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A Multi-Model Framework for Synthesizing High-Fidelity Network Intrusion data Using Generative AI 5

A Multi-Model Framework for Synthesizing High-Fidelity Netwo...

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5th International conference on Advanced Research in Computing, ICARC 2025

作者： Rathakrishnan, Mathushaharan Gayan, Samiru Edirisinghe, Sampath Inaltekin, Hazer University of Moratuwa Dept. of Electronic and Telecommunication Engineering Moratuwa Sri Lanka University of Sri Jayewardenepura Dept. of Computer Engineering Nugegoda Sri Lanka Macquarie University School of Engineering North RydeNSW2109 Australia

ISBN: (纸本)9798331530983

the rapid evolution of network technologies and frequent network intrusions have amplified the demand for intrusion detection systems (IDSs) powered by artificial intelligence (AI). However, the performance of IDSs is limited by labeled intrusion data and class imbalances in existing datasets. Collecting such data through live network simulations is costly, insecure, and poses regulatory challenges. this paper presents a multi-model framework that uses a conditional tabular generative adversarial network (CTGAN) to synthesize realistic network intrusion data. the framework addresses challenges such as unstable training, mode collapse, and handling mixed data types, which are common in most generative AI models when applied to non-Gaussian tabular data generation while validating the synthesized data through qualitative and quantitative analyses. Overlaid density histograms and cumulative distribution functions (CDFs) are used for qualitative analysis. For quantitative analysis, the train-real-test-synthetic (TRTS) and train-synthetic-test-real (TSTR) methods, as well as the Kolmogorov-Smirnov (KS) test, are utilized. the TRTS and TSTR methods are classifier-based approaches with classification accuracies of 98.36% and 97.95%, respectively, and are used to assess the synthesized intrusion data to ensure the interchangeability of the synthesized intrusion data and real-world intrusion data for training IDSs. the KS test results indicate a similarity score of 0.92 between real and synthesized data. Additionally, to provide interpretable insights into the performance of the model and refrain from using real-world intrusion data for analysis purposes, the framework employs an explainable AI (XAI) approach called local interpretable model-agnostic explanation (LIME), enabling network security analysts to interpret features that influence the intrusive nature of the synthesized network intrusion data while preserving data privacy. © 2025 IEEE.

关键词： Network intrusion

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IoT-based Heart Health Monitoring System

IoT-based Heart Health Monitoring System

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Communication, Computing and Industry 4.0 (C2I4), International conference on

作者： Insha Mustafa Ritarani Sahu Suchismita Chinara Computer Science & Engineering NIT Rourkela Rourkela India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379891

the number of cases of cardiovascular illness has been on the rise for a long time. the proposed solution aims to address this issue using a system equipped with the Internet of things (IoT) to monitor the user’s heart rate and send a real-time alert and updates. the solution aims to integrate a pulse sensor connected to Arduino UNO, through which the user’s heart rate is transmitted wirelessly to a user-friendly Graphical User Interface(GUI) created using Python’s Tkinter library. the data is transmitted using the Message Queuing Telemetry Transport(MQTT) protocol. through this, the user information is sent to the backend of the application. Since this solution involves critical information, data privacy, and security is maintained using Advanced Encryption Standard-256(AES-256). the system implements a machine learning (ML) algorithm to predict heart attack risk using heart rate and only static features and notify the user of any abnormality in near real-time. the solution is proposed to be integrated into a digital watch, offer accessibility in remote areas, and alert the user instantly when there is a cardiovascular risk.

关键词： Heart rate Protocols Machine learning algorithms Prediction algorithms Real-time systems Encryption Telemetry Internet of things security Monitoring

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5th International conference on Applied Informatics, ICAI 2022

5th International Conference on Applied Informatics, ICAI 20...

