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FedECCR: Federated Learning Method with Encoding Comparison and Classification Rectification 19th

FedECCR: Federated Learning Method with Encoding Comparison...

19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yan Zheng, Hui Wang, Xin Zhang, Beibei Zhou, Mingyao Zhang, Jilin Ren, YongJian School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Zhejiang Lab Hangzhou311100 China Hangzhou Huawei Communication Technology Co. Ltd. Hangzhou310052 China

ISBN: (纸本)9783031545306

Federated learning is a distributed training method that integrates multi-party data information using privacy-preserving technologies through dispersed client data sets to jointly construct a global model under the coordination of a central server. However, in practical applications, there is a high degree of data distribution skewness among clients, which causes the optimization direction of the client models to diverge, resulting in model bias and reducing the accuracy of the global model. Existing methods require the calculation and transmission of much information to correct the optimization direction of the client models, or only roughly limit the deviation of the client models end-to-end, ignoring targeted processing of the internal structure of the model, resulting in unclear improvement effects. To address these problems, we propose a federated optimization algorithm FedECCR based on encoding contrast and classification correction. This algorithm divides the model into an encoder and a classifier. It utilizes prototype contrastive training of the model encoder and unbiased classification correction of the classifier. This approach notably improves the accuracy of the global model while maintaining low communication costs. We conducted experiments on multiple data sets to evaluate the validity of our method, and the quantified results showed that FedECCR can improve the global model classification accuracy by approximately 1% to 6% compared to FedAvg, FedProx, and MOON. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Classification (of information)

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Transmission Power Control for Interference Reduction in Cellular D2D Networks

Transmission Power Control for Interference Reduction in Cel...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Mehrdad Sadehvand Neda Moghim Behrouz Shahgholi Ghahfarokhi Sachin Shetty Department of Computer Engineering University of Isfahan Isfahan I.R.Iran Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk USA

Interference is one of the most critical issues in the cellular Device-to-Device (D2D) networks. The sharing of radio resources in cellular and D2D communications offers spectrum efficiency advantages for the cellular network. However, resource sharing also introduces interference, leading to a reduction in the quality of service experienced by users. In this paper, we propose an adversarial Multi-Armed Bandit learning-based transmission Power Control method called MAB-PC for both cellular and D2D transmitters. The objective of this method is to ensure the minimum service quality for users, considering the minimum spectral efficiency and maximum block error rate. Additionally, to address the conflicting objectives of D2D and cellular communications, MAB-PC is modeled as a Pareto optimization problem for D2D transmitters. MAB-PC is a distributed method that minimizes signaling overhead and relies solely on local Channel State Information (CSI). The effectiveness of the proposed method is evaluated based on reliability, total data rate, spectral efficiency, block error rate, and outage probability, demonstrating superior performance compared to its counterpart.

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FedAG:A Federated Learning Method Based on Data Importance Weighted Aggregation

FedAG:A Federated Learning Method Based on Data Importance W...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Xu, Mengchu Zeng, Yan Xue, Meiting Zhang, Ji-Lin Wan, Jian Zhou, Mingyao Wen, Yilin Shi, Yukun Hangzhou Dianzi University School of Computer Science and Technology Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou China Zhejiang Engineering Research Center of Data Security Governance Hangzhou China Hangzhou Dianzi University School of Cyberspace Hangzhou China Huawei Enterprise Communication Technology Co Hangzhou China

ISBN: (纸本)9798350345384

Federated learning is a distributed machine learning approach that trains models with multiple clients and data locally. However, the existing methods ignore the differences between local models caused by the data heterogeneity, and give a high aggregation weight for the low-quality local model, which leads to low accuracy of the global model. For this problem, we propose a federated learning method based on data importance weighted aggregation, FedAG. It constructs an evaluation model of data importance based on the data distribution skewness, data volume, and data quality of clients, and aggregates local models with different weights computed by the evaluation model of data importance into a global model. We make experiments on five datasets including EMNIST, MNIST, and CIFAR-10. The results show that the accuracy of FedAG is respectively improved by 6.5%, 1.8%, 1.3% and 2.1%, compared with FedAvg, FedProx, FedVar, and Scaffold algorithms. © 2023 IEEE.

