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A Survey of Major Cybersecurity Compliance Frameworks

A Survey of Major Cybersecurity Compliance Frameworks

引用

ieee international conference on Big data Security on Cloud (BigdataSecurity), High Performance and Smart computing (HPSC) and Intelligent data and Security (IDS)

作者： Wenjia Wang Seyed Masoud Sadjadi Naphtali Rishe Knight Foundation School of Computing and Information Sciences Florida International University Miami USA

ISBN: (数字)9798350389524

ISBN: (纸本)9798350389531

Motivated by the challenge of navigating the complex landscape of cybersecurity compliance, this study critically examines and evaluates seven major cybersecurity frameworks: SOC 2, GDPR, PCI DSS, HIPAA, CIS Controls V8, NIST CSF, and CMMC 2.0. Our research focuses on understanding their distinct features and operational nuances, addressing a significant gap in current compliance strategies. We contribute a novel set of risk management-based evaluation criteria, offering a comprehensive analysis of these frameworks. The study further explores the Secure Controls Framework (SCF) and its effective integration with these frameworks, summarizing a unified mapping approach. This mapping facilitates streamlined compliance across multiple standards, providing a strategic tool for organizations. Our findings offer pivotal insights into the efficacy of each framework in managing cybersecurity risks, underlining the necessity for an integrated, risk-focused approach to compliance in the digital era.

关键词： Surveys Navigation Organizations NIST Big data Computer security

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Deep Learning Approaches to Babylonian Numeral Recognition: A CNN-SVM Hybrid Model

Deep Learning Approaches to Babylonian Numeral Recognition: ...

引用

computing (ICOCO), ieee international conference on

作者： Loay Alzubaidi Islam Al-Qudah Kheir Eddine Bouazza Computer and Infomration Science Higher College of Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

This paper presents a novel deep learning framework for classifying Babylonian numerals by integrating Convolutional Neural Networks (CNNs) with a hybrid CNN-SVM model. The core contributions include the development of a robust feature extraction pipeline combining zoning, component analysis, and angular analysis of cuneiform symbols, optimized with deep learning techniques. The proposed system extends beyond numeral classification by incorporating object recognition and counting mechanisms to accurately convert multi-digit Babylonian numbers into their decimal equivalents in the base- 60 system. This approach achieves remarkable accuracy of $98.33 \%$, significantly advancing automated recognition and interpretation of ancient mathematical symbols. By bridging machine learning with historical data analysis, the research contributes to the preservation and scholarly study of Babylonian mathematical heritage while demonstrating the efficacy of hybrid models for complex, historical numeral recognition tasks.

关键词： Deep learning Image recognition Computational modeling Pipelines Symbols Feature extraction Mathematical models data models Convolutional neural networks Object recognition

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Privacy-Preserving Real-Time Gesture Recognition using Cloud-Trained Neural Networks

Privacy-Preserving Real-Time Gesture Recognition using Cloud...

引用

ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Kewin Ignasiak Wojciech Kowalczyk Łukasz Krzywiecki Mateusz Nasewicz Hannes Salin Marcin Zawada Department of Fundamentals of Computer Science Wroclaw University of Science and Technology Wroclaw Poland Swedish Transport Administration Borlänge Sweden

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

This paper presents a novel approach to privacy-preserving gesture recognition using a remotely trained neural network. Our method ensures the protection of sensitive user data from potential threats, thereby mitigating concerns about data privacy and security. By utilizing encryption techniques, we enable organizations to train complex machine learning models on large-scale datasets without compromising data integrity. We demonstrate the feasibility of this approach through the implementation of proposed models for gesture classification, which achieved high accuracy in both encrypted and plain modes. Our results show that these models are suitable for embedded devices, making them a viable option for commercial or industrial applications such as smart car navigation systems.

关键词： Training Navigation Computational modeling Machine learning Gesture recognition Organizations data models Real-time systems Protection Homomorphic encryption

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Empowering IT Maintenance with Human-AI Collaboration for Cloud Native

Empowering IT Maintenance with Human-AI Collaboration for Cl...

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Electronic Communications, Internet of Things and Big data (ICEIB), international conference on

作者： Wei-Ji Wang Wen-Da Lain Ya-Shian Wang Cloud Computing Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

We introduce an innovative paradigm to enhance cloud operations to evolve the IT infrastructure landscape and the intricate integration of cloud-native technologies. The progression towards complex application architectures necessitates stringent requirements for operational personnel. Furthermore, the expanding operational boundaries amplify the challenges in maintaining cloud environments. To tackle these challenges, we propose a pioneering human-AI collaboration model tailored to cloud-native operations. This model not only effectively mitigates risks arising from misjudgments and human errors but also substantially bolsters operational efficiency. In the medium to long term, this novel approach aids enterprises in reducing the demand for extensive IT operational workforce deployment.

