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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Zhang, Zuowei Liu, Zhe Ma, Zongfang Zhang, Yiru Wang, Hao University of Rennes 1 Institut de Recherche en Informatique et Systèmes Aléatoires France Xi'an University of Architecture and Technology School of Information and Control Engineering China Norwegian University of Science and Technology Department of Computer Science Norway

ISBN: (纸本)9798350322279

Imputing the incomplete patterns in clustering tasks is a common but risky procedure, because the estimated values may affect the real distribution of the data and deteriorate the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) with uncertainty and imprecision reasoning is proposed in this paper. First, the complete patterns are grouped into a few clusters to obtain the corresponding reliable centers, and thereby are divided into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classify unreliable patterns and incomplete patterns edited by the neighbors. Finally, some imprecise patterns are carefully reassigned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. The simulation results show that the BPC has the potential to deal with real datasets. © 2023 IEEE.

关键词： Uncertainty analysis

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Simulation Optimization Method for Intelligent Compaction Construction of Railway Subgrade Oriented Towards Quality Control

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SSRN 2024年

作者： Ma, Zhaoxi Zhao, Qin Jia, Hongfei Sheng, Yiheng Yang, Mingsong Gu, Jingtian Zhu, Yiyun Hei, Xinhong Department of Civil Engineering and Architecture Xi’an University of Technology Xi’an710048 China Department of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China

This study, conducted in the context of railway subgrade construction, focuses on quality control and explores an effective method by developing a simulation model and a real-time optimization algorithm. A real-time optimization algorithm was initially utilized to optimize the compaction scheme based on Intelligent Compaction (IC) technology. Subsequently, a comprehensive construction simulation process was established, defining features such as entity arrival patterns, service modes, queueing rules, and service processes, ensuring the accuracy and reliability of the simulation process. Simulation experiments were then conducted to determine the globally optimal compaction construction scheme. Comparison with the original construction scheme revealed that the optimal compaction scheme met quality requirements and improved construction efficiency by nearly 11.4%. Additionally, it facilitated synergistic operations with other construction activities. Thus, this study's quality control method offers a scientific and efficient management tool and decision-making basis for railway subgrade construction, possessing significant practical and promotional value. © 2024, The Authors. All rights reserved.

关键词： Discrete event simulation

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MadDroid: malicious adware detection in Android using deep learning

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Journal of Cyber Security technology 2024年第3期8卷 163-190页

作者： Seraj, Saeed Pavlidis, Michalis Trovati, Marcello Polatidis, Nikolaos School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom Department of Computer Science Edge Hill University Omrskirk United Kingdom

The majority of Android smartphone apps are free. When an application is used, advertisements are displayed in order to generate revenue. Adware-related advertising fraud costs billions of dollars each year. Adware is a form of advertising-supported software, that turns into malware when it automatically installs additional malware and adware on an infected device, steals user data, and exposes other vulnerabilities. Better techniques for detecting adware are needed due to the evolution of increasingly sophisticated evasive malware, particularly adware. Even though significant work has been done in the area of malware detection, the adware family has received very little attention. This paper presents a deep learning-based scheme called MadDroid to detect malicious Android adware based on static features. Moreover, this paper delivers a novel dataset that consists of malicious Adware and benign applications and an optimised Convolutional neural network (CNN) for detecting Adware infected by malware based on the permissions of the applications. The results indicate an average classification rate that is higher than previous work for individual adware family classification in terms of well-known evaluation metrics. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Android (operating system)

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Simulation Optimization Method for Intelligent Compaction Construction of Railway Subgrade Oriented Towards Quality Control

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SSRN 2024年

关键词： Discrete event simulation

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On the Design of a New Stochastic Meta-Heuristic for Derivative-Free Optimization 22nd

On the Design of a New Stochastic Meta-Heuristic for Der...

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22nd International Conference on Computational Science and Its Applications , ICCSA 2022

作者： Cruz, N.C. Redondo, Juana L. Ortigosa, E.M. Ortigosa, P.M. Department of Computer Architecture and Technology University of Granada Granada Spain Department of Informatics University of Almería ceiA3 Excellence Agri-food Campus Almería Spain

