Internet of things(IOT)possess cultural,commercial and social effect in life in the future.The nodes which are participating in IOT network are basi-cally attracted by the cyber-attack targets.Attack and identification...Internet of things(IOT)possess cultural,commercial and social effect in life in the future.The nodes which are participating in IOT network are basi-cally attracted by the cyber-attack targets.Attack and identification of anomalies in IoT infrastructure is a growing problem in the IoT domain.Machine Learning Based Ensemble Intrusion Detection(MLEID)method is applied in order to resolve the drawback by minimizing malicious actions in related botnet attacks on Message Queue Telemetry Transport(MQTT)and Hyper-Text Transfer Proto-col(HTTP)protocols.The proposed work has two significant contributions which are a selection of features and detection of attacks.New features are chosen from Improved Ant Colony Optimization(IACO)in the feature selection,and then the detection of attacks is carried out based on a combination of their possible proper-ties.The IACO approach is focused on defining the attacker’s important features against HTTP and MQTT.In the IACO algorithm,the constant factor is calculated against HTTP and MQTT based on the mean function for each element.Attack detection,the performance of several machine learning models are Distance Deci-sion Tree(DDT),Adaptive Neuro-Fuzzy Inference System(ANFIS)and Mahala-nobis Distance Support Vector Machine(MDSVM)were compared with predicting accurate attacks on the IoT network.The outcomes of these classifiers are combined into the ensemble model.The proposed MLEID strategy has effec-tively established malicious incidents.The UNSW-NB15 dataset is used to test the MLEID technique using data from simulated IoT sensors.Besides,the pro-posed MLEID technique has a greater detection rate and an inferior rate of false-positive compared to other conventional techniques.展开更多
Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a ...Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.展开更多
Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an ...Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an improved ant colony optimization(IACO) algorithm.Firstly,a problem domain describing buffer allocation is structured.Then a mathematical programming model is established with an objective of maximizing throughput rate of the production line.On the basis of the descriptions mentioned above,combining with a two-opt strategy and an acceptance probability rule,an IACO algorithm is built to solve the BAP.Finally,the simulation experiments are designed to evaluate the proposed algorithm.The results indicate that the IACO algorithm is valid and practical.展开更多
文摘Internet of things(IOT)possess cultural,commercial and social effect in life in the future.The nodes which are participating in IOT network are basi-cally attracted by the cyber-attack targets.Attack and identification of anomalies in IoT infrastructure is a growing problem in the IoT domain.Machine Learning Based Ensemble Intrusion Detection(MLEID)method is applied in order to resolve the drawback by minimizing malicious actions in related botnet attacks on Message Queue Telemetry Transport(MQTT)and Hyper-Text Transfer Proto-col(HTTP)protocols.The proposed work has two significant contributions which are a selection of features and detection of attacks.New features are chosen from Improved Ant Colony Optimization(IACO)in the feature selection,and then the detection of attacks is carried out based on a combination of their possible proper-ties.The IACO approach is focused on defining the attacker’s important features against HTTP and MQTT.In the IACO algorithm,the constant factor is calculated against HTTP and MQTT based on the mean function for each element.Attack detection,the performance of several machine learning models are Distance Deci-sion Tree(DDT),Adaptive Neuro-Fuzzy Inference System(ANFIS)and Mahala-nobis Distance Support Vector Machine(MDSVM)were compared with predicting accurate attacks on the IoT network.The outcomes of these classifiers are combined into the ensemble model.The proposed MLEID strategy has effec-tively established malicious incidents.The UNSW-NB15 dataset is used to test the MLEID technique using data from simulated IoT sensors.Besides,the pro-posed MLEID technique has a greater detection rate and an inferior rate of false-positive compared to other conventional techniques.
基金The National Natural Science Foundation of China(No.61074147)the Natural Science Foundation of Guangdong Province(No.S2011010005059)+2 种基金the Foundation of Enterprise-University-Research Institute Cooperation from Guangdong Province and Ministry of Education of China(No.2012B091000171,2011B090400460)the Science and Technology Program of Guangdong Province(No.2012B050600028)the Science and Technology Program of Huadu District,Guangzhou(No.HD14ZD001)
文摘Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.
基金Supported by the National Natural Science Foundation of China(No.61273035,71471135)
文摘Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an improved ant colony optimization(IACO) algorithm.Firstly,a problem domain describing buffer allocation is structured.Then a mathematical programming model is established with an objective of maximizing throughput rate of the production line.On the basis of the descriptions mentioned above,combining with a two-opt strategy and an acceptance probability rule,an IACO algorithm is built to solve the BAP.Finally,the simulation experiments are designed to evaluate the proposed algorithm.The results indicate that the IACO algorithm is valid and practical.