关键词： Cloud computing Collaboration Big data data models Maintenance complexity theory Personnel

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A Time Series Forecasting Model Based on Machine Learning

A Time Series Forecasting Model Based on Machine Learning

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ieee international conference on Power, Intelligent computing and Systems (ICPICS)

作者： Xiangyu Cui School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Time series forecasting plays a critical role in domains such as finance, environmental science, and health management. Traditional methods like ARIMA and exponential smoothing often fall short in addressing the complexities of modern datasets, especially in modeling non-linear relationships and intricate dependencies. This study introduces the CLT model, an innovative ensemble approach that combines CatBoost, LightGBM, and Transformers to enhance forecasting accuracy and reliability. Employing a dataset of Bitcoin prices from 2014 to 2024, this research involves rigorous data preprocessing, sophisticated feature engineering, and systematic model selection. The CLT model significantly outperforms traditional and single-model approaches by effectively capturing complex dependencies and demonstrating remarkable stability across various validation folds. The results demonstrate that the CLT model outperforms traditional and single-model approaches by effectively capturing complex dependencies and showing remarkable stability across various validation folds. In terms of quantitative metrics, our model achieves an average Root Mean Square Error (RMSE) of 434.54 and an average Mean Absolute Error (MAE) of 374.9, underscoring its superior predictive capabilities. This research underscores the potential of integrating multiple advanced machine learning models to significantly improve the performance of time series forecasting, offering a scalable and efficient solution for high-stakes domains.

关键词： Adaptation models Computational modeling Time series analysis Finance Predictive models Transformers data models Stability analysis Ensemble learning Forecasting

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The Impact of Generative AI on Cloud data Security: A Systematic Study of Opportunities and Challenges 17

The Impact of Generative AI on Cloud Data Security: A System...

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17th ieee/ACM international conference on Utility and Cloud computing, UCC 2024

作者： Ruparel, Hardik Daftary, Harshal Singhai, Videet Kumar, Pramod School of Computer Science University of California Irvine San JoseCA United States School of Computer Science Stony Brook University New York SeattleWA United States College of Engineering Carnegie Mellon University SeattleWA United States The Pennsylvania State University San JoseCA United States

ISBN: (纸本)9798350367201

Since the launch of ChatGPT, Generative AI (GenAI) has transformed virtually every domain, addressing complex problems in areas previously considered insurmountable. At the same time, the adoption of cloud computing for storing confidential data has been rapidly increasing. This swift shift to the cloud has highlighted the critical need for robust data protection measures. In this paper, we merge these two emerging fields to examine how GenAI influences cloud data security. While research on traditional AI-based solutions for cyber threats has been ongoing, the recent surge in GenAI adoption calls for a focused, systematic study to address specific research questions related to GenAI. We formulate 7 research questions and analyze data from 4 major digital repositories (ieee, Springer, ACM, Wiley) to provide an extensive overview of how GenAI contributes to mitigating key cloud security threats such as Malware, Denial-of-Service (DoS), and Phishing, among others. We also discuss the potential risks posed by GenAI in cloud security and highlight the operational and ethical challenges that impede its widespread adoption. This systematic study aims to provide valuable insights for fellow researchers and IT professionals, guiding them in effectively leveraging GenAI to enhance cloud data security while being mindful of its limitations and challenges. © 2024 ieee.

关键词： Deep learning

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Research on TD3-Based Offloading Strategies for complex Tasks in MEC Systems 10

Research on TD3-Based Offloading Strategies for Complex Task...

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10th international conference on Computer and Communications, ICCC 2024

作者： Wang, Shuai Yu, Bo Wang, Ning Wang, Wei Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China

The rapid growth of smart medical devices, smart home devices, and vehicular equipment has significantly increased the demand for computing power in mobile devices. These devices handle large-scale data processing and complex computational tasks but face inherent limitations in resources and performance. Mobile Edge computing (MEC) addresses these challenges by providing IT service environments and cloud computing capabilities at the network edge, reducing latency, enhancing network efficiency, and optimizing user experience. However, MEC faces limitations such as high deployment and maintenance costs, limited application types, and an unstable operating environment. This paper proposes a Twin Delayed Deep Deterministic Policy Gradient (TD3)-based MEC multi-objective hierarchical complex task offloading algorithm. The algorithm aims to adaptively allocate computational and network resources, reduce average service time, balance resource utilization, and minimize task delay rates. Key contributions include integrating MEC with SDN and deep reinforcement learning, detailed modeling of complex tasks using Directed Acyclic Graphs (DAG), and defining a hierarchical multi-objective reward function. This mechanism optimizes multiple objectives simultaneously, enhancing decision-making. Experiments in a simulated MEC environment show that the TD3-based algorithm outperforms existing methods in learning speed, average service time, task delay rate, and server load balancing. This study provides a novel solution for complex task offloading in MEC environments and offers valuable insights for future MEC system design. © 2024 ieee.

