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Data adjusting strategy and optimized XGBoost algorithm for novel insider threat detection model

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2023年第16期360卷 11414-11443页

作者： Kan, Xiu Fan, Yixuan Zheng, Jinjie Chi, Chi-hung Song, Wanqing Kudreyko, Aleksey Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Natl Univ Singapore Sch Comp Singapore 119275 Singapore Ufa Univ Sci & Technol Dept Gen Phys Zaki Valedi 32 Ufa 450076 Russia

With the growth of access to internal system resources, the insider threat problem is emerging and can bring immeasurable losses to enterprises. In order to detect the hidden threats and guide the formulation of enterprise management strategy, it is important to analyze and understand the employee behavior. Thus, we suggest a user behavior analysis system framework, which mainly includes data processing, user behavior modeling and results analysis. Data Adjusting (DA) strategy and optimized eXtreme Gradient Boosting (XGBoost) model are utilized with the aim of full analysis small amount of feature information. The strategy for detecting suspicious behavior can be the following. Firstly, select initial suspicious data, misclassification data retention and combination sampling. Secondly, for further behavior model construction, an improved particle swarm optimization algorithm based on ethnic randomized particles (ERPSO), which introduces Gaussian white noise with adjustable intensity into acceleration coefficients is given for searching the optimal XGBoost parameters. In addition, based on the designed DA strategy and the proposed ERPSO algorithm, we have also compared the results of the proposed methods with the current state-of-the-art methods. Experimental results show that the XGBoost optimized by the ERPSO (ERPSO-XGBoost) model has comprehensive performance, which proves the rationality and effectiveness of the insider behavior analysis system framework. Through a comprehensive understanding of insider behavior, the obvious characteristic behavior is found to adjust the management strategy and guide the behavior modeling, so as to prevent more losses in time. (c) 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.

关键词： Insider threat Behavior modeling particle swarm optimization algorithm eXtreme Gradient Boosting Management strategy

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Control of thermal uniformity in microwave heating process by BPNN and adaptive particle swarm optimization

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Heliyon 2024年第21期10卷 e37971页

作者： Yao, Qing Zheng, Xin Wang, Rui Liang, Wenlong Liu, Tie Chu, Wenxiao Key Laboratory of Healthy & Intelligent Kitchen System Integration of Zhejiang Province Ningbo 315336 China Ningbo Fotile Kitchen Ware Company Zhejiang Ningbo 315336 China Key Laboratory of Thermal-Fluid Science and Engineering Ministry of Education Xi'an Jiaotong University Shaanxi Xi'an 710049 China

As China advances its green economy, innovative methods are being employed to enhance energy optimization and conservation within energy-intensive industries. Among these methods, microwave heating stands out due to its superior heating efficiency and lack of pollution. Nonetheless, uniform heating remains a challenge because the microwave absorption capacity of the heated medium varies with changes in heating time and temperature. To address this issue, an Adaptive particle swarm optimization (APSO) neural network microwave system based on the Back Propagation Neural Network (BPNN) is proposed. This system leverages the fundamental principles of particle swarm optimization (PSO), categorizing particle swarms into three types and iterating them through distinct processes to achieve optimal results. An APSO controller is designed based on system identification, adjusting the controller parameters according to the error between the real-time system output and the identification model. The feedback error is used as the fitness function in the PSO algorithm, continuously adjusting the weights and thresholds of the neural network. This intelligent control approach optimizes the microwave oven's input power to minimize the error between the actual temperature output and the identified temperature. The APSO controller is designed based on system identification, with the intelligently controlled microwave heating system adjusting its input power to minimize the error between the identified and actual temperature outputs. Unlike traditional Proportional Integral Differential (PID) and BPNN controllers, this approach calculates the output of the identified model and the error of the actual model, feeding this information back to the controller. The feedback error serves as the fitness function in the PSO algorithm, enabling continuous adjustment of the network's weights and thresholds to regulate the microwave equipment's output power, thereby ensuring the output temperature

