Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexi...Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.展开更多
Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In...Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In recent years,meta-heuristic algorithms have been widely used in FS problems,so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization(HBCSSDBO)algorithm is proposed in this paper to improve the effect of FS.In this hybrid algorithm,the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem.By combining the K nearest neighbor(KNN)classifier,the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI(University of California,Irvine)datasets.Seven evaluation metrics such as average adaptation,average prediction accuracy,and average running time are chosen to judge and compare the algorithms.The selected dataset is also discussed by categorizing it into three dimensions:high,medium,and low dimensions.Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy,shows better optimization performance.In addition,the results of statistical tests confirm the significant validity of the method.展开更多
针对传统空气质量预测模型收敛速度慢,精度低的问题,提出一种基于变分模态分解(variational mode decomposition,VMD)和蜣螂优化算法(dung beetle optimizer,DBO)优化长短期记忆网络(long short term memory,LSTM)的预测模型。首先,针对...针对传统空气质量预测模型收敛速度慢,精度低的问题,提出一种基于变分模态分解(variational mode decomposition,VMD)和蜣螂优化算法(dung beetle optimizer,DBO)优化长短期记忆网络(long short term memory,LSTM)的预测模型。首先,针对AQI原始数据具有大量噪声的问题,使用VMD方法对非平稳信号进行模态分解以降低噪声对预测结果的影响从而获得多个不同特征的模态分量;其次,针对LSTM靠人工经验调参存在一定局限性,利用DBO算法对LSTM模型参数进行优化;最后,对分解后的各个子序列使用LSTM模型预测,将各个子序列进行叠加得到最后的预测结果。实验结果表明,VMD对非平稳数据的分解有助于提高预测精度,VMD-DBO-LSTM模型的性能较其他模型均有不同程度的提高,该模型预测的均方根误差为4.73μg/m^(3),平均绝对误差为3.61μg/m^(3),拟合度达到了97.8%。展开更多
城市无人机渗透作战中使用智能无人机执行隐蔽穿插、渗入和目标定位等任务,但其面临着城市环境复杂、空域限制等挑战。为了解决城市渗透背景下无人机的路径规划难题,提出了一种ATD-DBO(Adaptive T Distribution-Dung Beetle Optimizer)...城市无人机渗透作战中使用智能无人机执行隐蔽穿插、渗入和目标定位等任务,但其面临着城市环境复杂、空域限制等挑战。为了解决城市渗透背景下无人机的路径规划难题,提出了一种ATD-DBO(Adaptive T Distribution-Dung Beetle Optimizer)驱动的无人机在不规则区域的渗透路径规划算法。首先,提出融合城市建筑物分布、岗哨位置以及无人机特性的无人机城市渗透模型。其次,提出了虫口混沌映射初始化种群、自适应t分布和动态变异策略扰动蜣螂位置和将非精英个体进行二次变异的ATD-DBO算法。最后,提出了一种融合城市实战不规则区域场景和打击意图的快速突进模型。实验证明,算法规划出的路径在有效避开岗哨位置的同时能够确保路径较短。展开更多
基金supported by the National Natural Science Foundation of China under Grant 61602162the Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.
文摘Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.
基金This research was funded by the Short-Term Electrical Load Forecasting Based on Feature Selection and optimized LSTM with DBO which is the Fundamental Scientific Research Project of Liaoning Provincial Department of Education(JYTMS20230189)the Application of Hybrid Grey Wolf Algorithm in Job Shop Scheduling Problem of the Research Support Plan for Introducing High-Level Talents to Shenyang Ligong University(No.1010147001131).
文摘Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In recent years,meta-heuristic algorithms have been widely used in FS problems,so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization(HBCSSDBO)algorithm is proposed in this paper to improve the effect of FS.In this hybrid algorithm,the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem.By combining the K nearest neighbor(KNN)classifier,the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI(University of California,Irvine)datasets.Seven evaluation metrics such as average adaptation,average prediction accuracy,and average running time are chosen to judge and compare the algorithms.The selected dataset is also discussed by categorizing it into three dimensions:high,medium,and low dimensions.Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy,shows better optimization performance.In addition,the results of statistical tests confirm the significant validity of the method.
文摘针对传统空气质量预测模型收敛速度慢,精度低的问题,提出一种基于变分模态分解(variational mode decomposition,VMD)和蜣螂优化算法(dung beetle optimizer,DBO)优化长短期记忆网络(long short term memory,LSTM)的预测模型。首先,针对AQI原始数据具有大量噪声的问题,使用VMD方法对非平稳信号进行模态分解以降低噪声对预测结果的影响从而获得多个不同特征的模态分量;其次,针对LSTM靠人工经验调参存在一定局限性,利用DBO算法对LSTM模型参数进行优化;最后,对分解后的各个子序列使用LSTM模型预测,将各个子序列进行叠加得到最后的预测结果。实验结果表明,VMD对非平稳数据的分解有助于提高预测精度,VMD-DBO-LSTM模型的性能较其他模型均有不同程度的提高,该模型预测的均方根误差为4.73μg/m^(3),平均绝对误差为3.61μg/m^(3),拟合度达到了97.8%。
文摘城市无人机渗透作战中使用智能无人机执行隐蔽穿插、渗入和目标定位等任务,但其面临着城市环境复杂、空域限制等挑战。为了解决城市渗透背景下无人机的路径规划难题,提出了一种ATD-DBO(Adaptive T Distribution-Dung Beetle Optimizer)驱动的无人机在不规则区域的渗透路径规划算法。首先,提出融合城市建筑物分布、岗哨位置以及无人机特性的无人机城市渗透模型。其次,提出了虫口混沌映射初始化种群、自适应t分布和动态变异策略扰动蜣螂位置和将非精英个体进行二次变异的ATD-DBO算法。最后,提出了一种融合城市实战不规则区域场景和打击意图的快速突进模型。实验证明,算法规划出的路径在有效避开岗哨位置的同时能够确保路径较短。
