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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Andrew D’Angelo Chenglong Fu Xiaojiang Du Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems University of North Carolina at Charlotte Charlotte NC USA Information Warfare Division Air Force Research Laboratory Rome NY USA

IoT-enabled smart homes can be double-edged swords. On one side, the convenience and efficiency brought about by smart device integrations can improve the standard of living dramatically. However, on the other side, the prevalent interconnectivity among home devices can drastically increase the potential risk of attack. Therefore, in order to reduce the possibility of a successful attack we propose a complex correlation-based anomaly detection system powered by intricate two-to-one correlations. Through the use of a state-of-the-art transformer model, we present a novel correlation mining mechanism that leverages the power of attention weights to develop an understanding of the underlying correlations that exist between IoT events in a smart home environment. Using this knowledge, we use a special validation algorithm to verify 52 two-to-one correlations in our system. Furthermore, we simulate four distinct attack scenarios and attain an average detection accuracy and recall of 96.59% and 97.38% respectively. Our results indicate that our method is effective at identifying a range of IoT attacks and successfully demonstrates the capabilities of IoT correlations.

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Intelligent Synthesis of Individual Rehabilitation Plans for Patients after Joint Endoprosthesis Based on computer Vision Technologies 7

Intelligent Synthesis of Individual Rehabilitation Plans for...

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7th International Conference on Information Technologies in engineering Education, Inforino 2024

作者： Arseniev, D.G. Borisov, V.V. Misnik, A.E. Gomzhina, E.A. Shalukhova, M.A. Scientific Innovation Institute 'Mathematical Modeling and Intelligent Control Systems' Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia National Research University 'MPEI' Branch in Smolensk Department of Computer Engineering Smolensk Russia Information Technology Software Inter-State Educational Institution of Higher Education 'Belarusian-Russian University' Mogilev Belarus Early Medical Rehabilitation FSBI 'FCTOE' The Ministry of Health of the Russian Federation Smolensk Russia Inter-State Educational Institution of Higher Education 'Belarusian-Russian University Peter the Great St. Petersburg Polytechnic University Information Technology Software Mogilev Belarus

ISBN: (纸本)9798350371369

The article presents an innovative approach to the rehabilitation process for patients with joint endoprosthetics, focusing on the integration of a specialized software module based on computer vision technologies and the ontological approach. Developed for the Federal Centre for Traumatology, Orthopaedics, and Endoprosthesis in Smolensk, Russia, this system is designed to improve the quality of life for patients by offering personalized rehabilitation plans. The core of the study lies in the application of ontological models and meta-associative graphs for synthesizing individual rehabilitation plans, which streamline the recovery process and reduce the workload on healthcare professionals. This method allows for the processing of large amounts of information, providing detailed conclusions and recommendations for functional correction of the musculoskeletal system. The software utilizes computer vision to analyze patient movement and develop tailored rehabilitation strategies, overcoming the limitations of traditional diagnostic tools like MRI, which may not be suitable for all patients. By incorporating artificial intelligence and augmented reality, the system enhances the accuracy of patient assessments and the effectiveness of rehabilitation plans. Furthermore, the article discusses the application of this technology in the educational process at the Belarusian-Russian University, where students are introduced to cutting-edge software tools that assist in creating personalized rehabilitation plans. This educational integration not only enriches the students' learning experience but also fosters innovation in the development of new applications for computer vision in medical rehabilitation. The ontological approach, combined with modern technology, represents a significant advancement in the management of musculoskeletal system diseases, offering a more efficient, personalized approach to rehabilitation and recovery. © 2024 IEEE.

关键词： Application programs

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AutoDebloater: Automated Android App Debloating

AutoDebloater: Automated Android App Debloating

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IEEE International Conference on Automated software engineering (ASE)

作者： Jiakun Liu Xing Hu Ferdian Thung Shahar Maoz Eran Toch Debin Gao David Lo School of Computing and Information Systems Singapore Management University Singapore School of Software Technology Zhejiang University China School of Computer Science Tel Aviv University Israel Department of Industrial Engineering Tel Aviv University Israel

Android applications are getting bigger with an increasing number of features. However, not all the features are needed by a specific user. The unnecessary features can increase the attack surface and cost additional resources (e.g., storage and memory). Therefore, it is important to remove unnecessary features from Android applications. However, it is difficult for the end users to fully explore the apps to identify the unnecessary features, and there is no off-the-shelf tool available to assist users to debloat the apps by themselves. In this work, we propose AutoDebloater to debloat Android applications automatically for end users. AutoDebloater is a web application that can be accessed by end-users through a web browser. In particular, AutoDebloater can automatically explore an app and identify the transitions between activities. Then, AutoDebloater will present the Activity Transition Graph to users and ask them to select the activities they do not want to keep. Finally, AutoDebloater will remove the activities that are selected by users from the app. We conducted a user study on five Android apps downloaded from three categories (i.e., Finance, Tools, and Navigation) in Google Play and F-Droid. The results show that users are satisfied with AutoDebloater in terms of the stability of the debloated apps and the ability of AutoDebloater to identify features that are never noticed before. The tool is available at http://***. The code is available at https://***/jiakun-liu/autodebloater/ and the demonstration video can be found at https://***/Gmz0-p2n9D4.

