Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation inform...Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts.Thus,this paper introduces the probabilistic linguistic term sets(PLTSs)to model the evaluation information of experts.Meanwhile,we propose a probabilistic linguistic multi-criteria decision-making(PL-MCDM)method to solve the information security assessment problem of ICSs.Firstly,we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods.Secondly,we use the Best Worst Method(BWM)method and Criteria Importance Through Inter-criteria Correlation(CRITIC)method to obtain the subjective weights and objective weights,which are used to derive the combined weights.Thirdly,we use the subscript equivalence distance measure method and the combined weight method to improve the probabilistic linguistic Visekriterijumska Optimizacija I Kompromisno Resenje(PL-VIKOR)method.Finally,we apply the proposed method to solve the information security assessment problem of ICSs.When comparing with the existing methods such as the probabilistic linguistic Tomada deDecisão Iterativa Multicritério(PL-TODIM)method and probabilistic linguistic Technique for Order Preference by Similarity to Ideal Solution(PL-TOPSIS)method,the case example shows that the proposed method can provide more reasonable ranking results.By evaluating and ranking the information security level of different ICSs,managers can identify problems in time and guide their work better.展开更多
Programmable Logic Controllers(PLC),core of industrial control systems,is widely used in industrial control systems.The security of PLC is the key to the security of industrial control systems.Nowadays,a large number ...Programmable Logic Controllers(PLC),core of industrial control systems,is widely used in industrial control systems.The security of PLC is the key to the security of industrial control systems.Nowadays,a large number of industrial control systems are connected to the Internet which exposes the PLC equipment to the Internet,and thus raising security concerns.First of all,we introduce the basic principle of PLC in this paper.Then we analyze the PLC code security,firmware security,network security,virus vulnerability and Modbus communication protocol by reviewing the previous related work.Finally,we make a summary of the current security protection methods.展开更多
Gradual increase in the number of successful attacks against Industrial Control Systems(ICS)has led to an urgent need to create defense mechanisms for accurate and timely detection of the resulting process anomalies.T...Gradual increase in the number of successful attacks against Industrial Control Systems(ICS)has led to an urgent need to create defense mechanisms for accurate and timely detection of the resulting process anomalies.Towards this end,a class of anomaly detectors,created using data-centric approaches,are gaining attention.Using machine learning algorithms such approaches can automatically learn the process dynamics and control strategies deployed in an ICS.The use of these approaches leads to relatively easier and faster creation of anomaly detectors compared to the use of design-centric approaches that are based on plant physics and design.Despite the advantages,there exist significant challenges and implementation issues in the creation and deployment of detectors generated using machine learning for city-scale plants.In this work,we enumerate and discuss such challenges.Also presented is a series of lessons learned in our attempt to meet these challenges in an operational plant.展开更多
In this paper,we propose a intrusion detection algorithm based on auto-encoder and three-way decisions(AE-3WD)for industrial control networks,aiming at the security problem of industrial control network.The ideology o...In this paper,we propose a intrusion detection algorithm based on auto-encoder and three-way decisions(AE-3WD)for industrial control networks,aiming at the security problem of industrial control network.The ideology of deep learning is similar to the idea of intrusion detection.Deep learning is a kind of intelligent algorithm and has the ability of automatically learning.It uses self-learning to enhance the experience and dynamic classification capabilities.We use deep learning to improve the intrusion detection rate and reduce the false alarm rate through learning,a denoising AutoEncoder and three-way decisions intrusion detection method AE-3WD is proposed to improve intrusion detection accuracy.In the processing,deep learning AutoEncoder is used to extract the features of high-dimensional data by combining the coefficient penalty and reconstruction loss function of the encode layer during the training mode.A multi-feature space can be constructed by multiple feature extractions from AutoEncoder,and then a decision for intrusion behavior or normal behavior is made by three-way decisions.NSL-KDD data sets are used to the experiments.The experiment results prove that our proposed method can extract meaningful features and effectively improve the performance of intrusion detection.展开更多
文摘Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts.Thus,this paper introduces the probabilistic linguistic term sets(PLTSs)to model the evaluation information of experts.Meanwhile,we propose a probabilistic linguistic multi-criteria decision-making(PL-MCDM)method to solve the information security assessment problem of ICSs.Firstly,we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods.Secondly,we use the Best Worst Method(BWM)method and Criteria Importance Through Inter-criteria Correlation(CRITIC)method to obtain the subjective weights and objective weights,which are used to derive the combined weights.Thirdly,we use the subscript equivalence distance measure method and the combined weight method to improve the probabilistic linguistic Visekriterijumska Optimizacija I Kompromisno Resenje(PL-VIKOR)method.Finally,we apply the proposed method to solve the information security assessment problem of ICSs.When comparing with the existing methods such as the probabilistic linguistic Tomada deDecisão Iterativa Multicritério(PL-TODIM)method and probabilistic linguistic Technique for Order Preference by Similarity to Ideal Solution(PL-TOPSIS)method,the case example shows that the proposed method can provide more reasonable ranking results.By evaluating and ranking the information security level of different ICSs,managers can identify problems in time and guide their work better.
