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A Review of Intelligent Configuration and Its Security for Complex Networks

Chinese Journal of Electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on Communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of Computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of Advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security Software defined network Lifecycle management

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TCP Vegas Simulation and Analysis on Wireless Sensor Network for Building Structure Condition Monitoring Topology

TCP Vegas Simulation and Analysis on Wireless Sensor Network...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Kamillia, Revina Nuha, Hilal Hudan Mugitama, Satria Akbar Kirana, Disca Safa Information Technology School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350369106

This study aims to analyze the effect of TCP Vegas implementation in wireless sensor networks for building condition structure monitoring protocols. Wireless sensor networks are very effective because they are able to detect vibrations without using cables and at low cost. This research method involves a quantitative approach with an experimental type of research. The study will be conducted using NS-2 as a simulator tool. The data analysis to be carried out aims to identify the results of throughput, delay, and packet loss ratio. TCP Vegas is one of the transmission control protocols that is suitable for monitoring building structure conditions because it can obtain more consistent throughput while reducing delay. This study aims to provide insight into TCP Vegas on wireless sensor networks, especially for monitoring building structure conditions. © 2024 IEEE.

关键词： Packet loss

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BDI Agents Based Dynamic Resource Allocation in Emergency Scenarios

BDI Agents Based Dynamic Resource Allocation in Emergency Sc...

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2024 IEEE International Conference on Agents, ICA 2024

作者： Shang, Junhui Yan, Jun Ren, Fenghui University of Wollongong School of Computing and Information Technology Wollongong Australia

ISBN: (纸本)9798331539917

This study proposes a dynamic resource scheduling framework based on a multi-agent system (MAS). The framework integrates the Belief-Desire-Intention model with fuzzy logic and time windows to enable efficient dynamic resource allocation. It is designed to address various uncertainties in emergency resource allocation, such as delays in data transmission and sudden changes in the external environment. These uncertainties impact the efficiency and accuracy of resource scheduling. Effective resource allocation is critical for ensuring the continuous operation of critical infrastructure, minimizing disaster-related losses, and expediting relief efforts. However, existing approaches often lack consideration for dynamic allocation and real-world constraints. The proposed framework aims to improve resource scheduling under these challenging conditions. © 2024 IEEE.

关键词： Fuzzy logic

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Simulation and Analysis of TCP Tahoe on Wireless Sensor Network for Building Structure Condition Monitoring Topology

Simulation and Analysis of TCP Tahoe on Wireless Sensor Netw...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Kirana, Disca Safa Nuha, Hilal Hudan Mugitama, Satria Akbar Kamillia, Revina Information Technology School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350369106

Simulation and performance analysis of TCP Tahoe on WSN for building structure condition monitoring topology is important to understand the behavior of this protocol in complex wireless environments. By conducting simulations, we can evaluate the performance of TCP Tahoe in sending data with the parameters used, such as throughput, delay, and packet loss ratio. Analysis of simulation results can provide insight into the effectiveness of TCP Tahoe in ensuring reliable and efficient data delivery, as well as help identify areas that require further improvement or optimization. This study aims to evaluate the performance of the Transmission Control Protocol (TCP) Tahoe protocol in a wireless sensor network (Wireless Sensor Network/WSN) used for building structure condition monitoring. This study uses a quantitative method by collecting data from simulations and analysis of TCP Tahoe on a wireless sensor network for building structure condition monitoring topology. The type of research conducted is experimental research. In this study, experiments were conducted through simulations to test the performance of the TCP Tahoe protocol in terms of throughput, delay, and packet loss ratio. This research is expected to contribute to the development and optimization of communication protocols for WSN in building structure condition monitoring applications. © 2024 IEEE.