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Work Engagement Recognition in Smart Office

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Procedia computer science 2022年 200卷 451-460页

作者： Congcong Ma Carman Ka Man Lee Juan Du Qimeng Li Raffaele Gravina Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong Special Administrative Region. School of Computer and Software Nanyang Institute of Technology Nanyang 473004 China. Department of Informatics Modeling Electronics and Systems University of Calabria Rende 87036 Italy.

The COVID-19 pandemic has forced a sudden change of traditional office works to smart working models, which however force many workers staying at home with a significant increase of sedentary lifestyle. Metabolic disorders, mental illnesses, and musculoskeletal injuries are also caused by the physical inactivity and chronic stress at work, threatening office workers' physical and physiological health. In the modern vision of smart workplaces, cyber-physical systems play a central role to augment objects, environments, and workers with integrated sensing, data processing, and communication capabilities. In this context, a work engagement system is proposed to monitor psycho-physical comfort and provide health suggestion to the office workers. Recognizing their activity, such as sitting postures and facial expressions, could help assessing the level of work engagement. In particular, head and body posture could reflects their state of engagement, boredom or neutral condition. In this paper we proposed a method to recognize such activities using an infrared sensor array by analyzing the sitting postures. The proposed approach can unobstructively sense their activities in a privacy-preserving way. To evaluate the performance of the system, a working scenario has been set up, and their activities were annotated by reviewing the video of the subjects. We carried out an experimental analysis and compared Decision Tree and k-NN classifiers, both of them showed high recognition rate for the eight postures. As to the work engagement assessment, we analyzed the sitting postures to give the users suggestions to take a break when the postures such as lean left/right with arm support, lean left/right without arm support happens very often.

关键词： Infrared Sensor Sitting Posture Recognition Smart Office Work Engagement

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A Way to Protect the Surveillance Data Stored in the Cloud Using FC and SVM Methods

A Way to Protect the Surveillance Data Stored in the Cloud U...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： D. Suganthi M Raju N.B. Mahesh Kumar M. Vasavi Oday Faris Washeel Tareq Hafdhi Abdtawfeeq Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Thandalam Chennai Tamilnadu India Department of Software Systems Sri Krishna Arts and Science College Coimbatore Department: Computer Science and Engineering Institute/university: Hindusthan Institute of Technology Valley Campus Pollachi Main Road Othakkalmandapam (Post) Coimbatore Tamil Nadu INDIA ET department CVR College of Engineering Hyderabad Collage of Nursing National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Building trust between consumers and service providers is critical to the functioning of cloud computing, a technology that offers a range of services over the Internet. When consumers choose which cloud services to utilise for processing and storing sensitive data, trust values are a major factor. In addition, service providers can employ trust values as a means of granting access to resources, protecting against malevolent users. The subjective trust model category, which evaluates trust based on user and service provider behaviour, is the one that this research suggests for trust models. Because trust is intrinsically ambiguous in our method, trust levels for cloud consumers and service providers are calculated using fuzzy logic and support vector machines. Using triangle membership functions for defuzzification and Gauss membership functions for fuzzification, we use the Mamdani fuzzy approach. In order to determine how trustworthy a resource is, our paradigm for evaluating trust takes performance and flexibility into account. Performance evaluation includes measurements related to workload and reaction time, whereas elasticity evaluation takes into account elements like scalability, availability, security, and usability. We apply fuzzy C-means clustering on pertinent criteria to establish trust levels for users, including faulty requests, fake requests, unauthorised requests, and total requests.

关键词： Support vector machines Performance evaluation Fuzzy logic Cloud computing Surveillance Scalability Vectors

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Resource-Aware Split Federated Learning for Fall Detection in the Metaverse

Resource-Aware Split Federated Learning for Fall Detection i...