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5th International conference on Applied Informatics, ICAI 2022

ISBN: (纸本)9783031196461

the proceedings contain 32 papers. the special focus in this conference is on Applied Informatics. the topics include: Statistical Characterization of Image Intensities in Regions Inside Arteries to Facilitate the Extraction of Center Lines in Atherosclerosis Frameworks;Text Encryption with Advanced Encryption Standard (AES) for Near Field Communication (NFC) Using Huffman Compression;a Systematic Review on Phishing Detection: A Perspective Beyond a High Accuracy in Phishing Detection;application of Duality Properties of Renyi Entropy for Parameter Tuning in an Unsupervised Machine Learning Task;preliminary Study for Impact of Social Media Networks on Traffic Prediction;website Phishing Detection Using Machine Learning Classification Algorithms;AESRSA: A New Cryptography Key for Electronic Health Record security;Detection of COVID-19 Using Denoising Autoencoders and Gabor Filters;development of a Mobile application to Save Lives on Catastrophic Situations;COVID-19 Article Classification Using Word-Embedding and Different Variants of Deep-Learning Approach;internet of things with Wearable Devices and Artificial Intelligence for Elderly Uninterrupted Healthcare Monitoring Systems;a Wayuunaiki Translator to Promote Ethno-Tourism Using Deep Learning Approaches;rating the Acquisition of Pre-writing Skills in Children: An Analysis Based on Computer Vision and data Mining Techniques in the Ecuadorian Context;study Techniques: Procedure to Promote the Comprehension of Texts in University Students;TPACK and Toondoo Digital Storytelling Tool Transform Teaching and Learning;Fully Automated Lumen Segmentation Method and BVS Stent Struts Detection in OCT Images;predictive Modeling Toward the Design of a Forensic Decision Support System Using Cheiloscopy for Identification from Lip Prints;Automotive Industry applications Based on Industrial Internet of things (IIoT). A Review;implementation of an IoT Architecture for Automated Garbage Segregation;architecture on Knowledge Managem

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application of Computer Image Recognition Algorithm in Agricultural Product Quality Inspection

Application of Computer Image Recognition Algorithm in Agric...

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5th International conference on Machine Learning and Big data Analytics for IoT security and privacy, SPIoT 2024

作者： Lu, Xiangyu Bureau of Agriculture and Rural Affairs of Ningyang County Shandong Taian China

In agricultural product quality inspection, subtle defects of agricultural products are easily disturbed by complex background and lighting changes, making them difficult to identify. To solve this problem, this paper uses Vision Transformer (ViT) to increase the precision of identifying minute flaws on agricultural items' surfaces. the training data set is enlarged using data augmentation technology, which rotates, crops, and modifies brightness to enhance the model's capacity to adjust to various changes. Using a self-attention mechanism, the ViT model detects minor surface flaws globally and captures long-range dependencies in the image. Combining transfer learning and fine-tuning strategies, the ViT model pre-trained on a large-scale image dataset was optimized on a specific agricultural product dataset, further improving recognition accuracy and robustness. Experimental results indicate that the ViT model in this work has F1-score of 0.89 and a precision of 92.5% when compared to other models. this shows that ViT has high accuracy and robustness in agricultural product quality detection. the results of this paper have made a great contribution to the development of future agricultural automation detection. © 2025 the Authors. Published by Elsevier B.V.

关键词： Fruits

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A Review on application of Deep Learning in Natural Language Processing 5

A Review on Application of Deep Learning in Natural Language...

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5th International conference on Contemporary Computing and Informatics, IC3I 2022

作者： Kshirsagar, Pravin Ramdas Reddy, Dhoma Harshavardhan Dhingra, Mallika Dhabliya, Dharmesh Gupta, Ankur G H Raisoni College of Engineering Department of Artificial Intelligence Maharashtra Nagpur India Trainee Security Analyst JunoClinic Davman Technology Services Private Limited Maharashtra Mumbai India Manipal University Jaipur Department of Mathematics and Statistics Rajasthan Jaipur India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vaish College of Engineering Department of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798350398267