关键词： Packet loss

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Comparative Performance Analysis of Large Language Models for Deployment in Women's Healthcare Mobile Applications 3

Comparative Performance Analysis of Large Language Models fo...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Bedwa, Mukul Hooda, Nishtha Kumar, Vikram School of Computing Indian Institute of Information Technology Una India

ISBN: (纸本)9798331518592

Large Language Models (LLMs) have sparked considerable interest among researchers across diverse disciplines considering their remarkable text generation and processing capabilities. Their capabilities have the potential to promise better health awareness among female users when integrated with women-specific chatbot services. Therefore, this study aims to compare the LLM models BERT, DistilBERT, Roberta, DialoGPT, TinyLLAMA, and GPT-2 by fine-tuning them on a health-specific dataset and evaluating their efficiency in generating information and engaging responses. Our analysis suggests that text-generation models are well-positioned to deliver more engaging answers and may prove instrumental in effectively enhancing health awareness among women. © 2024 IEEE.

关键词： Natural language processing systems

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Enhancing fairness in breast cancer recurrence prediction through temporal machine learning models

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Neural computing and Applications 2024年第36期36卷 22697-22718页

作者： Sundus, Katrina I. Hammo, Bassam H. Al-Zoubi, Mohammad B. King Abdullah II School of Information Technology The University of Jordan Amman Jordan King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan

Breast cancer recurrence prediction is a significant challenge in oncology. Advanced methodologies are required to improve prediction accuracy and clinical decision-making. This study presents a novel approach to breast cancer recurrence prediction by integrating machine learning techniques and a hybrid data mining methodology incorporating a temporal dimension into dataset derivation. Our research is based on the Jordan Breast Cancer Dataset (JBRCA), which includes over 44,000 cases spanning 15 years collected from the King Hussein Cancer Center’s registry database in Amman, Jordan. The proposed methodology encompasses data understanding, preparation, and model development stages. We use a thorough data preparation process involving multicollinearity feature selection, feature scaling, and strategic sampling to address dataset challenges. Moreover, we introduce a temporal-derived dataset strategy, dividing the data into four distinct time intervals to capture evolving characteristics and optimize model relevance. We employ diverse base classifiers and ensemble methods to enhance predictive performance in model development. We use evaluation metrics such as accuracy, recall, specificity, G-mean, and ROC-AUC to assess model efficacy across temporal intervals. Our experimental findings reveal significant impacts on classifier performance with temporal dataset derivation, with notable strengths observed in specific classifiers and temporal intervals. For instance, the Naive Bayes model demonstrates efficacy in identifying recurrence cases, while logistic regression exhibits robust performance in ROC-AUC and G-mean metrics. Our study contributes to breast cancer recurrence prediction by introducing a novel methodology that addresses dataset challenges and leverages temporal insights for enhanced predictive accuracy. The findings have a direct impact on clinical practice, providing valuable tools for early detection and improved therapy planning. © The Author(s), under e

关键词： Adversarial machine learning

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Predicting Soil Moisture Levels with Gated Recurrent Units: A Deep Learning Approach Integrated with Internet of Things Data

Predicting Soil Moisture Levels with Gated Recurrent Units: ...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Mustafa, Fahmi Rozan Nuha, Hilal Hudan Putrada, Aji Gautama Information Technology School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350369106

This study examines the prediction of soil moisture levels using Gated Recurrent Units (GRU) integrated with Internet of Things (loT) technology. Recognizing the critical role of soil moisture in plant growth and ecosystem health, the research employs real-time data collected from loT devices, including soil moisture, temperature, and humidity, to enhance irrigation efficiency in agricultural practices. Various prediction samples (n-samples) were analyzed, specifically focusing on n-samples of 6, 12, 18, and 24. The findings reveal that the GRU model with a prediction sample of n-samples =12 achieved the highest accuracy, bring in an R2 value of 0.89944 and the lowest Mean Absolute Percentage Error (MAPE) of 0.03201. In contrast, shorter and longer prediction intervals demonstrated decreased accuracy and increased error rates. The study underscores the importance of selecting optimal prediction intervals for reliable soil moisture forecasting and highlights the potential of GRU models in real-time environmental monitoring. By combining deep learning methodologies and loT technology, this research contributes to more efficient irrigation practices that can enhance water conservation and improve crop yields, ultimately promoting sustainable agricultural management strategies. Future work may focus on further enhancing model performance and expanding its applicability across diverse agricultural contexts. © 2024 IEEE.

