Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
再生器是溶液除湿系统中的关键部件之一,提高再生器综合性能是解决极端热湿气候区高湿问题的有效手段。将指标相关法(CRITIC,Criteria Importance Through Intercriteria Correlation)和优劣解距离法(TOPSIS,Technique for Order Prefer...再生器是溶液除湿系统中的关键部件之一,提高再生器综合性能是解决极端热湿气候区高湿问题的有效手段。将指标相关法(CRITIC,Criteria Importance Through Intercriteria Correlation)和优劣解距离法(TOPSIS,Technique for Order Preference by Similarity to Ideal Solution)联合,建立CRITIC-TOPSIS模型,用于再生器运行入口参数(空气质量流量、溶液质量流量、温度)的选择评价。以永暑岛礁地区为例,通过再生器处理模型得到基础数据以及4个指标的评价体系,采用CRITIC法求得权重后使用TOPSIS法对220种运行入口参数的组合方案进行评价得到最优解。结果表明,空气质量流量为0.1 kg/s、溶液质量流量为0.3 kg/s、溶液温度为56℃时得分0.885,为永暑地区再生器综合性能最优解;再生量指标的权重为0.4,可作为永暑地区再生器综合性能的代表性指标。展开更多
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
文摘再生器是溶液除湿系统中的关键部件之一,提高再生器综合性能是解决极端热湿气候区高湿问题的有效手段。将指标相关法(CRITIC,Criteria Importance Through Intercriteria Correlation)和优劣解距离法(TOPSIS,Technique for Order Preference by Similarity to Ideal Solution)联合,建立CRITIC-TOPSIS模型,用于再生器运行入口参数(空气质量流量、溶液质量流量、温度)的选择评价。以永暑岛礁地区为例,通过再生器处理模型得到基础数据以及4个指标的评价体系,采用CRITIC法求得权重后使用TOPSIS法对220种运行入口参数的组合方案进行评价得到最优解。结果表明,空气质量流量为0.1 kg/s、溶液质量流量为0.3 kg/s、溶液温度为56℃时得分0.885,为永暑地区再生器综合性能最优解;再生量指标的权重为0.4,可作为永暑地区再生器综合性能的代表性指标。