关键词： APSO controller Back propagation neural network Control system Microwave heating particle swarm optimization algorithm

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Research on data mining algorithm based on neural network and particle swarm optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第3期35卷 2921-2926页

作者： Fei, Xianju Tian, Guozhong Changzhou Inst Technol Sch Comp Informat & Engn Changzhou Jiangsu Peoples R China

In previous studies, due to the sparsity and chaos of distributed data, such a result would lead to a local convergence phenomenon by using PSO algorithm, resulting in low accuracy of data mining. So this time we proposed a data mining algorithm based on neural network and particle swarm optimization. At the beginning, we calculated the global kernel function of differentiated distributed data mining and mixed to build the mining decision model. The training error was used as the constraint condition of mining optimization to realized data optimization mining. The results showed that the differential distributed data mining with this algorithm has higher accuracy and stronger convergence.

关键词： Neural network algorithm particle swarm optimization algorithm data mining algorithm

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Fault diagnosis of high power grid wind turbine based on particle swarm optimization BP neural network during COVID-19 epidemic period

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第6期39卷 9027-9035页

作者： Chen, Xi Zhoukou Normal Univ Sch Mech & Elect Engn 9 Wenchang St Chuanhui Dist Zhoukou Peoples R China

During the COVID-19 pandemic, the maintenance of the wind turbine is unable to be processed due to the problem of personnel. This paper presents two neural network models: BP neural network and LSTM neural network combined with particle swarm optimization (PSO) algorithm to realize obstacle maintenance detection for wind turbine Aiming at the problem of gradient vanishing existing in the traditional regression neural network, a fault diagnosis model of wind turbine rolling bearing is proposed by using long-term and short-term memory neural network. Through the analysis of an example, it is verified that the diagnosis results of this method are consistent with the actual fault diagnosis results of wind turbine rolling bearing and the diagnosis accuracy is high. The results show that the proposed method can effectively diagnose the rolling bearing of wind turbine, and the long-term and short-term memory neural network still has good fault diagnosis performance when the difference of fault characteristics is not obvious, which shows the feasibility and effectiveness of the method.

关键词： Wind turbine fault diagnosis particle swarm optimization algorithm BP neural network

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Economic evaluation of energy storage integrated with wind power

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CARBON NEUTRALITY 2023年第1期2卷 16页

作者： Zhang, Xinjing Feng, Lu Li, Xiaoyu Xu, Yujie Wang, Liang Chen, Haisheng Chinese Acad Sci Inst Engn Thermophys Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Energy storage can further reduce carbon emission when integrated into the renewable generation. The integrated system can produce additional revenue compared with wind-only generation. The challenge is how much the optimal capacity of energy storage system should be installed for a renewable generation. Electricity price arbitrage was considered as an effective way to generate benefits when connecting to wind generation and grid. This wind-storage coupled system can make benefits through a time-of-use (TOU) tariff. A proportion of electricity is stored from the wind power system at off-peak time (low price), and released to the customer at peak time (high price). Thus, extra benefits are added to the wind-storage system compared with wind-only system. A particle swarm optimization (PSO) algorithm based optimization model was constructed for this integrated system including constraints of state-of-charge (SOC), maximum storage and release powers etc. The proposed optimization model was to obtain the optimal capacity of energy storage system and its operation control strategy of the storage-release processes, to maximize the revenue of the coupled system considering the arbitrage. Furthermore, the energy storage can provide reserve ancillary services for the grid, which generates benefits. The benefits of energy storage system through reserve ancillary services were also calculated. A case study was analyzed with respect to yearly wind generation and electricity price profiles. The benefit compared with no energy storage scenario was calculated. The impact of the energy storage efficiency, cost and lifetime was considered. The sensitivity and optimization capacity under various conditions were calculated. An optimization capacity of energy storage system to a certain wind farm was presented, which was a significant value for the development of energy storage system to integrate into a wind farm.