关键词： Mobile edge computing

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Research on the Application of Contrast Attention Hypergraph Neural Networks (CAHG) in Image Classification

Research on the Application of Contrast Attention Hypergraph...

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ieee international conference on Power, Intelligent computing and Systems (ICPICS)

作者： Yuxuan Chen Teng Wang School of Software Engineering South China University of Technology Guangzhou China College of Technology and Data Yantai Nanshan University Yantai China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Hypergraphs, contrastive learning, and attention mechanisms are widely applied in many fields. In the field of computer vision, they are commonly used to capture complex information from images. In order to achieve better performance in image classification tasks and expand to more other tasks, this work proposes a new framework-Contrast Attention Hypergraph neural networks (CAHG), which can better capture complex relationships in image data and improve image classification performance by comparing views constructed in different ways. In the future, this framework can be extended to other fields such as multimodality. This study used a remote sensing cloud image dataset, which includes 19000 remote sensing cloud images and 7 classification methods. Compared with several baseline methods, our CAHG model has superior performance. Our downstream tasks cover various fields, including but not limited to autonomous driving, medical imaging, and many other areas. This work indicates that extracting information in multiple ways and transforming it into a hypergraph representation, followed by comparative learning, can improve performance and can be used in various tasks in the future.

关键词： Computer vision Attention mechanisms Neural networks Contrastive learning Information representation Transformers data models data mining Remote sensing Image classification

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Enhancing Digital Health Analysis with Gravitational Models: Creative computing for Semantic 3D Visualisation

Enhancing Digital Health Analysis with Gravitational Models:...

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Digital Health (ICDH), ieee international conference on

作者： Chong Zeng Hongji Yang Yue Wu Philip P Zhang Yanju Yao Marwan Al-Akaidi Zisen Zhou School of Computing and Mathematical Science University of Leicester Leicester England College of Science and Computing Wigwe University Isiokpo Rivers State Nigeria

ISBN: (数字)9798350368574

ISBN: (纸本)9798350368581

In this paper, we explore the innovative application of physical laws, specifically Newton’s Law of Universal Gravitation, as metaphors within the realm of creative computing. By adapting gravitational concepts to measure the semantic “distances” and “attractions” between entities in textual data, we propose a computational model that interprets these relationships through the lens of physical force dynamics. This approach allows us to creatively visualise complex data interactions in a 3D space, where the metaphorical ‘mass’ and ‘distance’ between entities dictate their spatial arrangement based on calculated ‘gravitational’ forces. The core of our methodology involves the adaptation of the gravitational formula. We apply this model to a case study that demonstrates how entities with stronger semantic links are visually closer, creating imaginative and interpretative 3D scenes. This paper not only highlights the utility of physical metaphors in enhancing computational creativity but also opens new avenues for the application of such models in digital media, interactive art, and educational technologies. Further, this methodology can be extended to visualise and interpret health data, providing a novel representation of chronic conditions. By bridging physical theories and computational aesthetics, we provide a fresh perspective on data visualisation and creative computing.

关键词： Solid modeling Analytical models Adaptation models Three-dimensional displays Computational modeling Semantics data visualization

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Measuring Spoken English Proficiency Level Based on IELTS Speaking Test Using Machine Learning Models

Measuring Spoken English Proficiency Level Based on IELTS Sp...

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2024 ieee/WIC international conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2024

作者： Yves, Dylan Mac Chan, Jonathan Hoyin School of Information Technology King Mongkut's University of Technology Thonburi Innovative Cognitive Computing Research Center Bangkok Thailand

ISBN: (纸本)9798331504946

This study presents the development of a system for evaluating English-speaking proficiency based on the IELTS framework. We employed data collection, analysis, and machine learning techniques to build a predictive model. The system focuses on using the statistical features extracted from the transcription of the speaking test. The initial model (XGBoost) showed a mean squared error of 2.29. After feature reduction and hyperparameter tuning, performance improved, achieving an average cross-validation score of 1.57 with a variance of 0.3872 across five folds. Our analysis found that lexical diversity, measured by the total number of unique words, is the most influential factor in predicting IELTS scores, while other features had minimal impact. Despite the improvements, the model does not account for critical speech components like fluency and pronunciation. Future research should address these aspects to better align with IELTS criteria. This study contributes to natural language processing and linguistics by offering a new machine learning model for predicting IELTS scores from the test transcription. © 2024 ieee.

关键词： Mean square error

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