关键词： Scaffolds

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Multiclass Classification Approaches for Intrusion Detection in IoT-Driven Aerial Computing Environment

Multiclass Classification Approaches for Intrusion Detection...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Saksham Mittal Amit Kumar Mishra Mohammad Wazid D. P. Singh Ashok Kumar Das Sachin Shetty Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Modeling Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Center for Cybersecurity Education and Research Old Dominion University Suffolk VA USA

Aerial Computing is one of the applications of Internet of Things (IoT) which makes use of autonomous aerial devices, such as drones and unmanned aerial vehicles (UAVs). The ubiquitous nature of IoT and aerial computing is poised to revolutionize our daily lives by enabling seamless real-time information sharing among interconnected objects. However, ensuring the safety and security of such network is crucial in preventing potential threats and attacks. The purpose of this study is to develop a sophisticated intrusion detection system that is effective, efficient, and intelligent using complex machine learning models trained on relevant intrusion detection datasets. In this article, the multiclass classification approaches for intrusion detection in IoT-driven aerial computing environment are presented (in short, MCA-IDAC). In the comparative study, it has been observed that proposed MCA-IDAC performs significantly better than the other existing competing schemes, in terms of important performance parameters.

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Facs-Charm: A Hybrid Agent-Based and Discrete-Event simulation Approach for Covid-19 Management at Regional Level 22

Facs-Charm: A Hybrid Agent-Based and Discrete-Event Simulati...

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Proceedings of the Winter simulation Conference

作者： Anastasia Anagnostou Derek Groen Simon J. E. Taylor Diana Suleimenova Nura Abubakar Arindam Saha Kate Mintram Maziar Ghorbani Habiba Daroge Tasin Islam Yani Xue Edward Okine Nana Anokye Modeling and Simulation Group Department of Computer Science Brunel University London Uxbridge UK Global Public Health Department of Health Sciences Brunel University London Uxbridge UK

Pandemics have huge impact on all aspect of people's lives. As we have experienced during the Coronavirus pandemic, healthcare, education and the economy have been put under extreme strain. It is important therefore to be able to respond to such events fast in order to limit the damage to the society. Decision-makers typically are advised by experts in order to inform their response strategies. One of the tools that is widely used to support evidence-based decisions is modeling and simulation. In this paper, we present a hybrid agent-based and discrete-event simulation for the Coronavirus pandemic management at regional level. Our model considers disease dynamics, population interactions and dynamic ICU bed capacity management and predicts the impact of various public health preventive measures on the population and the healthcare service.

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Performance Analysis of Fixed Broadband Wireless Access in mmWave Band in 5G

arXiv

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arXiv 2023年

作者： Banerjee, Soumya Gochhayat, Sarada Prasad Shetty, Sachin Virginia Modeling Analysis and Simulation Center Old Dominion University NorfolkVA United States Department of Electrical and Computer Engineering Villanova University VillanovaPA United States

An end-to-end fiber-based network has the potential to offer multi-gigabit fixed access to end-users. However, a fiber network’s biggest hurdle is delivering that fiber access to the end-user. Especially in places where fiber is non-existent, it can be time-consuming and costly to deploy, resulting in Operators experiencing a long delay in realizing a return on their investment. This work investigates transmission data from fixed broadband wireless Access in mmWave band in 5G. With the increasing interest in this domain, it is worth investigating the transmission characteristic of the data and utilizing the same to build more sophisticated capabilities. Existing datasets for mmWave band are detailed but generated from simulated environments. In this work, we introduce a dataset built from the collection of real-world transmission data from Fixed Broadband Wireless Access in mmWave Band device(RWM6050). The goal of this data is to enable self-configuration capability based on transmission characteristics. Towards this goal, we present an online machine learning-based approach that can classify transmission characteristics with real-time training. We also present two more advanced temporal models for more accurate classifications. We demonstrate that it is possible to detect the transmission angle and distance directly from the analysis of transmission data with very high accuracy. We achieved up to 99% accuracy on the combined classification task. Finally, we outline some interesting future research scopes based on the collected data. © 2023, CC BY-NC-ND.

关键词： Fibers

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Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid

Trustworthy Artificial Intelligence Framework for Proactive ...

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simulation Winter Conference

作者： Md. Shirajum Munir Sachin Shetty Danda B. Rawat Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA Department of Electrical and Computer Science Howard University Washington D.C. USA

The rapid growth of distributed energy resources (DERs), such as renewable energy sources, generators, consumers, and prosumers in the smart grid infrastructure, poses significant cybersecurity and trust challenges to the grid controller. Consequently, it is crucial to identify adversarial tactics and measure the strength of the attacker’s DER. To enable a trustworthy smart grid controller, this work investigates a trustworthy artificial intelligence (AI) mechanism for proactive identification and explanation of the cyber risk caused by the control/status message of DERs. Thus, proposing and developing a trustworthy AI framework to facilitate the deployment of any AI algorithms for detecting potential cyber threats and analyzing root causes based on Shapley value interpretation while dynamically quantifying the risk of an attack based on Ward’s minimum variance formula. The experiment with a state-of-the-art dataset establishes the proposed framework as a trustworthy AI by fulfilling the capabilities of reliability, fairness, explainability, transparency, reproducibility, and accountability.