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IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Mohamad Wazzeh Ahmad Hammoud Mohsen Guizani Azzam Mourad Hadi Otrok Chamseddine Talhi Zbigniew Dziong Chang-Dong Wang Department of Software and IT Engineering Ecole de Technologie Supérieure Montreal Canada Department of CSM Artificial Intelligence and Cyber Systems Research Center Lebanese American University Beirut Lebanon Department of ML Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE Department of Electrical Engineering Ecole de Technologie Supérieure Montreal Canada Department of Computer Science KU 6G Research Center Khalifa University Abu Dhabi UAE Department of Computer Science Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

As the Metaverse develops, it is becoming more crucial to prioritize the safety of users, especially regarding the potential risks, such as users experiencing dizziness or making incorrect movements that may lead to falls. With more virtual environments becoming increasingly available and immersive, detecting and preventing falls within the Metaverse is required. Given the constrained resources of wearable sensors, precise fall prediction models are critical to efficiently analyzing data gathered by these devices. Traditional fall detection systems require centralizing data collection, which raises privacy concerns over the collected data. Resource-aware Split Federated Learning (RSFL) enables collaboration among multiple devices within the Metaverse to train a fall detection model, all while preserving individual data privacy. The approach also leverages parallelism in Federated Learning (FL) and Split Learning (SL) by decomposing training tasks between clients and servers. Moreover, we devise an efficient client selection mechanism to ensure timely training and model convergence performance. We implemented our architecture and assessed its performance using a sensory dataset. The evaluation results with the baseline demonstrate our architecture's superiority in terms of convergence time. Our approach mitigates data heterogeneity and privacy concerns, creating secure and efficient fall detection systems for the Metaverse.

关键词： Training Wireless communication Data privacy Wireless sensor networks Metaverse Federated learning computer architecture Data models Fall detection Convergence

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Corrigendum to: Facial Surface Anthropometric Features and Measurements With an Emphasis on Rhinoplasty

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Aesthetic surgery journal 2022年第2期42卷 230-231页

作者： M Mazhar Celikoyar Michael F Pérez M Ilhan Akbas Oguzhan Topsakal Department of Otolaryngology Istanbul Florence Nightingale Hospital Istanbul Turkey. Computer Science Department Florida Polytechnic University Lakeland FL USA. Electrical Computer Software and Systems Engineering Department Embry-Riddle Aeronautical University Daytona Beach FL USA.

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant Security Controls

arXiv

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arXiv 2024年

作者： Gupta, Deepti Elluri, Lavanya Jain, Avi Moni, Shafika Showkat Aslan, Omer Dept. of Computer Information Systems Texas A&M University - Central Texas TX United States College of Business University of Tampa Florida United States Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida United States Department of Software Engineering Bandırma Onyedi Eylül University Bandırma Turkey

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamper-proof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls—such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture—this approach strengthens an organization’s defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth’s transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, enab

关键词： Blockchain

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Towards Instant Clustering Approach for Federated Learning Client Selection

Towards Instant Clustering Approach for Federated Learning C...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Sarhad Arisdakessian Omar Abdel Wahab Azzam Mourad Hadi Otrok Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Department of Computer Science Lebanese American University Beirut Lebanon Division of Science New York University Abu Dhabi UAE Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE

In just few years, Federated Learning (FL) started to gain unprecedented attention given its ability to solve some fundamental privacy and communication challenges of traditional machine learning. Client selection is one of the main challenges in FL and is usually done in a random fashion, where the central server arbitrarily selects a certain number of clients to participate in each training round. However, given the heterogeneity of the client devices in terms of data quality and resource availability, randomly selecting clients is likely to result in long local training time and thus delayed global model’s convergence. To address this problem, in this work, we propose a real-time and on-demand client selection mechanism that employs the DBSCAN (Density-Based Spatial clustering of Applications with Noise) clustering technique from machine learning to group the clients into a set of homogeneous clusters based on a set of criteria defined by the FL task owners, such as resource availability, data quality, data size, data freshness and non-IID degree. Based on the requirements of each FL task, the server then intelligently selects the clusters of clients that best match with each task’s requirements, thus improving the performance of the overall federated learning process. Experiments suggest that our solution significantly improves the accuracy of FL compared to the Vanilla FL approach.

关键词： Training Privacy Federated learning Data integrity Clustering algorithms Real-time systems Data models

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Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data

arXiv

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arXiv 2023年

作者： Cheriguene, Youssra Jaafar, Wael Yanikomeroglu, Halim Kerrache, Chaker Abdelaziz Laboratoire d'Informatique et de Mathématiques Université Amar Telidji de Laghouat Laghouat Algeria Department of Software and Information Technology Engineering École de Technologie Supérieure MontrealQC Canada Lab Department of Systems and Computer Engineering Carleton University OttawaON Canada

Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this paper a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods. © 2023, CC BY.

关键词： Edge computing

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