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Securing Healthcare Data in IoMT Network Using Enhanced Chaos Based Substitution and Diffusion

computer Systems Science & engineering 2023年第11期47卷 2361-2380页

作者： Musheer Ahmad Reem Ibrahim Alkanhel Naglaa FSoliman Abeer D.Algarni Fathi E.Abd El-Samie Walid El-Shafai Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia

Patient privacy and data protection have been crucial concerns in Ehealthcare systems for many *** modern-day applications,patient data usually holds clinical imagery,records,and other medical ***,the Internet of Medical Things(IoMT),equipped with cloud computing,has come out to be a beneficial paradigm in the healthcare ***,the openness of networks and systems leads to security threats and illegal ***,reliable,fast,and robust security methods need to be developed to ensure the safe exchange of healthcare data generated from various image sensing and other IoMT-driven devices in the IoMT *** paper presents an image protection scheme for healthcare applications to protect patients’medical image data exchanged in IoMT *** proposed security scheme depends on an enhanced 2D discrete chaotic map and allows dynamic substitution based on an optimized highly-nonlinear S-box and diffusion to gain an excellent security *** optimized S-box has an excellent nonlinearity score of *** new image protection scheme is efficient enough to exhibit correlation values less than 0.0022,entropy values higher than 7.999,and NPCR values around 99.6%.To reveal the efficacy of the scheme,several comparison studies are *** comparison studies reveal that the novel protection scheme is robust,efficient,and capable of securing healthcare imagery in IoMT systems.

关键词： Secure communication healthcare data encryption Internet of Medical Things(IoMT) discrete chaotic map substitution box(S-box)

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Comparative Analysis of Machine Learning Algorithms and Datasets for Detecting cyberbullying on Social Media Platforms

Comparative Analysis of Machine Learning Algorithms and Data...

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Computing and Intelligent Reality Technologies (ICCIRT), International Conference on

作者： Anusha Palagati Santhosh Kumar Balan S Arun Joe Babulo Laxmi Raja K.K. Natarajan Rajkumar Kalimuthu Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Dept. of Computer Science and Engineering SMIEEE Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications K.S.R College of Arts and Science for Women Tiruchengode Tamilnadu India Department of Cyber Security Karpagam Academy of Higher Education Coimbatore Tamilnadu India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Sultanate of Oman Department of Cyber Security Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India

ISBN: (数字)9798331510299

ISBN: (纸本)9798331510305

cyberbullying is defined as sharing of undesirable material in the cyberspace, primarily- the use of the social sites that instigate hostility and create feelings of hatred among the individuals. Another increasing threat due to increase use of internet-based communication is cyberbullying especially on the site such as Twitter. Different ML and NLP approaches including support vector machine (SVM), Naϊve bayes, Random Forest, and logistic regression is employed to detect cyberbul Bullivating. These methods for supervised learning mine patterns in text and image, allowing computers to promptly flag risky interactions. Some of these approaches also utilizes OCR for the identification of image-based bullying. Consequently, the objective of this research is to identify the correct algorithm and dataset for improving the identification of cyberbullying and solving the problem that manifests itself in the steady increase in cases of this type of bullying.

关键词： Support vector machines computers Logistic regression Machine learning algorithms Supervised learning Optical character recognition cyberbullying Bayes methods Object recognition Random forests

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Empowering Smart Homes with Fog Computing for IoT Connectivity

Empowering Smart Homes with Fog Computing for IoT Connectivi...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Amjed Abbas Ahmed Ali Hamza Najim Ali Jafer Mahdi Khattab M. Ali Alheeti Nurhizam Safie Mohd Satar Aisha Hassan Abdallah Hashim Faculty of Information Science and Technology Center for Cyber Security Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Imam AIKadhum College (IKC) Baghdad Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Al-Diwaniyah Iraq Department of Scientific Affairs AI-Zahraa University for Women Bangi Malaysia Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Innovative grid technology leverages Information and Communication technology (ICT) to enhance energy efficiency and mitigate losses. This paper introduces a “novel three-tier hierarchical framework for smart homes within the Internet of Things (IoT)” ecosystem, addressing the current shortage of intelligent solutions that underutilize IoT advantages. Our framework's primary objective is to elevate smart homes to the microgrid level, enabling the seamless integration from the microgrid's renewable distributed energy sources and attaining maximum energy efficiency. In addition to traditional data processing, we propose the application of fog computing in smart homes. Through a rigorous simulation using real smart meter datasets, our study demonstrates that fog computing, mainly when using predictive filtering, may drastically cut down on the amount of data being sent and the load on the home's network. This innovation holds immense potential for revolutionizing energy management and resource allocation in the context of smart homes, thus contributing to a sustainable and efficient future. This research represents a critical step towards harnessing the power of IoT and fog computing in smart grids and homes, offering a promising path towards more intelligent, eco-friendly, and energy-efficient living spaces.

