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LDDoS Attack Detection by Using Ant Colony optimization algorithms

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2016年第4期32卷 995-1020页

作者： Chen, Hsia-Hsiang Huang, Shih-Kun Natl Chiao Tung Univ Dept Comp Sci Hsinchu 300 Taiwan Natl Chiao Tung Univ Informat Technol Serv Ctr Hsinchu 300 Taiwan

Internet service providers and equipment vendors are subject to cyber threats. One of the most prevalent security threats is the distributed denial of service (DDoS) attack. In a DDoS attack, the attack traffic and attacker's IP address are respectively difficult to detect and trace. This is because attack traffic is similar to regular traffic and the attack is executed by multiple attackers. This study focused on solving the low-rate distributed denial of service (LDDoS) problem;this problem is difficult to detect and trace compared with a DDoS attack. We therefore propose a novel distributed detection and identification ant colony system (DDIACS) framework, which is an ant-colony-optimization based metaheuristic technique, for solving the LDDoS problem. The DDIACS framework comprises three stages, which entail an information heuristic rule, a multiagent algorithm, and a backward and forward search method. Moreover, the DDIACS framework is compliant with the emerging software defined network (SDN) because in this framework, a control plane and data plane are used to monitor and manage the network topology. The proposed framework demonstrates SDN advantages such as enabling networks to exhibit flexibility, fast convergence, and robustness in overcoming complicated multi-attacker problems. In addition, this study investigated the time and space complexity of the DDIACS framework and compared this framework with the swarm optimization algorithm and probabilistic packet marking. This study designed the network topology by using the data set from the DARPA and KDD repository. The simulation results show that the proposed framework resolves the problems in using other algorithms and that the DDIACS framework demonstrates better performance than existing methods;furthermore, the adaptive metaheuristic algorithm outperforms other methods in thwarting an LDDoS attack. The detection rate is about 89% and the accuracy is greater than 83%.

关键词： distributed denial of service (DDoS) low-rate distributed denial of service (LDDoS) ant colony system ant colony optimization (ACO) metaheuristic technique software defined network (SDN) swarm optimization algorithm probabilistic packet marking (PPM)

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Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model

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JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2021年第4期29卷 721-739页

作者： Tamoor, Maria Younas, Irfan Natl Univ Comp & Emerging Sci FAST Sch Comp Lahore Pakistan

Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.

关键词： Image segmentation swarm optimization algorithm active contour model skin lesion segmentation and machine learning

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Scheduling of twin automatic stacking cranes and automated guided vehicle considering buffer mode on automated container terminal

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 193卷

作者： Zhu, Jin Ma, Feiyang Shanghai Maritime Univ Inst Logist Sci & Engn 1550 Haigang Ave Shanghai Peoples R China

To improve the efficiency of automatic stacking crane (ASC) and automated guided vehicle (AGV) scheduling in synchronized import and export mode at automated container terminal (ACT), with the objective of minimizing the maximum ASC completion time and AGV wait time, a twin ASCs and AGV scheduling model is established, which considers both the size of handshake area and buffer, and the location constraints of handshake area. Longest transport time priority (LTTP) and traversal priority (TP) heuristics strategies are designed to adjust the execution order of ASCs to reduce the wait time of ASCs. Based on the comprehensive learning particle swarm optimization algorithm (CLPSO), the discrete coding CLPSO algorithm (CLDPSO) is developed by designed permutation coding for ASC execution order and integer coding for AGV numbering, and the convergence speed is improved by dynamically adjusting the weight parameters. The experimental results show that increasing the handshake area and buffer size can reduce the fitness value by 3.64 % and 5.58 % on average respectively, the selected optimal handshake area location can reduce the fitness value by 3.51 % on average compared with the conventional location, the TP strategy can reduce the waiting time of ASC by up to 16.89 %. Compared to the PSO and DPSO algorithms, the fitness value of CLDPSO can reduce the fitness value by 13.83 % and 7.80 %, and the computation time is reduced by 10.67 % and 7.43 % on average, respectively.

关键词： Scheduling Automated guided vehicles Automated stacking cranes Heuristics strategy Comprehensive learning discrete particle swarm optimization algorithm

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Online surrogate multiobjective optimization algorithm for contaminated groundwater remediation designs

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APPLIED MATHEMATICAL MODELLING 2020年 78卷 519-538页

作者： Jiang, Xue Na, Jin China Univ Geosci Sch Environm Studies Wuhan 430074 Hubei Peoples R China Yangtze Univ Coll Resources & Environm Wuhan 430100 Hubei Peoples R China