ISBN: (纸本)9783031105616

Optimization problems are frequent in several fields, such as the different branches of Engineering. In some cases, the objective function exposes mathematically exploitable properties to find exact solutions. However, when it is not the case, heuristics are appreciated. This situation occurs when the objective function involves numerical simulations and sophisticated models of reality. Then, population-based meta-heuristics, such as genetic algorithms, are widely used because of being independent of the objective function. Unfortunately, they have multiple parameters and generally require numerous function evaluations to find competitive solutions stably. An attractive alternative is DIRECT, which handles the objective function as a black box like the previous meta-heuristics but is almost parameter-free and deterministic. Unfortunately, its rectangle division behavior is rigid, and it may require many function evaluations for degenerate cases. This work presents an optimizer that combines the lack of parameters and stochasticity for high exploration capabilities. This method, called Tangram, defines a self-adapted set of division rules for the search space yet relies on a stochastic hill-climber to perform local searches. This optimizer is expected to be effective for low-dimensional problems (less than 20 variables) and few function evaluations. According to the results achieved, Tangram outperforms Teaching-Learning-Based Optimization (TLBO), a widespread population-based method, and a plain multi-start configuration of the stochastic hill-climber used. © 2022, The Author(s).

关键词： Function evaluation

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Integrating Post-Quantum Cryptography Plugins for IPsec Offloads to Data Processing Units in the Cloud-Edge Continuum

Integrating Post-Quantum Cryptography Plugins for IPsec Offl...

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International Conference on Network Protocols

作者： Abraham Cano Carlos Rubio Garcia Raphaël Frantz Idelfonso Tafur Monroy José Luis Imaña Juan José Vegas Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands Department of Computer Architecture and Automation Complutense University of Madrid Madrid Spain Software Architecture NVIDIA corporation Yokneam Israel

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

The imminent advent of Quantum computers poses a significant threat to the cryptographic algorithms supporting the public key infrastructure (PKI) of widely used communication protocols. High Performance Computing (HPC) data centers among other interested parties are well aware of the catastrophic consequences quantum attacks could have on their PKI and are consequently transitioning to Post-Quantum Cryptographic (PQC) methods, despite the substantial overhead this introduces for handling incoming network packets. This work addresses the transition to PQC within the context of the Cloud-Edge Continuum by integrating the Open Quantum Safe (OQS) library into the accelerated strongSwan developed by Mellanox for Data Processing Units (DPUs). This integration offloads cryptographic operations from central servers to data DPUs distributed across the cloud-edge continuum. Our solution ensures quantum security by providing PQ authentication through CRYSTALS-Dilithium or CRYSTALS-FALCON, PQ key exchanges via CRYSTALS-Kyber, and confidential data transmission using AES-256. Additionally, the deployment of this implementation on DPUs helps reduce the computational load on both HPC data centers and edge devices, promoting more efficient and secure operations across the entire cloud-edge continuum.

关键词： Resistance Data centers Quantum computing Protocols Public key Throughput Data processing Libraries Cryptography Servers

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AI-Driven Smart Health Monitoring System Using Wearable IoT Devices and Predictive Analytics 2

AI-Driven Smart Health Monitoring System Using Wearable IoT ...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Prashanthi, Bh. Sharma, Ashish Mahima, M. Navya, K. Al-Fatlawy, Ramy Riad Chidambaranathan, Senthilnathan GRIET Bachupally Department of CSE Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India New Prince Shri Bhavani College of Engineering and Technology Department of IT Tamil Nadu Chennai600073 India CMR Institute of Technology Department of ECE Telangana Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Virtusa Corporation Department of Architecture and Design NJ United States

ISBN: (纸本)9798350368109

The demonstrative realisation of this research is the smart health monitoring system, proposed and designed to employ wearable IoT technology and predictive analysis for constant health check. The system is intended to gather the physiological data;for example, pulse, blood pressure, and oxygen saturation and others via wearables and process it using sophisticated AI techniques and approaches, including deep learning based on CNN and LSTM. According to the systems, there are predictions that need to be offered by means of the programs and health risks that should be anticipated for timely intervention in order to increase chances for the patients' recovery. Although the system is designed for home use, the writers applied a cloud-based solution that provides scalability and real-time data processing;data is securely transferred from the wearable devices to the cloud through Bluetooth and Wi-Fi. This physiological data is augmented by the other public datasets available in the healthcare domain such that the AI learning algorithms can learn from an extensive health record. From the above research, it manifests how the system was able to predict serious health precursors including;heart attack and respiratory failure with LSTM models having prediction rate of more than 96%. It also delivers on-time alarms to the clinical practitioners, with average response time of less than three seconds for critical states. A set of surveys concerning the usability and comfort of the wearable devices among the patients has been conducted and the results show high satisfaction levels reported by the patients about the accuracy as well as simplicity of the system. Despite the introduced effort of the system, there exist significant concerns involving data protection, compatibility, and AI ethic;therefore, the future study should overcome such shortcomings. With these advancements of an artificial intelligence driven health monitoring system, healthcare systems would shift to being mor

关键词： Blood pressure

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A Trusted and Decentralized Federated Learning Framework for IoT devices in Smart City 17