关键词： Renewable energy sources Smart homes Microgrids Data processing Energy efficiency Smart grids Information and communication technology Internet of Things Optimization Edge computing

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Using AI and Natural Language Processing to Enhance Consumer Banking Decision-Making

Using AI and Natural Language Processing to Enhance Consumer...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Temara, Sheetal Samanthapudi, Sagar Varma Rohella, Piyush Gupta, Ketan Ashokkumar, S.R. University of The Cumberlands Dept of Computer & Information Sciences WilliamsburgKY United States The Polis Center Indiana University Purdue University Indianapolis Applied Data Science IndianapolisIN United States Payments & Commerce GoDaddy EdmondsWA United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Centre For Blockchain and Cyber Security Department of Computer and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350394399

This technical summary outlines the use of Artificial Intelligence (AI) and Natural Language Processing (NLP) to beautify patron banking decision-making. AI-based banking systems can autonomously identify patterns in consumer information and generate insights from a number of sources, including but not restricted to purchaser 360 profiles, 1/3-party statistics, and transactional statistics. With the assistance of AI and NLP, this gadget can answer customer questions relating to their banking wishes and offer personalized recommendations. AI-based monetary decision-making systems also permit banks to offer computerized customer service with information on clients' needs, respond speedily to purchaser requests, and offer quick acclaim for loans and other banking merchandise. Moreover, AI and NLP enhance patron engagement via chatbot-like solutions and purchaser sentiment analysis. The extraordinary AI and NLP-based services being presented by using banks are mentioned in this technical abstract. In conclusion, AI and NLP technology are giving banks the capacity to meet the desires of their customers and improve typical purchaser pleasure. © 2024 IEEE.

关键词： Artificial intelligence

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New Coefficient Grouping for Complex Affine Layers

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Chinese Journal of Electronics 2025年第2期34卷 520-532页

作者： Qiao Wenxiao Sun Siwei Chen Ying Hu Lei Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Chinese Academy of Sciences Beijing 100085 China School of Cyber Security University of Chinese Academy of Sciences Beijing 100049 China School of Cryptology University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Cryptology Beijing 100878 China Department of Cryptology Science & Technology Beijing Electronic Science & Technology Institute Beijing 100070 China

Recently, designing symmetric primitives for applications in cryptographic protocols including multi-party computation, fully homomorphic encryption, and zero-knowledge proofs has become an important research topic. Among many such new symmetric schemes, a power function over a large finite field \begin{document}$ \mathbb{F}_q $\end{document} is commonly used. In this paper, we revisit the algebraic degree’s growth for a substitution-permutation network (SPN) cipher over \begin{document}$ \mathbb{F}_{2^n} $\end{document}\begin{document}$ (n\ge3) $\end{document}, whose S-box is defined as a composition of a power function \begin{document}$ P(x)=x^{2^d+1} $\end{document} where \begin{document}$ d\ge1 $\end{document} with a polynomial \begin{document}$ A(x)=a_0+ $\end{document}\begin{document}$ \sum\limits_{w=1}^Wa_wx^{2^{\beta_w}} $\end{document} where \begin{document}$ a_i\in\mathbb{F}_{2^n} $\end{document} for \begin{document}$ 0\le i\le W $\end{document} and \begin{document}$ a_w\neq0 $\end{document} for \begin{document}$ 1\le w\le W $\end{document}. We propose a new coefficient grouping technique, which is based on our new description of the monomials that will probably appear in the state. Specifically, we propose a new measure to find proper \begin{document}$ (\beta_1,\beta_2,\dots,\beta_W) $\end{document} for the algebraic degree’s fastest growth and a new method to compute the algebraic degree’s upper bound for arbitrary \begin{document}$ A(x) $\end{document}. Especially for Chaghri, which was presented at ACM CCS 2022, we obtained a tighter upper bound on the algebraic degree.

