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Data Analytic for Healthcare cyber Physical System

IEEE Transactions on Network Science and engineering 2023年第5期10卷 2490-2502页

作者： Gan, Wensheng Hu, Kaixia Huang, Gengsen Chien, Wei-Che Chao, Han-Chieh Meng, Weizhi Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien97401 Taiwan National Dong Hwa University Department of Electrical Engineering Hualien97401 Taiwan UCSI University Institute of Computer Science and Innovation Kuala Lumpur Malaysia Department of Applied Mathematics and Computer Science Cyber Security Section Lyngby2800 Denmark

Nowadays, a large number of AI-powered healthcare cyber-physical systems (CPSs) have been used in healthcare services. In order to provide better care, AI-powered healthcare CPSs analyze the data they collect using a variety of techniques. Data analysis for artificial intelligence (AI)-driven healthcare CPS is one of these approaches. However, none of the techniques in data analysis can provide a good representation of contiguous and negative information. Therefore, we are the first to introduce the problem of contiguous negative sequential pattern mining. A novel algorithm called Contiguous Negative Sequential Pattern Miner (CNSPM) is proposed to discover and analyze contiguous negative sequential patterns (CNSPs) from the data collected by healthcare CPSs. Finally, we select some real medical and non-medical datasets to conduct numerous experiments. We further analyze the discovered patterns and show how healthcare services can use meaningful patterns for medical decision-making. The performance results on these datasets demonstrate that the proposed algorithm can discover more valuable patterns efficiently and effectively from the collected and transformed medical data. © 2013 IEEE.

关键词： Data mining

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Adapting to Evasive Tactics through Resilient Adversarial Machine Learning for Malware Detection 18

Adapting to Evasive Tactics through Resilient Adversarial Ma...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Krishna, G. Bala Sravan Kumar, G. Ramachandra, Mummadi Sampurnima Pattem, K. Rani, D. Sandhya Kakarla, Geeta Cvr College of Engineering Department of Computer Science Engg. Rangareddy Ibrahimpatnam India Cvr College of Engineering Department of Computer Science Engg.-Cyber Security Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Hyderabad India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Cyber Security Hyderabad India

ISBN: (纸本)9789380544519

This paper presents the Adaptive Resilience-based Convolutional Network (ARCNet), a sophisticated machine learning framework specifically designed to detect advanced, evasive malware. ARCNet combines convolutional and recurrent neural networks, making it highly adaptable to changing cyber threats. Its core components, the Adversarial Learning Module (ALM), Predictive Analysis Engine (PAE), and Dynamic Adaptation System (DAS), significantly boost its detection power. Tests using a synthetic dataset show ARCNet's superiority over traditional models like the Support Vector Machine (SVM). It achieved 95.2% accuracy under normal conditions (compared to SVM's 89.4%) and maintained 92.5% accuracy even during adversarial attacks (against SVM's 80.3%). Notably, ARCNet's detection rates improved from 78.5% to 86.7% in five months after integrating the DAS. These results confirm ARCNet's efficiency in tackling complex malware challenges, contributing greatly to cybersecurity. The study underscores the importance of evolving and enhancing machine learning methods to keep pace with the rapidly changing landscape of cyber threats. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Block chain Blockchains Challenges of Block chain Consensus algorithm Data privacy Digital signatures Distributed ledger Faces Organizations Smart contracts

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Advancing Stance Detection of Political Fan Pages: A Multimodal Approach 24

Advancing Stance Detection of Political Fan Pages: A Multimo...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Kuo, Kuan-Hung Wang, Ming-Hung Kao, Hung-Yu Dai, Yu-Chen National Chung Cheng University Department of Computer Science and Information Engineering Chiayi Taiwan National Institute of Cyber Security Taipei Taiwan

ISBN: (纸本)9798400701726

The evolution of political campaigns is evident with the ascent of social media. Ideological beliefs are increasingly disseminated through political-affiliated fan pages. The interaction between politicians and the general public on these platforms plays a pivotal role in election outcomes. In this study, we utilize a multimodal approach to explore and quantify similarities of ideologies among political fan pages. we employed visualization techniques to demonstrate the political stance of each fan page. To validate our proposal, we concentrated on an analysis of the 2021 national referendums in Taiwan, encompassing a collection of fan pages and their corresponding posts that were related to these referendums. Through a qualitative analysis of the content of these fan pages, the efficacy of our multimodal framework in clustering fan pages according to their respective political ideologies was evaluated. The findings of this study underscore the significant enhancement in the accuracy of stance detection when integrating multiple modalities of data, namely textual content, visual imagery, and user interactions. © 2024 Copyright held by the owner/author(s).