A Trusted and Decentralized Federated Learning Framework for...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Wang, Sheng Chen, Chun Han, Bing Zhu, Jun Nanjing Construction Market Supervision Station Nanjing China Jiangsu Public Works Co.Ltd Nanjing China Nanjing University of Aeronautics and Astronautics Department of College of Computer Science and Technology Nanjing China City University of Hong Kong Department of Architecture and Civil Engineering Hong Kong

ISBN: (纸本)9798350351637

Smart cities, through investment in human and social capital as well as traditional and modern communications infrastructure, drive sustainable economic growth and high quality of life. IoT devices, as crucial components of smart cities, leverage the Federated Learning (FL) paradigm to contribute significantly to the advancement of smart cities by providing the value of data while safeguarding local data. However, existing federated learning frameworks for smart cities face several issues, including excessive centralization of decision-making and data, unbalanced resource allocation, and security and privacy concerns. To address these challenges, This paper aims to propose a blockchain-based federated learning framework for IoT devices in smart cities. We combine multiple security and privacy-preserving techniques such as differential privacy and Trusted Execution Environments (TEE) with federated learning to establish a framework that facilitates secure and trustworthy data exchange and FL requirements for IoT devices in smart cities. Moreover, we offload the local training computation from the limited IoT devices to the edge server and execute trust aggregation on the blockchain smart contracts. We build a prototype of our designed system and conduct experiments on diverse federated learning datasets. Experimental results demonstrate that our scheme achieves high efficiency while ensuring security and privacy. Through this work, we provide a viable solution for federated learning in smart cities, thereby advancing the sustainable development and intelligence of smart cities. © 2024 IEEE.

关键词： Federated learning

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Landslide Disaster Assessment with Integration of Seismic, Social Media and Remote Sensing Data 6

Landslide Disaster Assessment with Integration of Seismic, S...

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6th International Conference on Signal Processing and Information Security, ICSPIS 2023

作者： Rawat, Deepak Sharma, M.L. Kumar, Roshan Saxena, Nishant Kumar, Atul Kadir, Abdul Indian Institute of Technology Roorkee Centre of Excellence in Disaster Mitigation and Management Uttarakhand Roorkee India Indian Institute of Technology Roorkee Department of Earthquake Engineering Roorkee Uttarakhand Roorkee India Miami College Henan University Department of Electronics and Information Technology Kaifeng Henan China Indian Institute of Technology Roorkee Department of Architecture and Planning Uttarakhand Roorkee India Indian Institute of Technology Roorkee Computer Science and Engineering Department Uttarakhand Roorkee India

ISBN: (纸本)9798350329599

On February 7, 2021, a rockfall in the Rishiganga Valley killed about 200 people. For the integration of seismic, social media, and remote sensing, a case study on this tragedy has been conducted. This study explores the role of social media in managing Himalayan landslides in conjunction with remote sensing data analytics. We look at how these technologies can enhance damage management, quick mobilization, and early warning systems. A literature review and case studies of the successful use of these technologies in other disaster-prone areas will support these suggestions. The intention is to equip decision-makers and experts in disaster management with the practical knowledge they need to effectively prepare for and respond to Himalayan landslide events, saving lives and minimizing the impact of natural hazards. © 2023 IEEE.

关键词： Remote sensing

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Data Availability Optimization for Cyber-Physical Systems 15

Data Availability Optimization for Cyber-Physical Systems

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Li, Liying Cong, Peijin Zhou, Junlong Gu, Zonghua Li, Keqin School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Department of Applied Physics and Electronics Ume˚a University Sweden Department of Computer Science State University of New York United States

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

As the backbone of Industry 4.0, Cyber-Physical Systems (CPSs) have attracted extensive attention from industry, academia, and government. Missing data is a common problem in CPS data processing and may cause incorrect results and eventually serious malfunction. Existing data availability optimization methods either rely on a large amount of complete training data or suffer from poor performance. To solve these problems, this paper proposes an iterative data availability optimization method for CPSs. Specifically, the proposed method first pre-processes the raw dataset by using a Singular Value Decomposition-based feature selection approach to identify crucial features and reduce computation overheads. It then makes an initial guess for missing values via a designed K-Means-based imputation approach. The appropriate initial estimation decreases the probability of the proposed method falling into the local optimum. Finally, the proposed method iteratively estimates missing data based on the Orthogonal Matching Pursuit algorithm. The proposed method optimizes data availability by accurately imputing missing values. Simulation results on two datasets demonstrate that compared to multiple state-of-the-art approaches, the proposed data availability optimization method can reduce imputation error by up to 99.65%. © 2022 IEEE.

关键词： Iterative methods

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