The concept of microgrid systems together with advanced digitization concepts (e.g., Internet of Thing) at the production/consumption and distribution level are evolving as key components of deploying intelligent energy management systems. Therefore, it has become essential to establish reliable and effective energy trading systems among networked microgrids in order to reduce transmission losses while increasing reli- ability. Thus, recent research work has started to concentrate on how energy could be efficiently shared. Hence, in order to make energy trad- ing decisions safely for managing the overall load, this paper introduces a blockchain-based secure peer-to-peer energy trading management framework. A platform’s prototype is under development for being deployed in real sitting scenarios, e.g., farms’ micro-grid systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Smart contract

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Cross-technology Communication between WiFi and ZigBee 9

Cross-Technology Communication between WiFi and ZigBee

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9th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, MAPE 2022

作者： Fan, Wendong Chen, Tian Gu, Yu Li, Yang Hefei University of Technology S2AC Lab School of Computer and Information Hefei China Hefei University of Technology Institute of Affective Computing and Systems Architecture Hefei China Anhui Agricultural University School of Information & Computer Hefei China

ISBN: (数字)9781665427753

ISBN: (纸本)9781665427753

The coexistence of heterogeneous devices in the Internet of Things wireless network brings a new topic of cross-technology communication in order to improve the communication efficiency between heterogeneous devices and promote the cooperation between heterogeneous devices. WiFi and ZigBee are the two most commonly used IoT devices in IoT environments. They have the advantages of low communication delay, high speed, low cost, and easy deployment. Since the spectrum of WiFi and ZigBee overlap, ZigBee signal will affect the transmission of CSI signal. We propose a CTC technique based on machine learning and deep learning, which uses only WiFi channel state information (CSI), and can distinguish whether there is ZigBee signal transmission in WiFi signals by classifying WiFi channel state information. In this paper, machine learning classification methods and deep learning methods are used to classify CSI sequences respectively. We extract the features of 8 CSI sequences to improve the accuracy of machine learning classifier and LSTM classifier, and adopt transformer network classifier to further improve the classification accuracy. In our experimental dataset, the highest accuracy rate can reach 94.9%. The evaluation results show that the accuracy of this method is significantly higher than the existing classification methods. © 2022 IEEE.

关键词： Channel state information

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Point-Correlate Adversarial Transformer for Unsupervised Multivariate Time Series Anomaly Detection

Point-Correlate Adversarial Transformer for Unsupervised Mul...

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International Conference on computer Supported Cooperative Work in Design

作者： Huan Li Xiangjie Kong Guojiang Shen Xiaoran Yan Yao Yang Mario Collotta School of Computer Science and Technology Zhejiang University of Technology Hangzhou China Zhejiang Lab Hangzhou China Faculty of Engineering and Architecture Kore University of Enna Enna Italy

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Multivariate time series anomaly detection plays a crucial role in industrial production. However, the inherent complexity and randomness of time series pose significant challenges. Furthermore, existing detection methods struggle to provide reliable explanations for outliers. To address these issues, this paper presents an unsupervised multivariate time series anomaly detection model named Point-Correlate Adversarial Transformer (PCAT). In this work, we leverage Transformer networks to capture the underlying correlations between different points in a time series and reconstruct the original sequence. By analyzing the correlation differences and reconstruction errors, we identify anomalies at the point level. Our model incorporates an adversarial structure, enabling unsupervised learning and enhancing the learning capability and robustness of the detection network. Experimental evaluations on four real-world datasets demonstrate the superiority of our approach over other state-of-the-art models in terms of detection delay and accuracy.

关键词： Training Correlation Accuracy Federated learning Time series analysis Production Transformers

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FlatProxy: A DPU-centric Service Mesh architecture for Hyperscale Cloud-native Application

arXiv

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arXiv 2023年

作者： Li, Ming Lu, Wenyan Lin, Hanyue Wu, Jingya Zhang, Yu Yan, Guihai State Key Lab of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Service mesh is a fundamental technology for building cloud-native applications, which ensures the stable running of a large number of services by an intermediate layer that governs communication between services. However, service mesh is not well suited for high-performance scenarios. The root cause is that the current service mesh is not suitable for the evolution of cloud-native applications. On the one hand, the service mesh built on CPU cannot listen to communication bypassing the CPU. On the other hand, service mesh includes many I/O-intensive and computationally-intensive tasks that can overload CPU cores as traffic grows beyond CPU performance. Therefore, we propose a data-centric service mesh that migrates the proxy of the service mesh to the entrance of the network. Moreover, we also design the DPU-centric FlatProxy, a data-centric service mesh based on DPU. There are three advantages to the DPU-centric service mesh. Firstly, it takes over all traffic flow in and out of the node, which expands the sense scale of the service mesh from container to node. Secondly, it improves communication performance and reduces host resource usage by offloading some functions and optimizing communication. Thirdly, it minimizes performance and security issues through the physical isolation of business services and cloud infrastructure. Compared with Envoy, the current mainstream service mesh implementation, FlatProxy reduces latency by 90% and improves throughput by 4x in Gbps and 8x in qps, and it only occupies a small amount of CPU resources. Copyright © 2023, The Authors. All rights reserved.

