A New Model for Network Security Situation Assessment of the Industrial Internet

To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge.Second,the evaluation indicators are fused with expert knowledge and the ER algorithm.According to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model parameters.This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion.Moreover,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data.Finally,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method.The research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet systems.It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.

Damage assessment of aircraft wing subjected to blast wave with finite element method and artificial neural network tool

Damage assessment of the wing under blast wave is essential to the vulnerability reduction design of aircraft. This paper introduces a critical relative distance prediction method of aircraft wing damage based on the back-propagation artificial neural network(BP-ANN), which is trained by finite element simulation results. Moreover, the finite element method(FEM) for wing blast damage simulation has been validated by ground explosion tests and further used for damage mode determination and damage characteristics analysis. The analysis results indicate that the wing is more likely to be damaged when the root is struck from vertical directions than others for a small charge. With the increase of TNT equivalent charge, the main damage mode of the wing gradually changes from the local skin tearing to overall structural deformation and the overpressure threshold of wing damage decreases rapidly. Compared to the FEM-based damage assessment, the BP-ANN-based method can predict the wing damage under a random blast wave with an average relative error of 4.78%. The proposed method and conclusions can be used as a reference for damage assessment under blast wave and low-vulnerability design of aircraft structures.

Mass concrete strength assessment method by Sonreb and Core combined method using artificial neural network

The Sonreb and Core (SRC) combined method is proposed to assess the concrete compression strength of mass concrete structures.Artificial neural network is employed together with the SRC combined method to obtain the optimal core number.The artificial neural network is trained based on data from different testing methods.The procedure of using artificial neural network to assess the concrete strength is described.It proves that the SRC combined method is superior in many aspects and artificial the presented neural network has a high efficiency and reliability.The combined method using artificial intelligence is promising in the strength assessment of mass concrete structures such as the dam,the anchor of the suspension bridge,etc.

Construction of Air Quality Evaluation System Based on FCM Algorithm and BP Neural Network

In order to solve the limitations of existing air quality evaluation system, a new air quality evaluation system was established based on FCM, the BP neural network, with the aim to provide scientific bases for the targeted and efficient control of air pollution, formulation of prevention and control strategy, and improvement of living environment. Based on the existing data of 6 air quality indices, the air quality data were reclassified by using FCM algorithm, obtaining the clustering center, which minimized the cost function of non-similar index. Then, the reclassified 6 classes of data were proceeded with BP neural network training and simulation, so as to achieve the purpose of identification, thereby forming a new air quality evaluation system.

A Website Security Risk Assessment Method Based on the I-BAG Model

In order to protect the website and assess the security risk of website, a novel website security risk assessment method is proposed based on the improved Bayesian attack graph(I-BAG) model. First, the Improved Bayesian attack graph model is established, which takes attack benefits and threat factors into consideration. Compared with the existing attack graph models, it can better describe the website's security risk. Then, the improved Bayesian attack graph is constructed with optimized website attack graph, attack benefit nodes, threat factor nodes and the local conditional probability distribution of each node, which is calculated accordingly. Finally, website's attack probability and risk value are calculated on the level of nodes, hosts and the whole website separately. The experimental results demonstrate that the risk evaluating method based on I-BAG model proposed is a effective way for assessing the website security risk.

Handling epistemic uncertainties in PRA using evidential networks

In order to overcome the limitations of traditional methods in uncertainty analysis, a modified Bayesian network(BN), which is called evidence network(EN), was proposed with evidence theory to handle epistemic uncertainty in probabilistic risk assessment(PRA). Fault trees(FTs) and event trees(ETs) were transformed into an EN which is used as a uniform framework to represent accident scenarios. Epistemic uncertainties of basic events in PRA were presented in evidence theory form and propagated through the network. A case study of a highway tunnel risk analysis was discussed to demonstrate the proposed approach. Frequencies of end states are obtained and expressed by belief and plausibility measures. The proposed approach addresses the uncertainties in experts' knowledge and can be easily applied to uncertainty analysis of FTs/ETs that have dependent events.