关键词： Energy storage Wind-storage coupled system Electricity price arbitrage and ancillary services Compressed air energy storage particle swarm optimization algorithm

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Effects of flight parameters for plant protection UAV on droplets deposition rate based on a 3D simulation approach

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International Journal of Agricultural and Biological Engineering 2023年第1期16卷 66-72页

作者： Lifeng Xu Zhongzhu Yang Zusheng Huang Weilong Ding Gerhard Buck-Sorlin College of Computer Science&Technology Zhejiang University of TechnologyHangzhou 310026China IRHS INRAEAGROCAMPUS OUESTUniversite d’AngersSFR 4207 QUASAV42 rue Georges Morel49071 BeaucouzécedexFrance

With the development of aviation agricultural technology,the number of farmers adopting the use of drones in daily agricultural activities is growing rapidly in recent *** these,a large portion constitutes agricultural drones being used in pest control and crop protection practices,*** spraying of *** pesticides with drones have proven to be faster than other traditional *** the downside,flight time and range of Unmanned Aerial Vehicles(UAV)are often ***,a proper arrangement of flight height and velocity will greatly improve spraying efficiency.A new strategy to optimize the flight parameters,*** height and flight velocity,for fixed-wing UAV with a 3D simulation-based approach together with an automatic optimization algorithm was proposed in this *** find the optimal parameters for a UAV to fly and spray under certain environmental conditions,a three-dimensional model of the target crop was established first,followed by a detailed simulation of droplet *** a demonstration case,a grass model was developed and used as the target plant,and a physics-based method was used to simulate realistically the movement of the droplets in the air as well as the interaction between the droplets and the plant to obtain the droplet deposition rate under the specified operating ***,the standard particle swarm optimization(PSO)algorithm was used to optimize the UAV operating parameters to obtain the best operating *** results indicate that using the standard PSO algorithm to optimize the operating parameters of the drone could significantly improve the deposition rate and find the best operating parameters.

关键词： unmanned aerial vehicle droplet deposition 3D simulation particle swarm optimization algorithm

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S-box generation algorithm based on hyperchaotic system and its application in image encryption

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第17期82卷 25559-25583页

作者： Yang, Sen Tong, Xiaojun Wang, Zhu Zhang, Miao Harbin Inst Technol Comp Sci & Technol Weihai 264200 Peoples R China

S-box is the only non-linear device in the cryptographic algorithm, and its quality determines the lower limit strength of the cryptographic algorithm. However, because the image data is highly correlated, the traditional encryption methods and their S-boxes, such as AES and DES are not suitable for using in image encryption. Based on this, this paper proposes an S-box generation algorithm based on a 4D hyperchaotic system and improved particle swarm optimization. Firstly, this paper improves on the Lorenz chaotic system and proposes a 4D hyperchaotic system with a higher Lyapunov exponent and more complex dynamics. Secondly, the idea of simulated annealing algorithm is introduced into the particle swarm optimization algorithm, which further improves the efficiency of the particle swarm optimization algorithm and improves the problem that the particle swarm optimization algorithm is easy to fall into the local optimal solution. Then an improved particle swarm optimization algorithm is used to optimize the nonlinearity of the S-box to improve the performance of the S-box. Finally, use the generated S-box to design an image encryption algorithm and prove the security of the S-box. The experimental results show that the S-box designed in this paper has excellent performance in the five indicators of Nonlinearity, SAC, BIC-NL, LP, and DP. At the same time, the encryption result can resist common attacks, so it has strong multimedia security.

