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The cuckoo Feature Filtration Method for Intrusion Detection (cuckoo-ID)

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第5期11卷 341-347页

作者： Alsharafat, Wafa Al al Bayt Univ Al Mafraq Jordan

Intrusion Detection Systems (IDSs) play a crucial role in keeping online systems secure from attacks. However, these systems usually face the challenge of needing to handle and analyze a vast volume of data in order to achieve intrusion detection. Feature filtration is a solution that overcomes this challenge by focusing on the characteristic network features that play a significant role in enabling these systems to achieve high detection rates. This paper presents an intelligent cuckoo feature filtration method that is intended to prune away insignificant network features. Then, an IDS (the cuckoo-ID) is designed in which an eXtended Classifier System (XCS) uses the filtered features for improving the rate of detection of network intrusions. Thus, the main objective of cuckoo-ID is to maximize the detection rate (DR) and minimize the false alarm rate (FAR). Experiments were then run on the KDDcup'99 dataset to test the intrusion detection (ID) efficiency of the proposed system. The results showed that cuckoo filtration does profoundly raise the ID rate of the entire system. Finally, the DR and FAR of cuckooID were compared with those of intrusion detection methods that depend on network feature filtration.

关键词： cuckoo algorithm feature filtration intrusion detection XCS detection rate

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cuckoo and krill herd-based k-means plus plus hybrid algorithms for clustering

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EXPERT SYSTEMS 2019年第4期36卷

作者： Aggarwal, Shruti Singh, Paramvir Sri Guru Granth Sahib World Univ Dept Comp Sci Fatehgarh Sahib Punjab India Dr BR Ambedkar Natl Inst Technol Dept Comp Sci & Engn Jalandhar Punjab India

Clustering algorithms can be optimized using nature-inspired techniques. Many algorithms inspired by nature, namely, firefly algorithm, ant colony optimization algorithm, and so forth, have improved clustering results. k-means is a popular clustering technique but has limitations of local optima, which have been overcome using its various hybrids. k-means++ is a hybrid k-means clustering algorithm that gives the procedure to initialize centre of the clusters. In the proposed work, hybrids of nature-inspired techniques using cuckoo and krill herd algorithm are implemented on k-means++ algorithm to enhance cluster quality and generate optimized clusters. The designed algorithms are implemented, and the results are compared with their counterparts. Performance parameters such as accuracy, f-measure, error rate, standard deviation, CPU time, cluster quality check, and so forth are used to measure the clustering capabilities of these algorithms. The results indicate the high performance of newly designed algorithms.

关键词： clustering cuckoo algorithm k-means k-means plus plus krill herd algorithm nature-inspired algorithms

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A Sidelobe and Energy Optimization Array Node Selection algorithm for Collaborative Beamforming in Wireless Sensor Networks

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IEEE ACCESS 2018年 6卷 2515-2530页

作者： Sun, Geng Liu, Yanheng Liang, Shuang Chen, Zhaoyu Wang, Aimin Ju, Qianao Zhang, Ying Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Changchun Vocat Inst Technol Dept Informat Technol Changchun 130033 Jilin Peoples R China

The beam pattern of the virtual node antenna array (VNAA) in wireless sensor networks has a high maximum sidelobe level (SLL), thereby causing communication interference because of the uncontrollable node positions. A sidelobe and energy optimization array node selection (SEOANS) algorithm is proposed for optimally selecting the sensor nodes to form a VNAA that optimizes the beam pattern of VNAA and reduces the average energy consumption of nodes. SEOANS uses a calculation method to determine the optimal number of array nodes, proposes a node location selection optimization method based on concentric circular ring array and a novel swarm intelligence optimization algorithm called cuckoo search chicken swarm optimization (CSCSO) to optimize the excitation current of each array node. CSCSO uses chaos theory, introduces the inertia weight Levy flight, and adopts the grade mechanism in chicken swarm optimization to improve the performance of the cuckoo search algorithm. In addition, the scheduling and fault tolerance mechanisms are designed and implemented in SEOANS. Simulation results show that the node position selection optimization method and the excitation current optimization based on CSCSO can effectively reduce the maximum SLL. Furthermore, compared with traditional clustering routing algorithms, SEOANS has advantages in the communication delay and average energy consumption of nodes, thus effectively improving network lifetime.