Natural Language Processing (NLP) is a developing method utilized in building different sorts of Artificial Intelligence (AI) that is available in today's time. More intellectual applications will tend to be a primary goal for ongoing and upcoming research. the requirement and desire for data-driven strategies for automatic semantic analysis have risen as a result of recent improvements in processing capacity as well as the accessibility of enormous several linguistic records. A boom in the applications throughout the previous several years of deep learning approaches has advanced the area of natural language processing. this review offers a succinct summary of deep learning architectures and techniques as well as a basic introduction to the area. Our goal is to develop a theoretical study of numerous sectors where NLP may have a significant impact and completely alter the situation with its automated approaches. Everyone is interested in investing in it since it is a hot issue. An in-depth investigation of NLP and its field is used to create these applications. Natural language processing (NLP) trends and its constituent parts are covered in the introduction to this article before it discusses applications for NLP, its emergence, and related issues. © 2022 IEEE.

关键词： Deep learning

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Research on the application and Sustainable Development of Digital Twin Technology Based on Big data Technology and Deep Learning in the Green Transformation of Manufacturing Industry

Research on the Application and Sustainable Development of D...

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5th International conference on Machine Learning and Big data Analytics for IoT security and privacy, SPIoT 2024

作者： Li, Bin Columbia Climate School Columbia University New YorkNY10027 United States

With the increasing global attention to sustainable development, traditional manufacturing is accelerating its transformation towards green, intelligent, and service-oriented directions. To address various challenges in green manufacturing, manufacturing companies are gradually introducing advanced technologies such as digital twins, the Internet of things (IoT), and Cyber Physical Systems (CPS). this article proposes a comprehensive evaluation method based on digital twins, which combines bill of materials (BOM) and energy consumption bill of materials (ECBOM) data, and achieves energy efficiency evaluation throughout the entire lifecycle of manufacturing services by integrating the energy efficiency characteristics of virtual and physical spaces. During the research process, this article constructed a five dimensional energy efficiency evaluation model and innovatively proposed a multi perspective BOM to ECBOM conversion method. In addition, a real-time feedback mechanism has been designed to ensure effective data transfer between physical and virtual spaces. the research results indicate that this method significantly improves the accuracy of energy efficiency assessment, and BOM and ECBOM play a crucial role as data links in energy efficiency assessment in virtual and physical spaces. the method proposed in this article has important application prospects for promoting the green transformation and sustainable development of the manufacturing industry. © 2025 the Authors. Published by Elsevier B.V.

关键词： Green development

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security Analysis of SplitFed Learning 22

Security Analysis of SplitFed Learning

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proceedings of the 20th acm conference on Embedded Networked Sensor Systems

作者： Momin Ahmad Khan Virat Shejwalkar Amir Houmansadr Fatima M. Anwar University of Massachusetts

ISBN: (纸本)9781450398862

Split Learning (SL) and Federated Learning (FL) are two prominent distributed collaborative learning techniques that maintain data privacy by allowing clients to never share their private data with other clients and servers, and find extensive IoT applications in smart healthcare, smart cities and smart industry. Prior work has extensively explored the security vulnerabilities of FL in the form of poisoning attacks. To mitigate the effect of these attacks, several defenses have also been proposed. Recently, a hybrid of both learning techniques has emerged (commonly known as SplitFed) that capitalizes on their advantages (fast training) and eliminates their intrinsic disadvantages (centralized model updates).In this paper, we perform the first empirical analysis of SplitFed's robustness to strong model poisoning attacks. We observe that the model updates in SplitFed have significantly smaller dimensionality as compared to FL that is known to have curse of dimensionality. We show that large models that have higher dimensionality are more susceptible to privacy and security attacks, whereas the clients in SplitFed do not have the complete model and have lower dimensionality, making them more robust to existing model poisoning attacks. Our results show that the accuracy reduction due to the model poisoning attack is 5x lower for SplitFed compared to FL.