关键词： 4D hyperchaotic system S-box Image encryption particle swarm optimization algorithm Simulated annealing algorithm

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Grid Scheduling Strategy Considering Electric Vehicles Participating in Multi-microgrid Interaction

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第3期18卷 1557-1572页

作者： Yu, Zexu Dou, Zhenhai Zhao, Ye Xie, Ruishuo Qiao, Mengmeng Wang, Yuanyuan Liu, Lianxin Shandong Univ Technol Coll Elect & Elect Engn Zibo 255049 Peoples R China

With the expansion of the scale of power grid and the increase of the number of microgrids, the energy interaction between microgrids using contact lines will greatly increase the computation of the superior dispatching center. Therefore, the contact lines between microgrids are cancelled, so that the microgrids can realize energy interaction through the distribution network. However, this method may lead to overload of distribution network connections while reducing the computation. Due to its good transfer characteristics, the addition of electric vehicles (EVs) can alleviate the contact line pressure and realize the load transfer of microgrids. On this basis, a grid dispatching model based on the participation of EVs in microgrid interaction is proposed. Contact lines between microgrids are replaced by EVs and 100% transmission through the distribution network. The grid load is optimized with the goal of minimizing total cost, maximizing renewable energy utilization, and maximizing profit of each integrator. In the process of model optimization, aiming at the problem that the speed factor in the particle swarm optimization algorithm cannot take into account the optimal direction and the optimal step size, the adaptive time factor is added to establish a two-layer improved particle swarm optimization algorithm, which realizes the cooperative optimization of load and electricity price. The simulation results show that the total cost and underutilization of renewable energy of IEEE33-node system are reduced by 13.79% and 67.85%, compared with the traditional interaction mode, while they are reduced by 0.425% and 6.11% in IEEE43-node system.

关键词： Electric vehicles Multi-microgrid Interaction particle swarm optimization algorithm Cost benefit analysist Optimal operation

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Application of Genetic and particle swarm optimization algorithms to Portfolio optimization Problem: Borsa Istanbul and Crypto Money Exchange 4

Application of Genetic and Particle Swarm Optimization Algor...

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4th International Conference on Computer Science and Engineering (UBMK)

作者： Huseyinov, Ilham Ulucay, Samed Istanbul Aydin Univ Yazilim Muhendisligi Bolumu Istanbul Turkey Istanbul Aydin Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781728139647

particle optimization (PSO) algorithm and genetic (GA) algorithms are widely used in a variety of optimization problems. Portfolio optimization problems offer minimum risk and maximum profit-based solutions to help investors on optimal investment. Current researches have focused on PSO and GA algorithms based on Markowitz's mean variance method. This paper examines the implementation of PSO and GA algorithms to solve the optimization problem of two portfolios, Borsa Istanbul (BIST) and Cryptocurrency Exchange (KPB). This study also uses Markowitz mean variance method, that is, variance method for risk minimization and mean method for profit maximization. PSO and GA varying coefficients are compared in terms of sharpe risk percentage and portfolio profit. The comparison result is in favor of PSO algorithm.

关键词： Portfolio optimization Level Variance Model Genetic algorithm particle swarm optimization algorithm

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Low-Carbon Cold-Chain Logistics Path optimization Problem Considering the Influence of Road Impedance

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IEEE ACCESS 2023年 11卷 124055-124067页

作者： Zhang, Aobei Zhang, Ying Liu, Yanqiu Shenyang Univ Technol Sch Management Shenyang 100870 Peoples R China

An improved road impedance function in conformity with Chinese city road traffic is designed for calculating the actual transportation time based on real-time traffic information and the complex road environment in the city. The objective function minimizes the total distribution cost, including fixed cost, transportation cost, damage cost, energy cost, penalty cost, and carbon emission cost. A mathematical model is constructed for cold-chain logistics distribution path optimization considering the influence of road impedance. The model is solved using three particle swarm optimization algorithms with improved weights. The experimental results show that the self-adaptive weighted particle swarm optimization algorithm is more efficient in solving this model. Experimental results obtained from the cold-chain logistics path optimization model considering road impedance compared to those of the model without road impedance indicate that the former are closer to the actual situation. Based on the changes in carbon emission and total cost under different carbon taxes, we analyze the critical carbon tax value and the optimal carbon tax value range to improve the economic and environmental benefits in the distribution process. This study should prove to be of great practical significance and application value for logistics enterprises to conduct rational planning of path problems.

关键词： Real-time traffic road impedance function particle swarm optimization algorithm coldchain logistics critical carbon tax value

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