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EXPLORING XENOPHOBIC EVENTS THROUGH GDELT DATA ANALYSIS

arXiv

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arXiv 2023年

作者： Jayanetti, Himarsha R. Frydenlund, Erika Weigle, Michele C. Department of Computer Science Old Dominion University NorfolkVA United States Virginia Modeling Analysis and Simulation Center Old Dominion University SuffolkVA United States

This study explores xenophobic events related to refugees and migration using the GDELT 2.0 database and APIs through visualizations. We conducted two case studies – the first being an analysis of refugee-related news following the death of a two-year-old Syrian boy, Alan Kurdi, and the second a surge in news articles in March 2021 based on the data obtained from GDELT API. In addition to the two case studies, we present a discussion of our exploratory data analysis steps and the challenges encountered while working with GDELT data and its tools. © 2023, CC BY-NC-SA.

关键词： Big data

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Achieving Runtime State Verification Assurance in Critical Cyber-Physical Infrastructures

Achieving Runtime State Verification Assurance in Critical C...

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2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2021

作者： Rivera, Abel O. Gomez Tosh, Deepak K. Shetty, Sachin University of Texas at El Paso Department of Computer Science TX United States Virginia Modeling Analysis and Simulation Center Old Dominion University TX United States

ISBN: (纸本)9781665415026

Industrial Cyber-Physical Systems (ICPS) are an essential backbone of national critical infrastructures. They help monitor and control crucial cyber-enabled services such as energy generation. Commonly ICPS monitors the physical process through Supervisory Control and Data Acquisition (SCADA) systems. The SCADA ecosystem takes critical real-time and future system operational decisions based on the runtime state behavior of field sensors. Traditional SCADA systems use legacy and insecure communication protocols such as the Modbus protocol that lack adequate security mechanisms to provide robust runtime state behavior assurance of constrained field sensors. Therefore, constrained field sensors are commonly vulnerable to standard semantic attacks that gradually change the behavior state of infected devices. This paper discusses process integrity assurance techniques necessary to enhance the security of behavior-based protocols such as the Modbus protocol. The Runtime State Verification (RSV) protocol proposed in this paper aims to address semantic attacks in the SCADA ecosystem by integrating behavior-based Mandatory Results Automata (MRA) and a Hyperledger Fabric (HLF) network. The RSV protocol provides high process integrity assurance through enhanced behavior-based MRA suitable for the constrained field devices. A proof of concept of the RSV protocol has been evaluated in an emulated water-tube boiler. Preliminary evaluations of the RSV protocol aimed to measure the efficiency of the proposed protocol by monitoring an Combustion Efficiency (CE) process necessary to preserve optimal combustion, thus minimizing costs and future maintenance of water-tube boilers. We analyze the overall network overhead and latency of the proposed RSV protocol by evaluating the HLF network performance and comparing the proposed RSV protocol with the state-of-art BloSPAI protocol. Through the preliminary evaluations of the proposed RSV protocol, this paper demonstrates that the proposed

关键词： Water tube boilers

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EM-PAD: An Effective Mechanism for Phishing Attack Detection

EM-PAD: An Effective Mechanism for Phishing Attack Detection

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Aakash Saksham Mittal Mohammad Wazid Amit Kumar Mishra Ashok Kumar Das Sachin Shetty Mohsen Guizani Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computational Modeling and Simulation Engineering Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

In the present era, the increasing number of network devices and ubiquitous computing leads to the flow of enormous amounts of data traffic, including sensitive and confidential information, on the internet and carrying out commercial and banking transactions online. This gives cybercriminals an opportunity to launch an attack like phishing to steal confidential information from the user and gain unauthorized access. This can be mitigated by the help of an intelligent machine learning-based phishing detection system, which can detect potential phishing attacks and take appropriate action. In this paper, we have addressed this major cyber issue and proposed a machine learning-based phishing detection scheme (in short, EM-PAD), which is trained on a benchmark dataset and evaluated on standard metrics: F1-score and Accuracy. The proposed model is compared with different existing schemes based on Accuracy, indicating that it has outperformed them with remarkable results.

关键词： Wireless communication Measurement Accuracy Phishing Computational modeling Banking Benchmark testing

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