Accurate early classification of elephant flows(elephants)is important for network management and resource optimization.Elephant models,mainly based on the byte count of flows,can always achieve high accuracy,but not ...Accurate early classification of elephant flows(elephants)is important for network management and resource optimization.Elephant models,mainly based on the byte count of flows,can always achieve high accuracy,but not in a time-efficient manner.The time efficiency becomes even worse when the flows to be classified are sampled by flow entry timeout over Software-Defined Networks(SDNs)to achieve a better resource efficiency.This paper addresses this situation by combining co-training and Reinforcement Learning(RL)to enable a closed-loop classification approach that divides the entire classification process into episodes,each involving two elephant models.One predicts elephants and is retrained by a selection of flows automatically labeled online by the other.RL is used to formulate a reward function that estimates the values of the possible actions based on the current states of both models and further adjusts the ratio of flows to be labeled in each phase.Extensive evaluation based on real traffic traces shows that the proposed approach can stably predict elephants using the packets received in the first 10% of their lifetime with an accuracy of over 80%,and using only about 10% more control channel bandwidth than the baseline over the evolved SDNs.展开更多
This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural habitat.FLO draws its inspi...This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural habitat.FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards.The algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases:(i)an exploration phase,which mimics the lizard’s sudden attack on its prey,and(ii)an exploitation phase,which simulates the lizard’s retreat to the treetops after feeding.To assess FLO’s efficacy in addressing optimization problems,its performance is rigorously tested on fifty-two standard benchmark functions.These functions include unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions,as well as the challenging CEC 2017 test suite.FLO’s performance is benchmarked against twelve established metaheuristic algorithms,providing a comprehensive comparative analysis.The simulation results demonstrate that FLO excels in both exploration and exploitation,effectively balancing these two critical aspects throughout the search process.This balanced approach enables FLO to outperform several competing algorithms in numerous test cases.Additionally,FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems,further validating its robustness and versatility in solving real-world optimization challenges.Overall,the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.展开更多
High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency...High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.展开更多
Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and processing.Any kind of malicious or abnormal fu...Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and processing.Any kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire IIoT.Moreover,they can allow malicious software installed on end nodes to penetrate the network.This paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge devices.The proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority voting.Experimental evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.展开更多
As the penetration of intermittent renewable energy resources in microgrids(MGs)continues to grow globally,optimal operation management becomes increasingly crucial due to the variability of these sources.One potentia...As the penetration of intermittent renewable energy resources in microgrids(MGs)continues to grow globally,optimal operation management becomes increasingly crucial due to the variability of these sources.One potential solution to this challenge is the use of demand response(DR)programs,which are practical and relatively low-cost options.However,ensuring the security of MG operation also requires evaluating its flexibility by determining the acceptable boundaries of uncertain variables.Additionally,in real-world operational decision-making problems,there is a simultaneous optimization of multiple objectives,including the maximization of system flexibility and the minimization of system cost.This paper presents a methodology for developing a cost-aware flexibility evaluation method for MGs connected to the upstream grid,which are subject to volatile market prices.The model is based on the feasibility analysis of the uncertain space of wind power generation and load,and it also investigates the level of inflexibility present in the system.The impact of the DR program on the flexibility of MGs is quantified through a case study.The case study confirms the success of the proposed method and underscores the significance of cost modeling in flexibility evaluation problems.展开更多
In the digital era,social media platforms play a crucial role in forming user communities,yet the challenge of protecting user privacy remains paramount.This paper proposes a novel framework for identifying and analyz...In the digital era,social media platforms play a crucial role in forming user communities,yet the challenge of protecting user privacy remains paramount.This paper proposes a novel framework for identifying and analyzing user communities within social media networks,emphasizing privacy protection.In detail,we implement a social media-driven user community finding approach with hashing named MCF to ensure that the extracted information cannot be traced back to specific users,thereby maintaining confidentiality.Finally,we design a set of experiments to verify the effectiveness and efficiency of our proposed MCF approach by comparing it with other existing approaches,demonstrating its effectiveness in community detection while upholding stringent privacy standards.This research contributes to the growing field of social network analysis by providing a balanced solution that respects user privacy while uncovering valuable insights into community dynamics on social media platforms.展开更多
With the integration of distributed generation(DG) into a microgrid, fault detection has become a major task to accomplish. A scheme for microgrid feeder protection based on a newly proposed feature, ΔRP, defined as ...With the integration of distributed generation(DG) into a microgrid, fault detection has become a major task to accomplish. A scheme for microgrid feeder protection based on a newly proposed feature, ΔRP, defined as the ratio of the sum of positive-sequence real power(PSRP) at the two ends of the feeder to the larger of the PSRPs among the two ends, is proposed. If ΔRP is larger than a threshold, an internal fault is detected;otherwise, the fault is external or there is no fault. The proposed scheme is tested in various scenarios, including fault type, fault resistance, fault location, fault inception angle, varying DG penetration levels, and simultaneous, evolving, and composite faults. In addition to this, the proposed scheme offers a robust performance when subjected to noise, synchronization error, changes in sampling frequency, and changes in the topology of a microgrid. The dominancy of the proposed scheme is proven by a comprehensive comparative study with various available recent schemes. Test results on the IEEE 13-bus network indicate the viability of the proposed protection scheme for a microgrid. Finally, the proposed scheme has been validated on a real-time simulator.展开更多
基金supported by the National Natural Science Foundation of China(61962016)the Ministry of Science and Technology of China(G2022033002L)+1 种基金National Natural Science Foundation of Guangxi(2022JJA170057)Guangxi Education Department’s Project on Improving the Basic Research Ability of Young and Middleaged Teachers in Universities(2023ky0812,Research on Statistical Network Delay Predictions in Large-scale SDNs).