关键词： Wireless sensor networks random antenna arrays cuckoo algorithm energy efficient beamforming

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Smart grid scheduling and control based on master-slave game

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2018年第3期6卷 162-168页

作者： Liang, Rongbo Yu, Yongjin Yang, Feiran Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Peoples R China

Electric power dispatch is an effective management method used to ensure the safe and stable operation of the power grid, however, there will be some conflicts of interest between the power selling company and the power generation company during the dispatch process. This article aims to minimize the cost of electricity purchased by the power grid company and maximize the sales revenue of power generating company. In this paper, the decision-making space is based on the set of strategies for each power plant output and the on-grid price, proposing a master-slave game scheduling model in which the power grid company is used as the main game and power plants are the game followers. By using the cuckoo algorithm to optimize the master-slave game model, scheduling test results show that master-slave game scheduling provides better overall performance than economic dispatch.

关键词： Electric power dispatch cost power grid interest cuckoo algorithm master-slave game

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Electricity Sales Prediction Model of Electricity Market of the Non-linear Regression Combination with the Optimal Weight

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电脑学刊 2019年第5期30卷 75-87页

作者： Zhao-Yang Qu Shu-Ya Xie Rong-qiang Feng Nan Qu Hong-bo Lv Zuo-Wei Chi Chong Qu

The model to estimate electric sales in electric market of the optimal weighting nonlinear regression combination is brought forth to improve the accuracy of electricity sales forecast. Firstly, the least square method is used to estimate the nonlinear regression equation in order to obtain the result sets of the prediction sequence. Secondly, the design method of minimizing the calculation of fitting variance and weighting com-bination to calculate the residual sequence and weighting of the Radial Basis Function (RBF) neural network and the least squares vector machine, finally the result sets of residual sequence concluded. At the end, a nonlinear decreasing strategy of inertia weight is introduced on the basis of cuckoo algorithm to optimize the extended search space of model parameters and the model solution. The simulation results show that the model can accurately and comprehensively reflect the law of electric sales and improve the accuracy of sales prediction.

关键词： cuckoo algorithm electricity power market least squares support vector machine RBF neural network

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Research on Lifetime Prediction-based Recharging Scheme in Rechargeable WSNs

Research on Lifetime Prediction-based Recharging Scheme in R...

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IEEE/IFIP Network Operations and Management Symposium (NOMS)

作者： Yang, Yang Li, He Qiu, Xuesong Guo, Shaoyong Zeng, XiaoXiao Zhao, Kang Xin, Haoran Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781538634165

In order to reduce the cost and energy consumption in wireless sensor network's charging process, this paper proposes a Recharging Scheme based on Lifetime Prediction (RSLP) for wireless rechargeable sensor networks. First of all, based on the historical quantity of electricity variation sequence of the sensor nodes, the lifetime prediction scheme of the sensor nodes is established;and then, considering the sensor nodes need to be recharged and the Sink nodes chosen by the mobile charger (MC) according to the charging value to establish an undirected complete diagram. A Hamilton charging circuit is established by using the Gene-Expressive cuckoo algorithm to solve the charging problem of the rechargeable sensor networks. The simulation experiments show that the proposed algorithm can improve charging efficiency and reduce the mobile energy consumption.

关键词： lifetime prediction cuckoo algorithm recharging Hamilton circuit

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Parameter Tuning of a PID Controller with Reactive Bio-Inspired for PMSM

Parameter Tuning of a PID Controller with Reactive Bio-Inspi...