基金This work is funded by the National Key Research and Development Plan(Grant No.2018YFB0803504)the National Natural Science Foundation of China(Nos.61702223,61702220,61871140,U1636215)the Opening Project of Shanghai Trusted Industrial Control Platform.
文摘Programmable Logic Controllers(PLC),core of industrial control systems,is widely used in industrial control systems.The security of PLC is the key to the security of industrial control systems.Nowadays,a large number of industrial control systems are connected to the Internet which exposes the PLC equipment to the Internet,and thus raising security concerns.First of all,we introduce the basic principle of PLC in this paper.Then we analyze the PLC code security,firmware security,network security,virus vulnerability and Modbus communication protocol by reviewing the previous related work.Finally,we make a summary of the current security protection methods.
基金the National Research Foundation(NRF),Prime Minister’s Office,Singapore,under its National Cybersecurity R&D Programme(Award No.NRF2016NCR-NCR002-023 and NRF2018NCR-NSOE005-0001)administered by the National Cybersecurity R&D Directorate.
文摘Gradual increase in the number of successful attacks against Industrial Control Systems(ICS)has led to an urgent need to create defense mechanisms for accurate and timely detection of the resulting process anomalies.Towards this end,a class of anomaly detectors,created using data-centric approaches,are gaining attention.Using machine learning algorithms such approaches can automatically learn the process dynamics and control strategies deployed in an ICS.The use of these approaches leads to relatively easier and faster creation of anomaly detectors compared to the use of design-centric approaches that are based on plant physics and design.Despite the advantages,there exist significant challenges and implementation issues in the creation and deployment of detectors generated using machine learning for city-scale plants.In this work,we enumerate and discuss such challenges.Also presented is a series of lessons learned in our attempt to meet these challenges in an operational plant.
基金supported by National Nature Science Foundation of China (Grant No.61471182)Postgraduate Research&Practice Innovation Program of Jiangsu Province (Grant No.KYCX20_2993)Jiangsu postgraduate research innovation project (SJCX18_0784)。
文摘In this paper,we propose a intrusion detection algorithm based on auto-encoder and three-way decisions(AE-3WD)for industrial control networks,aiming at the security problem of industrial control network.The ideology of deep learning is similar to the idea of intrusion detection.Deep learning is a kind of intelligent algorithm and has the ability of automatically learning.It uses self-learning to enhance the experience and dynamic classification capabilities.We use deep learning to improve the intrusion detection rate and reduce the false alarm rate through learning,a denoising AutoEncoder and three-way decisions intrusion detection method AE-3WD is proposed to improve intrusion detection accuracy.In the processing,deep learning AutoEncoder is used to extract the features of high-dimensional data by combining the coefficient penalty and reconstruction loss function of the encode layer during the training mode.A multi-feature space can be constructed by multiple feature extractions from AutoEncoder,and then a decision for intrusion behavior or normal behavior is made by three-way decisions.NSL-KDD data sets are used to the experiments.The experiment results prove that our proposed method can extract meaningful features and effectively improve the performance of intrusion detection.
