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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Science Moscow Russia

ISBN: (纸本)9789819706402

In the rapidly evolving realm of cyber-security, the detection of network anomalies serves as a pivotal line of defense against a myriad of malicious activities and cyberthreats. This research undertakes the task of enhancing the accuracy and efficacy of network anomaly detection by employing a comparative analysis of various individual machine learning models. The study delves into the performance of distinct models, including Random Forest, Gradient Boosting, AdaBoost, neural networks, and SVM, meticulously scrutinizing their capabilities in detecting network anomalies. The crux of this study lies in its meticulous evaluation of each individual model on the revered NSL-KDD dataset—an established benchmark within the field of network intrusion detection. Through a systematic blend of rigorous mathematical frameworks, precise model implementations, and comprehensive experimental assessments, this research offers a deep understanding of the inner workings of each algorithm. The pivotal aspect of this study revolves around the comprehensive comparative analysis of these standalone models. Going beyond the mere quantifica-tion of accuracy, the exploration delves into aspects of precision, recall, F1-score, and more, shedding light on their diverse facets of performance. With achieved accuracies of 99.2419% for Random Forest, 99.5197% for Gradient Boosting, 86.6044% for AdaBoost, 84.00% for neural networks, and 87.00% for SVM, this research underlines the distinctive attributes and potential of each model in the context of network anomaly detection. As the study unravels the distinct strengths and limitations of each model, it contributes to the broader landscape of cyber-security by providing insights into the efficacy of individual machine learning approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Two-level data middle platform flow-batch integrated transmission strategy based on dynamic priority 6

Two-level data middle platform flow-batch integrated transmi...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Yin, Jiangbo He, Longgang Beijing China-Power Information Technology Co. Ltd Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798331541750

State Grid two-level data center is a data management and service platform for smart grid, which can realize the functions of data collection, storage, processing, analysis and application. However, its data link transmission faces the challenges of high transmission delay, poor quality of service and difficult to transmit batch data cooperatively on the same data link. In order to solve these problems, this paper proposes an integrated flow and batch transmission configuration model. According to the characteristics and requirements of the data, the model comprehensively considers the factors such as data priority, real-time, urgency and data size, as well as the load and bandwidth requirements of the link, and realizes the data classification and priority division, weighted hybrid scheduling and link allocation transmission control. In order to verify the validity of the model, NS3 is used as the simulation platform, and simulation experiments are carried out in a network topology including headquarters data center, provincial data center and nodes of each business department. The simulation results show that under the same network conditions, compared with the traditional FIFO queue scheduling algorithm and WFQ queue scheduling algorithm, the model can increase the data link transmission efficiency by 20%, reduce the total delay by 21%, ensure the quality of service, and realize the coordinated transmission of stream and batch data. © 2024 IEEE.

关键词： Middleware

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Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

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Big Data Mining and Analytics 2024年第1期7卷 55-74页

作者： Guosheng Cui Ye Li Jianzhong Li Jianping Fan Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern *** has been widely used and studied in the multi-view clustering tasks because of its *** study proposes a general semi-supervised multi-view nonnegative matrix factorization *** algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different *** specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is *** on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

关键词： multi-view semi-supervised clustering discriminative information geometric information feature normalizing strategy

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Research on the Mechanism and Suppression Methods of Commutation Failure in High-Voltage Direct Current Transmission Systems 7

Research on the Mechanism and Suppression Methods of Commuta...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Li, Ming Shen, Xiaolin Li, Xuanlang Liu, Yiqi State Grid Economic and Technology Research Institute Co. Ltd. Beijing China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350359558

High-voltage Direct Current (HVDC) transmission technology has obvious advantages in power transmission, so it has been developed rapidly. Commutation failure is one of the most common faults in HV AC-DC hybrid transmission systems, which can cause DC lock and power oscillation. This paper takes commutation failure as the research object analyzes its causes and main influencing factors and puts forward corresponding suppression strategies, which are significant for the safe and stable operation of HVDC transmission systems. © 2024 IEEE.

关键词： HVDC power transmission

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On Combining Collective Entity Resolution and Repairing (Extended Abstract) 1

On Combining Collective Entity Resolution and Repairing (Ext...

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1st Workshop on AI-Driven Heterogeneous Data Management: Completing, Merging, Handling Inconsistencies and Query-Answering, ENIGMA 2023

作者： Bienvenu, Meghyn Cima, Gianluca Gutiérrez-Basulto, Víctor Univ. Bordeaux CNRS Bordeaux INP LaBRI UMR 5800 Department of Computer Control and Management Engineering Sapienza University of Rome School of Computer Science and Informatics Cardiff University

This work summarizes the salient aspects of our recent work [1], about combining collective entity resolution and repairing. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons Li... 详细信息

关键词： Repair

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MPC-Based Trajectory Tracking control Strategy Incorporating Autonomous Lane-Changing Mechanism for Intelligent Vehicles 8

MPC-Based Trajectory Tracking Control Strategy Incorporating...