关键词： Binary extension field Algebraic degree Coefficient grouping technique

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An Adaptive User Service Deployment Strategy for Mobile Edge Computing

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China Communications 2022年第10期19卷 238-249页

作者： Gang Li Jingbo Miao Zihou Wang Yanni Han Hongyan Tan Yanwei Liu Kun Zhai Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China School of Cyber Security University of Chinese Academy of SciencesBeijing 101408China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029China Sunwise Intelligent Technology Company Beijing 100190China

Mobile edge computing(MEC) is a cloud server running at the edge of a mobile network, which can effectively reduce network communication delay. However, due to the numerous edge servers and devices in the MEC, there may be multiple servers and devices that can provide services to the same user simultaneously. This paper proposes a userside adaptive user service deployment algorithm ASD(Adaptive Service Deployment) based on reinforcement learning algorithms. Without relying on complex system information, it can master only a few tasks and users. In the case of attributes, perform effective service deployment decisions, analyze and redefine the key parameters of existing algorithms, and dynamically adjust strategies according to task types and available node types to optimize user experience delay. Experiments show that the ASD algorithm can implement user-side decision-making for service deployment. While effectively improving parameter settings in the traditional Multi-Armed Bandit algorithm,it can reduce user-perceived delay and enhance service quality compared with other strategies.

关键词： edge computing adaptive algorithm reinforcement learning computing unloading service deployment

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AntiMal: an Approach of Malware Detection Employing Swin Transformer 23

AntiMal: an Approach of Malware Detection Employing Swin Tra...

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Proceedings of the 2023 13th International Conference on Communication and Network security

作者： Chenxi Lyu Yinan Yao Xiaodong Guo Zihong Huang Chen Dong Yuanyuan Zhang Zhenyi Chen College of Computer and Data Science Fuzhou University China College of Computer and Cyber Security Fujian Normal University China Department of Computer Science and Engineering University of South Florida USA

ISBN: (纸本)9798400707964

In the present era, the menace of malicious software is growing continuously, posing a significant challenge in the realm of cybersecurity. Currently, traditional machine learning algorithms have been widely applied to the analysis of malicious software and are considered effective methods. However, these approaches often require extensive feature engineering, feature learning, and feature representation, which can be time-consuming and resource-intensive. In this paper, a method for the identification of malicious software, named "AntiMal," is proposed. This method utilizes the Swin Transformer as the backbone network and combines it with image features for static analysis. It leverages the dataset provided by Microsoft for the Kaggle Microsoft Malware Classification Challenge. Initially, it converts the binary data of malicious code into grayscale images, treating every 8 bits as a pixel. Subsequently, it employs PyTorch to build and train the Swin Transformer, classifying the images in the validation set. The results demonstrate that this approach excels in the problem of malicious software classification, achieving an impressive accuracy of up to 95%, showcasing remarkable precision and generalization capabilities. Furthermore, it exhibits significant advantages when dealing with large datasets, requiring fewer computational resources, thus offering exceptional computational efficiency.

关键词： cybersecurity Deep learning Malware Detection Swin Transformer

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A review of DNA cryptograhic approaches 2

A review of DNA cryptograhic approaches

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2nd IEEE International Conference on cyberspace, cyber NIGERIA 2020

作者： Iliyasu, Mohammed Awwal Abisoye, Opeyemi Aderiike Bashir, Sulaimon Adebayo Ojeniyi, Joseph Adebayo Department of Computer Science Federal University of Technology Minna Nigeria Department of Cyber Security Federal University of Technology Minna Nigeria

ISBN: (纸本)9781665444095

Cryptography is described as the encryption analysis of data or secret data writing using logical and mathematical data protection principles. It is an information technology for banking, medical systems, transportation and other Internet of things applications. Cryptography has become more important, and it is subjected to growing security concerns. Each system is built with its own strength in cryptography;symmetric encryption provides an economical data protection solution without compromise but it is important to share or distribute the secret key during encryption and decryption process. In comparison, the asymmetric encryption addresses the issue of secret key distribution;however, the stand-alone technique is slow and needs more computing resources than the symmetric encryption approach. In this context, a study of papers relating to DNA cryptographic approaches are presented and the research was centered from 2015 to 2020. The primary sources of information are Science Direct and Research Gate publication platforms. The existing shortcomings of DNA cryptographic approaches were established and analysis was performed on the most frequently used encryption technique based on the literature. The significant findings of this research reviewed that DNA digital coding is the most adopted cryptographic technique used to improve information security and the most common limitations of the DNA cryptographic approaches are high time complexity and algorithm complexity which is possible to infer from the literature. © 2021 IEEE.