关键词： Soil moisture

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An iterated greedy algorithm based on NSGA-II for distributed hybrid flow shop scheduling problem 3

An iterated greedy algorithm based on NSGA-II for distribute...

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3rd International Conference on Communications, information System, and Data Science, CISDS 2024

作者： Liu, Dandan Liang, Xu Zou, Zhiyuan School of Computing Beijing Information Science and Technology University Beijing China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

The distributed hybrid flow shop scheduling problems (DHFSP) widely exist in various industrial production processes, and thus have received widespread attention. However, studies on HFSP considering green objective in distributed production environment are quite limited. Therefore, this paper investigated a distributed hybrid flow shop scheduling problem with objectives of minimization the makespan and total energy consumption (TEC). To solve it, an iterated greedy algorithm based on NSGA-II (NSGAIG) is developed. In the proposed algorithm, a random initialization strategy is used to generate the initial solution. Then, starting from the population initialization, a multi-objective local search is carried out for the current optimal solution in the population to obtain the global optimal solution. Next, random variation method is used to increase the exploration space of the algorithm. Finally, the proposed NSGAIG algorithm is compared with other multi-objective optimization algorithms. Experimental results indicate that the proposed NSGAIG outperforms its compared algorithms in solving this problem. © 2025 SPIE.

关键词： Optimization algorithms

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Predicting Soil Moisture Levels with Long Short-Term Memory: A Deep Learning Approach Integrated with Internet of Things Data

Predicting Soil Moisture Levels with Long Short-Term Memory:...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Grandika, Dipo Sukmana Nuha, Hilal Hudan Putrada, Aji Gautama Information Technology School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350369106

Water is essential for plant growth, so it is crucial to adjust watering practices to the specific needs of the plant. Factors that affect the watering process include air temperature and soil moisture. This research aims to predict soil moisture levels. Technologies are needed to support this research in obtaining accurate data on soil moisture levels. This research uses Long Short-Term Memory (LSTM) integrated with Internet of Things (IoT) technology. The data will then be processed and analyzed using a quantitative methodology, focusing on the development and application of the Long Short-Term Memory (LSTM) model. Several prediction steps (n) will be analyzed, specifically at steps 6, 12, 18, and 24. We present model performance metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R2), in both graphical and tabular formats. The results illustrate that the LSTM model achieved the best performance at n = 12, with RMSE = 0.4683 and R2 = 0.8970, indicating a high degree of correlation between predicted and actual values and a low prediction error. This method is expected to improve accuracy and efficiency in predicting soil moisture levels, support decision-making for irrigation strategies, and raise awareness about sustainable water use in agriculture. © 2024 IEEE.

关键词： deep learning lot lstm prediction soil moisture

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Building public trust through digital government transformation: A qualitative study of Indonesian civil service agency 7

Building public trust through digital government transformat...

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7th information Systems International Conference, ISICO 2023

作者： Virnandes, Sose Raeinaldo Shen, Jun Vlahu-Gjorgievska, Elena School of Computing and Information Technology University of Wollongong NSW Australia

This study aims to explore how the government can generate trust through digital government transformation (DGT). The study adopts qualitative approach by using semi-structured interviews of the Indonesian government employees who are involved in DGT on civil servants' recruitment process. The results elaborate the influence of digital capability, governance, leadership, organizational partnership, country characteristic, collaborative environment, financial support, and transparency towards the DGT in generating citizen's trust. This study contributes to the, still limited, body of knowledge for the relationship between DGT and public trust. This also can be important consideration in creating policies particularly for the Indonesian government officials. © 2023 The Authors. Published by Elsevier B.V.

关键词： Industry 4.0

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