关键词： Mesh generation

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Brain Cortical Surface Can Predict Fiber Trajectories

Brain Cortical Surface Can Predict Fiber Trajectories

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IEEE International Symposium on Biomedical Imaging

作者： Tianle Han Yifan Lv Mengshen He Yiheng Liu Tianming Liu Bao Ge School of Physics & Information Technology Shaanxi Normal University Xi'an China Department of Computer Science and Bioimaging Research Center Cortical Architecture Imaging and Discovery Lab The University of Georgia GA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Gray matter and white matter are fundamental constituents of the brain, and exploring the relationship between them is for gaining insights into the intricate architecture of the brain. This study utilizes information extracted from the gray matter surface of the brain to predict the trajectories of white matter fibers. Specifically, it predicts the coordinates of fiber trajectories by utilizing both fiber endpoints and reference points on the cortical surface. Both qualitative analysis and extensive quantitative experiments confirm that information derived from the brain's cortical surface can be employed to predict fiber trajectories. Furthermore, this conclusion remains robust across diverse fiber tracking and surface reconstruction techniques. The study introduces a novel perspective, elucidating the fundamental principles of fiber connectivity and the intricate relationship between gray matter and white matter.

关键词： Surface reconstruction Brain Pathology Nerve fibers Magnetic resonance imaging Grey matter Streaming media

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Tighter Bounds of Speedup Factor of Partitioned EDF for Constrained-Deadline Sporadic Tasks 42

Tighter Bounds of Speedup Factor of Partitioned EDF for Cons...

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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： Liu, Xingwu Chen, Zizhao Han, Xin Sun, Zhenyu Guo, Zhishan School of Mathematical Sciences Dalian University of Technology China SK-Lab of Media Convergence Production Technology and Systems Chinaso Inc School of Software Technology Dalian University of Technology China SK-Lab of Computer Architecture ICT CAS University of CAS Beijing China Department of Electrical and Computer Engineering University of Central Florida United States

ISBN: (纸本)9781665428026

Even though earliest-deadline-first (EDF) is optimal in terms of uniprocessor schedulability, it is co-NP-hard to precisely verify uniprocessor schedulability for constrained-deadline task sets. The most efficient way to solve this problem in polynomial time is via a partially linear approximation of the demand bound function. Such approximation leads to a simple uniprocessor schedulability testing with speedup factor ρ. Such a result further leads to Deadline-Monotonic Partitioned-EDF on multi-processors with speedup factor of 1 + ρ − 1/m (where m is the number of processors). The current state of the art results indicate that ρ is within the range [1.5, 14/9]. Especially, it has been a conjecture that ρ = 1.5. This paper improves the range of ρ to (1.5026, 1.5380). The improved lower bound disproves the conjecture of lower bound 1.5. A novel technique is to construct an auxiliary function that is larger than the approximate demand bound function but keeps the supremum ρ unchanged. It solves the dilemma that beating the lower bound 1.5 requires extremely large task sets, while the large size makes it difficult to check the schedulability. This technique not only enables us to disprove 1.5 by a task set of only eight tasks, but also sheds light on future work in transferring/downsizing task sets and deriving utilization bound based tests for various workload abstraction models, such as DAG tasks. ©2021 IEEE

关键词： Polynomial approximation

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Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Robust Global Localization in Autonomous Mobile Robotics

arXiv

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arXiv 2023年

作者： Stathoulopoulos, Nikolaos Pagliari, Emanuele Davoli, Luca Nikolakopoulos, George The Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå97187 Sweden Lab Department of Engineering and Architecture University of Parma Parma43124 Italy

This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi fingerprinting in a pre-mapped environment. This is motivated by the increasing demand for reliable localization in complex scenarios, such as urban areas or underground mines, requiring robust systems able to overcome limitations faced by traditional Global Navigation Satellite System (GNSS)-based localization methods. By leveraging the complementary strengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluate the confidence of each prediction as an indicator of potential degradation, we propose a redundancy-based approach that enhances the system’s overall robustness and accuracy. The proposed framework allows independent operation of the LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining the predictions while considering their confidence levels, we achieve enhanced and consistent performance in localization tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Safety Guaranteed Power-Delivered-to-Load Maximization for Magnetic Wireless Power Transfer

Safety Guaranteed Power-Delivered-to-Load Maximization for M...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Wangqiu Zhou Xinyu Wang Hao Zhou Shenyao Jiang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

关键词： Performance evaluation Monte Carlo methods System performance Inductive charging Prototypes Wireless power transfer Parallel processing

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Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Robust Global Localization in Autonomous Mobile Robotics

Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Ro...

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International Conference on Advanced Robotics (ICAR)

作者： Nikolaos Stathoulopoulos Emanuele Pagliari Luca Davoli George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden Department of Engineering and Architecture Internet of Things (IoT) Lab University of Parma Parma Italy

This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi finger-printing in a pre-mapped environment. This is motivated by the increasing demand for reliable localization in complex scenarios, such as urban areas or underground mines, requiring robust systems able to overcome limitations faced by traditional Global Navigation Satellite System (GNSS)-based localization methods. By leveraging the complementary strengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluate the confidence of each prediction as an indicator of potential degradation, we propose a redundancy-based approach that enhances the system's overall robustness and accuracy. The proposed framework allows independent operation of the LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining the predictions while considering their confidence levels, we achieve enhanced and consistent performance in localization tasks.

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