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Personal health records: Blockchain-based identity management and data ownership 5

Personal health records: Blockchain-based identity managemen...

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5th International conference on Future Networks and Distributed Systems: the Premier conference on Smart Next Generation Networking Technologies, ICFNDS 2021

作者： Aldamaeen, Ohud Rashideh, Waleed Alalawi, Sameer Obidallah, Waeal Imam Mohammad Ibn Saud Islamic University Saudi Arabia Technical and Vocational Training Corporation Saudi Arabia

ISBN: (纸本)9781450387347

data privacy and security preservation are essential issues in the public sector and, particularly, in the health and medical sectors. Patients often lose their data and medical history if they must move between hospitals. In these cases, other health care providers must repeat some procedures, such as analyses and x-rays. An approach that focuses on securely managing and sharing patient health records is thus required. As traditional centralised systems in hospitals do not meet the requirements for such an approach, this calls for the implementation of a secure structure that is capable of protecting patient data. One potential solution is the use of decentralised personal health records in a system that would allow the patient to own, manage and access their own data. this can be achieved by using the type of blockchain technology that would permit patients to identify shareable information and grant or prevent providers and other entities from accessing their data. Utilising the methodology of design science, this research will propose a solution for personal health records management by identifying the necessary requirements, and advantages and disadvantages of such a system. the study will propose a solution that will allow the patients to own, control, and benefit from their data. © 2021 acm.

关键词： Blockchain

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Network Anomaly Detection Algorithm Based on Fusion of Artificial Fish Swarming and Isolation Forest

Network Anomaly Detection Algorithm Based on Fusion of Artif...

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International conference on Computer Engineering and applications (ICCEA)

作者： Chen Zhang Wei Shen ShengZhao Wang Yue Wu Artificial Intelligence and Big Data Academy Hefei University Hefei China Guochuang Software Co. Ltd. Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Currently, network security involves all aspects of human life privacy, etc., and anomalous data detection has been an important means of protection against network attacks. Aiming at the problem of unreasonable allocation of voting weights of different isolation trees in isolation forest, an isolation forest algorithm based on artificial fish swarming is proposed. the artificial fish swarming algorithm(AFSA) has a strong global search capability for the problem space solution, and in the stage of calculating the anomaly score, the artificial fish swarming algorithm is used to optimise the weight of the isolation trees in isolation forest, increase the weight of isolation trees with high detection accuracy, and reduce the weight of isolation trees with low detection accuracy, so as to ultimately improve the accuracy of the detection of anomalous data. In this paper ,we conduct comparative experiments with Probabilistic Generalization of Isolation Forest(PGIF) and Isolation Forest(IForest) on the ODDS anomaly detection dataset and network intrusion detection dataset. the experimental results show that the present algorithm improves the average AUC value by 6% and 5% on the ODDS dataset and NSL-KDD dataset, respectively.

关键词： Accuracy Sociology Forestry Fish Search problems Resource management Time complexity

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Research on Feature and Architecture Design of AI Firewall 5

Research on Feature and Architecture Design of AI Firewall

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5th Annual International conference on data Science and Business Analytics, ICDSBA 2021

作者： Wang, Zhijia The Third Research Institute of Ministry of Public Security Shanghai China

ISBN: (纸本)9781665445900

this paper analyzes the current network security environment and the shortcomings of firewall products in the current network environment, and expounds the benefits that can be brought by the application of AI technology in threat detection. then, the paper analyzes the advantages of AI firewall and expounds the characteristics that AI firewall can build learning models and realize the independent evolution of threat detection capability. Finally, the intelligent defense system and logical architecture of AI firewall are designed. the intelligent defense system has three components, including cloud intelligence, security center and firewall. the logical architecture has six layers of architecture, including hardware layer, data layer, algorithm layer, detection layer, analysis layer and interaction layer. these designs provide some reference for the design and development of AI firewall. © 2021 IEEE.

关键词： Network architecture

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