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8th CAA International Conference on Vehicular control and Intelligence, CVCI 2024

作者： Zhang, Dongxue Liu, Huiwen Jiao, Xiaohong Zhang, Ting Yanshan University Hebei Key Laboratory of Industrial Computer Control Engineering Hebei Province Qinhuangdao066004 China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (纸本)9798331504892

Navigating the intricate traffic environment, lane-changing has emerged as a frequent and essential driving maneu-ver for intelligent vehicles. However, it is challenging to guarantee the safety of the intelligent vehicle and the trajectory tracking accuracy against the external environment during lane-changing. Considering these challenges to overcome, the model predictive control (MPC)-based trajectory tracking strategy incorporating an autonomous lane-changing mechanism is proposed in this paper. The autonomous lane-changing mechanism considering lane-changing intention is proposed to ensure that the intelligent vehicle generates safe lane-changing decisions and trajectories. Subsequently, the MPC strategy that considers the physical char-acteristics of the intelligent vehicle is proposed to guarantee the accuracy and smoothness of trajectory tracking. The validation of its effectiveness and advantages is established on the PreScan and MATLAB/Simulink co-simulation platforms. © 2024 IEEE.

关键词： Predictive control systems

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An Adaptive Fuzzy SIR Model for Real-Time Malware Spread Prediction in Industrial Internet of Things Networks

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IEEE Internet of Things Journal 2025年

作者： Zheng, Yan Na, Zhenyu Ji, Weidong Lu, Yang Harbin Normal University College of Computer and Information Engineering Harbin150010 China Northeast Forestry University College of Computer and Control Engineering Harbin150010 China

The Industrial Internet of Things (IIoT) networks serve as the foundational infrastructure for real-time communication and data exchange in smart manufacturing. Predicting the spread of malware within IIoT networks is particularly challenging due to uncertainties in infection and recovery rates, which are influenced by dynamic network conditions and device heterogeneity. In this paper, we propose an adaptive fuzzy SIR model that incorporates fuzzy logic and gradient descent optimization to address these uncertainties. Specifically, we integrate fuzzy logic with gradient descent, which introduces an adaptive mechanism to handle uncertain infection and recovery rates in real-time. This synergy ensures robust parameter tuning under fluctuating network states, significantly improving malware spread prediction. The proposed model dynamically adjusts infection and recovery rates using fuzzy differential equations and real-time data adaptation, enhancing prediction accuracy and resilience to network fluctuations. Experimental results demonstrate the model's advantages in improving predictive accuracy, convergence speed, and adaptability, making it a robust solution for securing IIoT networks in smart manufacturing. © 2014 IEEE.

关键词： Smart manufacturing

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Fast and Accurate LSTM Meta-modeling of TNF-induced Tumor Resistance In Vitro

Fast and Accurate LSTM Meta-modeling of TNF-induced Tumor Re...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Abrate, Marco P. Smeriglio, Riccardo Bardini, Roberta Savino, Alessandro Di Carlo, Stefanoi University College London Dept. of Cell and Developmental Biology London United Kingdom Politecnico di Torino Dept. of Control and Computer Engineering Turin Italy

ISBN: (纸本)9798350386226

Multi-level, hybrid models and simulations, among other methods, are essential to enable predictions and hypothesis generation in systems biology research. However, the computational complexity of these models poses a bottleneck, limiting the applicability of methodologies relying on large number of simulations, such as the Optimization via Simulation (OvS) of complex biological processes. Meta-models based on approximate surrogate models simplify multi-level simulations, maintaining accuracy while reducing computational costs. Among Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM) networks are well suited to handle sequential data, which often characterizes biological simulations. This paper presents an LSTM-based surrogate modeling approach for multi-level simulations of complex biological processes. Validation relies on the simulation of Tumor Necrosis Factor (TNF) administration to a 3T3 mouse fibroblasts tumor spheroid based on PhysiBoSS 2.0, a hybrid agent-based multi-level modeling framework. Results show that the proposed LSTM meta-model is accurate and fast compared with the simulator. In fact, it infers simulated behavior with an average relative error of 7.5%. Moreover, it is at least five orders of magnitude faster. Even considering the cost of training, this approach provides a faster, more accurate, and reusable surrogate of multi-scale simulations in computationally complex tasks, such as model-based OvS of biological processes. © 2024 IEEE.

关键词： Tumors

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An Occupant-Centric Comfort control Strategy Using Wearable Smart Devices in Smart-Buildings 12

An Occupant-Centric Comfort Control Strategy Using Wearable ...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Boicu, Mihaela-Gabriela Fagarasan, Ioana Stamatescu, Grigore Vasluianu, Mihaela Fudulu, Cosmin-Florin Dobrea, Marius-Alexandru University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Romania Technical University of Civil Engineering of Bucharest Faculty of Hidrotechnics Romania

ISBN: (纸本)9798350358056

The paper presents a method of assessing thermal comfort in office rooms, using data collected from Fitbit bracelets. The result is a fuzzy system for the level of thermal comfort that wants to meet two objectives: reducing the energy consumption in the building and fulfilling the thermal comfort for the occupants. The representative factors taken into account in this study are: air temperature, relative humidity, user activity, clothing level and skin temperature. The proposed fuzzy system was modeled and simulated in the Matlab/SIMULINK development environment, showing satisfactory results after the analysis of the data taken over a period of several days. © 2023 IEEE.

关键词： comfort control strategy fuzzy control HVAC system smart-building Van Zuilen model

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Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

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computer Systems Science & engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the *** unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the *** individuals,especially those in the agricultural sector,rely on rain *** rainfall is challenging because of the changing nature of the *** area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting *** estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),***,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed *** the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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