关键词： Image enhancement

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Machine Learning for Smart Agriculture: A Comprehensive Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2568-2588页

作者： Mahmood, M. Rezwanul Matin, Mohammad Abdul Goudos, Sotirios K. Karagiannidis, George North South University Department of Electrical and Computer Engineering Dhaka1229 Bangladesh Aristotle University of Thessaloniki ELEDIA@AUTH Department of Physics Thessaloniki54124 Greece Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Cyber Security Systems and Applied AI Research Center Beirut1102 2801 Lebanon

As communication technologies and equipment evolve, smart assets become smarter. The agricultural industry is also evolving in line with the implementation of modern communication protocols, intelligent sensors, and equipment. This evolution is enabling large-scale agricultural production processes to operate independently, thus, securing the food supply chain for an ever-growing population. Data processing for such a system with multiple heterogeneous sources requires proper management for effective agricultural operations. Recognizing the advantages of machine learning (ML) in performing large-scale data processing, researchers are investigating the implementation of ML to design an effective intelligent agricultural architecture. The aim of this article is to provide a thorough analysis of the state-of-the-art in smart agriculture, open challenges, and guidelines for the development of further enhanced smart agriculture systems. Specifically, we describe how ML is used to create intelligent agricultural systems supported by state-of-the-art technology. © 2020 IEEE.

关键词： Weed control

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A MULTIMODAL FRAMEWORK FOR SATIRE VS. SARCASM DETECTION 15

A MULTIMODAL FRAMEWORK FOR SATIRE VS. SARCASM DETECTION

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Goyal, Devansh Mishra, Santosh Kumar Rai, Vikash Kumar Department of Computer Science and Engineering RGIPT Jais Amethi India School of Cyber Security and Digital Forensics NFSU Goa Goa India

ISBN: (纸本)9798350370249

Sarcasm and satire are common ways to communicate complicated ideas with humor and irony, both expressing criticism or mockery. Detecting sarcasm vs. satire in real life can foster better communication by understanding intent, preventing misunderstandings, and promoting critical thinking and empathy. We introduce an innovative dataset for detecting Multi-modal Satire vs. Sarcasm, leveraging the MUStARD dataset for its construction. This dataset includes visual utterances annotated with sarcasm and satire labels, enriching its potential for studying multimodal satire and sarcasm detection. This enriched dataset serves as a robust foundation for training and evaluating machine learning and deep learning architectures that combine textual and visual cues to identify satire and sarcasm in multimedia content. By incorporating this enhanced dataset, the research aims to advance the field of multimodal satire and sarcasm detection and contribute to a deeper understanding of humor and irony in online communication. We carry out experimentation with various architectures like LSTM, GPT2, BERT, SVM, and Logistic Regression. The proposed architecture achieves an F1 score of 0.956, outperforming other baselines. © 2024 IEEE.

关键词： Contrastive Learning

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security, Trust, and Privacy in cyber-physical Systems (CPS) 2

Security, Trust, and Privacy in Cyber-physical Systems (CPS)

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2nd International Conference on cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2024

作者： Busari, Wareez A. Bello, Abeeb A. Department of Cyber Security Science Federal University of Technology Niger State Minna Nigeria Department of Electrical and Computer Engineering Mahindra University Hyderabad India

ISBN: (纸本)9798350388664

This paper explores the convergence of physical and computational components in cyber-Physical Systems (CPS). security, trust, and privacy are paramount for reliable and resilient operation of these interconnected systems across diverse sectors like healthcare and smart cities. The paper analyses the complex interplay of these factors, identifying practical challenges and proposing solutions to enhance system robustness. By analysing trends, research gaps, and offering actionable recommendations, the study aims to empower stakeholders in fortifying CPS against evolving cyber threats. It emphasizes the need for comprehensive strategies to mitigate risks, protect sensitive information, and ensure data integrity and system reliability. Through a holistic approach integrating security, trust, and privacy mechanisms, the research contributes to advancing effective security measures in CPS, ultimately fostering a culture of trust and resilience in the digital era. © 2024 IEEE.

关键词： Differential privacy

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Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis

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SN computer Science 2024年第6期5卷 756页

作者： Ganesh, V.N. Gulappagol, Laxmi Pushparani, M.K. Ganesh, K. Department of Electronics and Communication Engineering Alvas Institute of Engineering and Technology Department of Internet of Things and Cyber Security including Block chain Technology A J Institute of Engineering and Technology Department of Computer Science & amp Design Alvas Institute of Engineering and Technology

Early and accurate diagnosis of melanoma, a potentially life-threatening skin cancer, is crucial for improving patient outcomes. In this study, we propose a novel approach for melanoma detection, termed Panoptic Region Slicing Segmentation (PRS2) using an optimized convolution neural network (PRS2-OCNN) based on AlexNet. The proposed system integrates several advanced methods to enhance the accuracy and efficiency of melanoma identification. The initial step involves preprocessing the dermoscopic image using a 2D Fusion Filter, which enhances the image quality and prepares it for subsequent analysis. Next, the Panoptic Region Slicing Segmentation (PRS2) method is applied to emphasize the boundary regions, allowing for precise localization of melanoma-affected areas. To assess color variations within the segmented regions, we employ Threshold Histogram Evaluation (THE), which effectively characterizes melanoma-specific color patterns. The features extracted using Spread Spectral Menzies’s Feature Selection (SSMFS), reducing the dimensionality and improving the efficiency of the subsequent analysis. The core of our proposed approach lies in the optimized convolution neural network, derived from the influential AlexNet architecture. By fine-tuning the AlexNet-OCNN on the reduced feature set, we maximize its ability to accurately classify melanoma lesions based on their risk level. The PRS2-OCNN identifies melanoma classes according to their risk severity, aiding dermatologists in making informed decisions for timely and appropriate *** evaluations were conducted on a diverse and extensive dataset of dermoscopic images. The proposed system demonstrated superior performance compared to existing methods, exhibiting heightened detection accuracy by deeply analyzing the melanoma-affected regions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 2D fusion filter AlexNet Early diagnosis Melanoma Panoptic region slicing segmentation PRS2-OCNN