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IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)

作者： Aguilar-Mejia, Omar Minor-Popocalt, Hertwin Tapia-Olvera, Ruben Univ Popular Autonoma Estado Puebla Dept Posgrad Ingn Puebla Mexico Univ Nacl Autonoma Mexico Dept Energia Elect Mexico City 04510 DF Mexico

ISBN: (纸本)9781538659359

In this paper, a speed control of permanent magnet synchronous motor using bat algorithm, whale algorithm and cuckoo search algorithm is presented. Time domain specification of the speed response such as rise time, peak overshoot, undershoot, recovery time, settling time and steady state error is obtained and compared for the considered controllers. In addition, gains tuning of PI controller's is optimized using the mentioned three optimization algorithms. In order to validate the effectiveness of the proposed controller, simulation is performed under variations of load condition and different values for set speed of the permanent magnet synchronous motor.

关键词： Bat algorithm whale algorithm cuckoo algorithm Permanent Magnet Synchronous Motor PI controller

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Credit Risk Assessment of P2P Lending Borrowers based on SVM

Credit Risk Assessment of P2P Lending Borrowers based on SVM

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International Conference on Business, Economics, Management Science

作者： Wenjing Tao Dan Chang School of Economic and Management Beijing Jiaotong University

ISBN: (纸本)9781510888050

With the development of Internet finance, peer to peer online(P2 P) lending, which makes a win-win situation between lenders and borrowers, has become one of the most popular means of Internet finance in China. However, problem platforms and borrower default events have also occurred frequently with an explosive-speed growth of P2 P online lending. Reducing credit risk of P2 P lending borrowers still holds the key to the steady development of P2 P online lending platforms. The results show that the SVM model based on cuckoo algorithm to optimize the parameter has a better classification accuracy. This model can be used to judge the potential credit risk of P2 P lending borrowers and provides a theoretical basis for the risk management of Internet financial institutions at the same time.

关键词： P2P Online lending Credit risk SVM Parameter Optimization cuckoo algorithm

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Research on Task Allocation Based on Quantum Particle Swarm and Hybrid algorithm of cuckoo

Research on Task Allocation Based on Quantum Particle Swarm ...

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2nd International Conference on Artificial Intelligence and Engineering Applications(AIEA 2017)

作者： YUNZHI ZHANG GANG WANG TAO LI School of Air and Missile Defense Air Force Engineering University

Aiming at the tracking task and interception task in air defense antimissile operations,a mathematical model of cooperative combat assignment is *** using the global searching ability of the cuckoo algorithm and the fast and efficient local search ability of the quantum particle swarm algorithm,the framework and process of hybrid optimization,based on quantum particle swarm optimization and the cuckoo,are ***,an example is given to demonstrate the rationality of the task allocation model and the effectiveness and superiority of the proposed *** provides a method and reference for solving the problem of air defense antimissile assignment.

关键词： cuckoo algorithm Quantum particle swarm algorithm Task allocation

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An Intelligence Scheduling algorithm for Multi-crane in the Steel Product Warehouse

An Intelligence Scheduling Algorithm for Multi-crane in the ...

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24th IEEE International Conference on Automation and Computing (ICAC) - Improving Productivity through Automation and Computing Newcastle

作者： Lei Zhao-ming Wang Peng-Cheng Liao Wen-zhe Zhao Fan Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China

ISBN: (纸本)9781862203426

A scheduling model with the least total time was proposed to deal with the collision avoidance of multi-crane scheduling with the transitive priority changing rules in the steel product warehouse. The cuckoo search algorithm ( CS) was applied to optimize multi-crane scheduling. In order to get a better performance, an improved CS (ICS) was provided by adding adaptive dynamic steps, dynamically changing the discovery probability and the local optimal solution mutation mechanism. The ICS has the characteristics of fast convergence, high solution precision and strong ability to jump out of the local optimum. The provided algorithm was validated by the simulation example.

关键词： intelligence scheduling algorithm cuckoo algorithm transitive priority changing rules crane collision

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