This paper proposes an online surrogate model-assisted multiobjective optimization framework to identify optimal remediation strategies for groundwater contaminated with dense non-aqueous phase liquids. The optimization involves three objectives: minimizing the remediation cost and duration and maximizing the contamination removal rate. The proposed framework adopts a multiobjective feasibility-enhanced particle swarm optimization algorithm to solve the optimization model and uses an online surrogate model as a substitute for the time-consuming multiphase flow model for calculating contamination removal rates during the optimization process. The resulting approach allows decision makers to find a balance among the remediation cost, remediation duration and contamination removal rate for remediating contaminated groundwater. The new algorithm is compared with the nondominated sorting genetic algorithm II, which is an extensively applied and well-known algorithm. The results show that the Pareto solutions obtained by the new algorithm have greater diversity and stability than those obtained by the nondominated sorting genetic algorithm II, indicating that the new algorithm is more applicable than the nondominated sorting genetic algorithm II for optimizing remediation strategies for contaminated groundwater. Additionally, the surrogate model and Pareto optimal set obtained by the proposed framework are compared with those of the offline surrogate model-assisted multiobjective optimization framework. The results indicate that the surrogate model accuracy and Pareto front achieved by the proposed framework outperform those of the offline surrogate model-assisted optimization framework. Thus, we conclude that the proposed framework can effectively enhance the surrogate model accuracy and further extend the comprehensive performance of Pareto solutions. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Multiobjective optimization Online surrogate model-assisted framework Multiobjective feasibility-enhanced particle swarm optimization algorithm Groundwater remediation designs Dense non-aqueous phase liquid

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Tetragonal high-pressure phase of InI predicted from first principles

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PHYSICA B-CONDENSED MATTER 2012年第3期407卷 398-402页

作者： Zhang, Meiguang Yan, Haiyan Zhang, Gangtai Wei, Qun Wang, Hui Baoji Univ Arts & Sci Dept Phys & Informat Technol Baoji 721016 Peoples R China Baoji Univ Arts & Sci Dept Chem & Chem Engn Shaanxi Key Lab Phytochem Baoji 721013 Peoples R China Xidian Univ Sch Sci Xian 710071 Peoples R China Jilin Univ Natl Lab Superhard Mat Changchun 130012 Peoples R China

Ab initio particle swarm optimization algorithm for crystal structural prediction was employed to uncover the high-pressure crystal structure of indium iodide (InI). We have predicted one tetragonal high-pressure phase for InI with P4/nmm symmetry, which is energetically much superior to the previously proposed CsCl-type structure. The P4/nmm-InI possesses alternative stacking of double I and In layers. The arrangement of adjacent I and In layers of P4/nmm-InI is similar to that of the CsCI-type structure. The calculated electronic density of states supports a metallic character for this tetragonal phase that is similar to the high-pressure behavior of IIA-VIB families. Furthermore, the phase transition path from the ambient pressure TII-InI -> P4/nmm-InI has been discussed. (C) 2011 Elsevier B.V. All rights reserved.

关键词： InI swarm optimization algorithm High pressure transition First principles

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Brain storm optimization algorithm for solving knowledge spillover problems

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NEURAL COMPUTING & APPLICATIONS 2023年第17期35卷 12247-12260页

作者： Cheng, Shi Zhang, Mingming Ma, Lianbo Lu, Hui Wang, Rui Shi, Yuhui Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Northeastern Univ Coll Software Shenyang 110819 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

The evolutionary game theory aims to simulate different decision strategies in populations of individuals and to determine how the population evolves. Compared to strategies between two agents, such as cooperation or noncooperation, strategies on multiple agents are rather challenging and difficult to be simulated via traditional methods. Particularly, in a knowledge spillover problem (KSP), cooperation strategies among more than hundreds of individuals need to be simulated. At the same time, the brain storm optimization (BSO) algorithm, which is a data-driven and model-driven hybrid paradigm, has the potential to simulate the complex behaviors in a group of simple individuals. In this paper, a modified BSO algorithm has been used to solve KSP from the perspective of evolutionary game theory. Knowledge spillover (KS) is the sharing or exchanging of knowledge resources among individuals. Firstly, the KS and evolutionary game theory were introduced. Then, the KS model and KS optimization problems were built from the evolutionary game perspective. Lastly, the modified BSO algorithms were utilized to solve KS optimization problems. Based on the applications of BSO algorithms for KSP, the properties of different swarm optimization algorithms can be understood better. More efficient algorithms could be designed to solve different real-world evolutionary game problems.