文摘Accurate early classification of elephant flows(elephants)is important for network management and resource optimization.Elephant models,mainly based on the byte count of flows,can always achieve high accuracy,but not in a time-efficient manner.The time efficiency becomes even worse when the flows to be classified are sampled by flow entry timeout over Software-Defined Networks(SDNs)to achieve a better resource efficiency.This paper addresses this situation by combining co-training and Reinforcement Learning(RL)to enable a closed-loop classification approach that divides the entire classification process into episodes,each involving two elephant models.One predicts elephants and is retrained by a selection of flows automatically labeled online by the other.RL is used to formulate a reward function that estimates the values of the possible actions based on the current states of both models and further adjusts the ratio of flows to be labeled in each phase.Extensive evaluation based on real traffic traces shows that the proposed approach can stably predict elephants using the packets received in the first 10% of their lifetime with an accuracy of over 80%,and using only about 10% more control channel bandwidth than the baseline over the evolved SDNs.
文摘This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural habitat.FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards.The algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases:(i)an exploration phase,which mimics the lizard’s sudden attack on its prey,and(ii)an exploitation phase,which simulates the lizard’s retreat to the treetops after feeding.To assess FLO’s efficacy in addressing optimization problems,its performance is rigorously tested on fifty-two standard benchmark functions.These functions include unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions,as well as the challenging CEC 2017 test suite.FLO’s performance is benchmarked against twelve established metaheuristic algorithms,providing a comprehensive comparative analysis.The simulation results demonstrate that FLO excels in both exploration and exploitation,effectively balancing these two critical aspects throughout the search process.This balanced approach enables FLO to outperform several competing algorithms in numerous test cases.Additionally,FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems,further validating its robustness and versatility in solving real-world optimization challenges.Overall,the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.
基金supported in part by the National Natural Science Foundation of China(62371116 and 62231020)in part by the Science and Technology Project of Hebei Province Education Department(ZD2022164)+2 种基金in part by the Fundamental Research Funds for the Central Universities(N2223031)in part by the Open Research Project of Xidian University(ISN24-08)Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education(Guilin University of Electronic Technology,China,CRKL210203)。
文摘High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.
文摘Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and processing.Any kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire IIoT.Moreover,they can allow malicious software installed on end nodes to penetrate the network.This paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge devices.The proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority voting.Experimental evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.
基金supported by the Estonian Research Council(No.PSG739)co-funded by the European Union and Estonian Research Council via project TEM-TA78.
文摘As the penetration of intermittent renewable energy resources in microgrids(MGs)continues to grow globally,optimal operation management becomes increasingly crucial due to the variability of these sources.One potential solution to this challenge is the use of demand response(DR)programs,which are practical and relatively low-cost options.However,ensuring the security of MG operation also requires evaluating its flexibility by determining the acceptable boundaries of uncertain variables.Additionally,in real-world operational decision-making problems,there is a simultaneous optimization of multiple objectives,including the maximization of system flexibility and the minimization of system cost.This paper presents a methodology for developing a cost-aware flexibility evaluation method for MGs connected to the upstream grid,which are subject to volatile market prices.The model is based on the feasibility analysis of the uncertain space of wind power generation and load,and it also investigates the level of inflexibility present in the system.The impact of the DR program on the flexibility of MGs is quantified through a case study.The case study confirms the success of the proposed method and underscores the significance of cost modeling in flexibility evaluation problems.
基金partially supported by the China Postdoctoral Science Foundation(No.2023M731951).
文摘In the digital era,social media platforms play a crucial role in forming user communities,yet the challenge of protecting user privacy remains paramount.This paper proposes a novel framework for identifying and analyzing user communities within social media networks,emphasizing privacy protection.In detail,we implement a social media-driven user community finding approach with hashing named MCF to ensure that the extracted information cannot be traced back to specific users,thereby maintaining confidentiality.Finally,we design a set of experiments to verify the effectiveness and efficiency of our proposed MCF approach by comparing it with other existing approaches,demonstrating its effectiveness in community detection while upholding stringent privacy standards.This research contributes to the growing field of social network analysis by providing a balanced solution that respects user privacy while uncovering valuable insights into community dynamics on social media platforms.
文摘With the integration of distributed generation(DG) into a microgrid, fault detection has become a major task to accomplish. A scheme for microgrid feeder protection based on a newly proposed feature, ΔRP, defined as the ratio of the sum of positive-sequence real power(PSRP) at the two ends of the feeder to the larger of the PSRPs among the two ends, is proposed. If ΔRP is larger than a threshold, an internal fault is detected;otherwise, the fault is external or there is no fault. The proposed scheme is tested in various scenarios, including fault type, fault resistance, fault location, fault inception angle, varying DG penetration levels, and simultaneous, evolving, and composite faults. In addition to this, the proposed scheme offers a robust performance when subjected to noise, synchronization error, changes in sampling frequency, and changes in the topology of a microgrid. The dominancy of the proposed scheme is proven by a comprehensive comparative study with various available recent schemes. Test results on the IEEE 13-bus network indicate the viability of the proposed protection scheme for a microgrid. Finally, the proposed scheme has been validated on a real-time simulator.