关键词： DNA

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An overview Scientific Cloud computing Web-based cloud services issues and challenges 23

An overview Scientific Cloud computing Web-based cloud servi...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Sayan Majumder Kommisetti Murthy Raju Nancy Arya Nazeer Shaik Sanjay Tiwari Akanksha Singh Department of cyber security The Heritage Academy India Department of Electronics and Communication Engineering Shri Vishnu Engineering College For Women India Department of Computer Science and Engineering Shree Guru Gobind Singh Tricentenary University India Department Of Computer Science and Engineering Srinivasa Ramanujan Institute Of Technology-- Autonomous- India Department of Computer Science and Engineering ARYA Institute of Engineering Technology and Management India Computer Science and Engineering Babu Banarasi Das University India

ISBN: (纸本)9798400709418

This research investigates the crucial role of cloud computing services in web development for creating scalable applications. Our study explores the benefits, challenges, and best practices associated with leveraging these services. Real-world case studies will be examined to showcase their significance. Additionally, we examine the integration of blockchain technologies in cloud-based web development and their potential impact on scalability. This paper presents a comprehensive analysis of the indispensability of cloud computing services in building scalable applications for the web. This research project delves into the transformative impact of cloud computing services on the field of web development. By providing scalable and flexible infrastructure, these services have revolutionized the way applications are built. Through an in-depth analysis of benefits, challenges, and best practices, along with real- world case studies, the project highlights the indispensable nature of cloud computing in creating scalable web applications. Furthermore, the exploration of blockchain integration within cloud-based web development offers insights into potential advancements in scalability and opens new avenues for innovation in the field. Additionally, the integration of blockchain technologies in cloud-based web development is investigated, providing insights into its potential impact on scalability and fostering innovation in the field. The research presents a comprehensive analysis of the indispensability of cloud computing services in creating scalable web applications and highlights their transformative impact on the development process.

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An Enhanced Product Quality Evaluation using Hybrid LSTM-FCNN Model and GWO Algorithm for Social Media Sentiment Analysis

An Enhanced Product Quality Evaluation using Hybrid LSTM-FCN...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： V. S. Raj Kumar T. Kumaresan P. Rajesh Kanna S. Jagadeesan P. Showmiya P. Nithin Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Computer Science – Cyber Security Nehru Institute of Technology Coimbatore India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Sentiment analysis is still in its developing era, and sometimes, struggles are faced in evaluating user sentiment due to the use of traditional models to analyze the relationship structures in social media interactions. To tackle the issue, we proposed an innovative approach Hybrid LSTM neural network and a fully connected neural network (FCNN) integrated with the Grey Wolf Optimization (GWO) algorithm. The objective of this proposed method is to improve sentiment analysis by employing the LSTM to capture the sequence of data and improve the ability of FCNNs to improve feature extraction and the analysis process. The proposed system process initiated with the data preprocessing of input Twitter sentiment dataset transformed into a suitable input to the Hybrid LSTM-FCNN neural network. Then, temporal dependency of the data captured by LSTM and FCNN is used to extract the features. The GWO method mimics the grey wolf hunting behaviour, for optimizing the proposed Hybrid LSTM-FCNN model’s parameters to enhance the classification accuracy. The system models start with the initialization of the model, parameters, and training the models using the dataset to reduce the cross-entropy loss, estimating the testing dataset from unseen data and fine-tuning using GWO as necessary. Results demonstrate significant improvements in sentiment categorization compared to traditional methods. The Hybrid LSTM-FCNN model, enhanced with GWO, effectively captures nuanced sentiment patterns, leading to more accurate classification. Additionally, GWO’s computational efficiency contributes to scalability. In conclusion, our proposed approach offers a promising solution for enhancing product quality evaluation through advanced neural network architectures and optimization algorithms applied to social media sentiment analysis.

关键词： Sentiment analysis Analytical models Accuracy Social networking (online) Neural networks Blogs Feature extraction Data models Optimization Long short term memory

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