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Research on Metaverse security and Forensics

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computers, Materials & Continua 2023年第10期77卷 799-825页

作者： Guangjun Liang Jianfang Xin Qun Wang Xueli Ni Xiangmin Guo Pu Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina Engineering Research Center of Electronic Data Forensics Analysis NanjingChina Key Laboratory of Digital Forensics Department of Public Security of Jiangsu ProvinceNanjingChina School of Intelligent Engineering Nanjing Institute of Railway TechnologyNanjingChina

As a subversive concept,the metaverse has recently attracted widespread attention around the world and has set off a wave of enthusiasm in academic,industrial,and investment ***,while the metaverse brings unprecedented opportunities for transformation to human society,it also contains related *** is a digital living space with information infrastructure,interoperability system,content production system,and value settlement system as the underlying structure in which the inner core is to connect real residents through applications and *** social incentives and governance rules,the metaverse reflects the digital migration of human *** article will conduct an in-depth analysis of the metaverse from the perspective of electronic data ***,from the perspective of Internet development,the background and development process of the metaverse is *** systematically elaborating on the concept and connotation of the metaverse,this paper summarizes the different views of current practitioners,experts,and scholars on the ***,from the perspective of metaverse security,the social risk and crime risks of the metaverse are *** the importance of metaverse forensics is ***,from the perspective of blockchain,smart wearable devices,and virtual reality devices,the objects and characteristics of metaverse forensics have been studied in *** smart wearable devices as an example,this paper gives the relevant experimental process of smart bracelet ***,many challenges faced by metaverse forensics are summarized by us which provide readers with some exploratory guidance.

关键词： Metaverse forensics blockchain smart wear virtual reality

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Maturity Model for Corporate Sector Based on Zero Trust Adoption 10

Maturity Model for Corporate Sector Based on Zero Trust Adop...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Ilyas, Muhammad Akal, Mustafa Althebyan, Qutaibah Al Ain University Department of Cyber Security Abu Dhabi United Arab Emirates Altinbas University Department of Electrical and Computer Engineering Istanbul Turkey

ISBN: (纸本)9798331532895

The rapid evolution of cybersecurity threats necessitates the adoption of robust security frameworks. One such approach gaining significant attention is the concept of zero trust, which emphasizes continuous and strong verification, strict access controls and identity management, and well-planned strategy and assessment to mitigate risks. However, organizations often face challenges in effectively implementing and assessing their progress in zero trust adoption. We propose a maturity model for zero trust adoption, designed to assist organizations in evaluating their current security posture, identifying gaps, and developing a roadmap for achieving higher levels of zero trust maturity. The model encompasses various dimensions, including network segmentation, access controls, data protection, and incident response. Additionally, a comprehensive set of self-assessment queries is provided to enable organizations to gauge their progress and identify areas for improvement. Through the utilization of this maturity model and self-assessment framework, organizations can enhance their understanding of zero trust principles, align their security strategies, and prioritize necessary investments to strengthen their overall security posture. © 2024 IEEE.

关键词： cybersecurity self-assessment framework security posture zero trust adoption zero trust approach zero trust maturity model

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Deployment of Co-operative Farming Ecosystems Using Blockchain 22nd

Deployment of Co-operative Farming Ecosystems Using Blockcha...

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on Information Assurance and security, IAS 2022

作者： Mahapatra, Aishwarya Gupta, Pranav Swarnkar, Latika Gupta, Deeya Kar, Jayaprakash Centre for Cryptography Cyber Security and Digital Forensics Department of Computer Science & Engineering Department of Communication & Computer Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9783031274084

Blockchain has helped us in designing and developing decentralised distributed systems. This, in turn, has proved to be quite beneficial for various industries grappling with problems regarding a centralised system. So, we thought of exploring blockchain’s feasibility in the agricultural industry. India is a country where a large part of the population is still dependent on agriculture, however, there’s no proper system in use as yet that can help the farmers get the right price for their farm products and help the consumers get an affordable price for their ***, we propose a blockchain based decentralized marketplace where we will implement a collaborative model between farmers and consumers. This model will allow the farmers to record their potential crops and the expected output on decentralised ledger besides, enabling them to showcase their integrity and credibility to consumers. The consumers, on the other hand, can actually check everything about the farmers with the help of their information based on the previous supplies. This open and full proof digital market framework will thus reduce black marketing, hoarding, adulteration, etc. In this research paper, we have explored one possible blockchain model by creating a breach proof ledger of records. We have used solidity and ethereum frameworks for the working of the model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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