关键词： Brain storm optimization Evolutionary game theory Knowledge spillover model swarm optimization algorithm

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Threshold image segmentation based on improved sparrow search algorithm (Apr, 10.1007/s11042-022-13073-x, 2022)

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第23期81卷 33547-33547页

作者： Wu, Dongmei Yuan, Chengzhi Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Jiangsu Engn Lab IOT Intelligent Robots Nanjing Peoples R China

Threshold segmentation based on swarm intelligence optimization algorithm is a research hotspot in image processing, because of its good segmentation effect and easy implementation. This paper proposes an image threshold segmentation method based on an improved sparrow search algorithm and 2-D maximum entropy method. In the proposed algorithm, the nonlinear inertia weight is introduced into the entrants’ update formula to improve the local exploration ability of the algorithm, and Levy flight is introduced into the vigilant sparrows’ update formula to prevent the algorithm from falling into the local optimal solution in the later stage of iteration. In addition, improved sparrow search algorithm is tested on fifteen benchmark functions. The results represent the merit of the proposed algorithm with respect to other algorithms. Finally, the proposed algorithm is applied to entropy based image segmentation. Experiment results on classical images and medical images show that the proposed method improves the segmentation effect in terms of peak signal-to-noise ratio and feature similarity.

关键词： 2-D histogram Levy flight Maximum entropy Nonlinear weight swarm optimization algorithm Threshold image segmentation

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A Novel Hybrid ABC-PSO algorithm for Effort Estimation of Software Projects Using Agile Methodologies

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JOURNAL OF INTELLIGENT SYSTEMS 2018年第3期27卷 489-506页

作者： Thanh Tung Khuat My Hanh Le Univ Sci & Technol Univ Danang Danang Vietnam

In modern software development processes, software effort estimation plays a crucial role. The success or failure of projects depends greatly on the accuracy of effort estimation and schedule results. Many studies focused on proposing novel models to enhance the accuracy of predicted results;however, the question of accurate estimation of effort has been a challenging issue with regards to researchers and practitioners, especially when it comes to projects using agile methodologies. This study aims at introducing a novel formula based on team velocity and story point factors. The parameters of this formula are then optimized by employing swarm optimization algorithms. We also propose an improved algorithm combining the advantages of the artificial bee colony and particle swarm optimization algorithms. The experimental results indicated that our approaches outperformed methods in other studies in terms of the accuracy of predicted results.

关键词： Software effort estimation agile software development user story particle swarm optimization artificial bee colony swarm optimization algorithm

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Uncertainty analysis-forecasting system based on decomposition-ensemble framework for PM2.5 concentration forecasting in China

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JOURNAL OF FORECASTING 2023年第8期42卷 2027-2044页

作者： Qu, Zongxi Hao, Xiaogang Zhao, Fazhen Niu, Chunhua Lanzhou Univ Sch Management Lanzhou 730000 Peoples R China Gansu Univ Chinese Med Clin Coll Tradit Chinese Med Lanzhou Peoples R China

Practical analysis and forecasting of PM2.5 concentrations is complex and challenging owing to the volatility and non-stationarity of PM2.5 series. Most previous studies mainly focused on deterministic predictions, whereas the uncertainty in the prediction is not considered. In this study, a novel uncertainty analysis-forecasting system comprising distribution function analysis, intelligent deterministic prediction, and interval prediction is designed. Based on the characteristics of PM2.5 series, 16 hybrid models composed of various distribution functions and swarm optimization algorithms are selected to determine the exact PM2.5 distribution. Subsequently, a hybrid deterministic forecasting model based on a novel decomposition-ensemble framework is established for PM2.5 prediction. Regarding uncertainty analysis, interval prediction is established to provide uncertain information required for decision-making based on the optimal distribution functions and deterministic prediction results. PM2.5 concentration series obtained from three cities in China are used to conduct an empirical study. The empirical results show that the proposed system can achieve better prediction results than other comparable models as well as provide meaningful and practical quantification of future PM trends. Hence, the system can provide more constructive suggestions for government administrators and the public.

关键词： artificial intelligence algorithm interval prediction PM2 5 prediction swarm optimization algorithm uncertainty analysis

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Using Chaotic Adaptive PSO-SVM for Heart Disease Diagnosis

Using Chaotic Adaptive PSO-SVM for Heart Disease Diagnosis

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2nd IEEE International Conference on Computer Science and Information Technology

作者： Yang, Haiyan Luo, Qifang Zhou, Yongquan Guangxi Univ Nationalities Coll Math & Comp Sci Nanning 530006 Guangxi Peoples R China

ISBN: (纸本)9781424445189

In this paper, the application of support vector machine (SVM) approach based on the statistics-learning theory of structural risk minimization in heart disease diagnosis. Aiming at the blindness of man made choice of parameter and kernel function of SVM, a chaotic adaptive particle swarm optimization (CAPSO) method is applied to select parameters of SVM in the paper and genetic characteristics of the subset of choices (GA_FSS) is applied to reduce large dimensions and improve greatly the accuracy of classification. The experimental results on heart disease diagnosis problem show that CAPSO-SVM .classifier algorithm is effective and correct.

关键词： Support vector machine (SVM) Chaos optimization swarm optimization algorithm Genetic algorithm (GA) Feature subset selection (FSS)

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