Situation Assessment Model Research Articles

An Industrial Internet Security Assessment Model Based on a Selectable Confidence Rule Base

To mitigate the impact of network security on the production environment in the industrial internet, this paper proposes a confidence rule-based security assessment model for the industrial internet that uses selective modeling. First, a definition of selective modeling tailored to the characteristics of the industrial internet is provided. Based on this, the assessment process of the Selectable Belief Rule Base (BRB-s) model is introduced. Then, in combination with the Selection covariance matrix adaptive evolution strategy (S-CMA-ES) algorithm, a parameter optimization method for the BRB-s model is designed, which expands the selective constraints on expert knowledge. This model establishes a better unidirectional selection strategy among different subgroups, and while expanding the selection constraints on expert knowledge, it achieves better evaluation results. This effectively addresses the issue of reduced modeling accuracy caused by insufficient data and poor data quality. Finally, the experiments of different evaluation models on industrial data sets are compared, and good results are obtained, which verify the evaluation accuracy of the industrial Internet network security situation assessment model proposed in this paper and the feasibility and effectiveness of the S-CMA-ES optimization algorithm.

Research on Security Situation Assessment and Prediction Model of Network System in Deep Learning Environment

With the development of the Internet, the network environment is increasingly complex, and the problem of network security is increasingly serious. Traditional passive network security technology has been unable to meet people’s current security needs, in this context, network security situation awareness arises at the historic moment. NSSA technology makes the traditional passive security into active security, from the analysis of unilateral elements to the analysis of the overall security. As key technology of situation perception, security situation assessment and prediction can evaluate and predict the network security situation at the overall level, help network security managers understand the overall network security changes and take protective measures in advance when predicting the dangerous state, which has important research significance. This paper mainly studies the situation assessment and prediction technology of network security, and puts forward the improved model and algorithm, which improves accuracy of situation assessment and prediction results. Prediction accuracy reaches 97.86%, and the efficiency of the situation assessment reaches 98.22%.

A network security situation assessment method based on fusion model

This research introduces an innovative network security situation assessment (NSSA) model, designed to overcome the shortcomings in feature extraction quality and efficiency observed in existing methods. At the core of this model is a fusion model (FM), which uniquely combines an attention mechanism with a bi-directional gated recurrent unit (BiGRU). This FM framework is adept at extracting pivotal information pertinent to various cyber threats. It employs the attention mechanism to assign appropriate weights to these crucial features, thereby elevating the model’s precision. The BiGRU, in synergy with newly proposed quantitative indicators, is responsible for generating the final evaluation results, offering a more refined measure of the cybersecurity stance. Comparative threat detection experiments reveal that the FM model exhibits superior performance across multiple evaluation metrics, marking a significant advancement in the field of network security assessment.

Research on Network Security Situation Assessment Model in Decentralized Computing Environment

Abstract In this paper, decentralized computing is used as an entry point to review the latest research results on network security issues through two methods, namely task assignment and hierarchical analysis, and construct a network security posture assessment model based on decentralized computing. A control framework is constructed by utilizing the functional complementarity of continuous authentication and security threat assessment in order to facilitate real-time observation of the security situation of the network and timely elimination of malicious nodes. A quantitative network security threat posture assessment model is constructed through hierarchical analysis to observe the extent of the breach of confidentiality and integrity of network information based on the security threat posture index. The effectiveness of the network security posture assessment model and method proposed in this paper was verified by empirical analysis in a simulated environment. The results show that after the simulated attack lasts for 12 minutes, the network security risk index measured by the assessment model in the test cascade case becomes larger with the intensification of the network attack, and the risk index value is up to 5.5. In summary, the network security posture assessment model based on decentralized computing designed in this paper can quickly reflect the changes in the security status of the underlying network and provide administrators with the current security status of the network in a macroscopic way.

A novel network security situation assessment model based on multiple strategies whale optimization algorithm and bidirectional GRU.

The rapid development of the internet has brought about a comprehensive transformation in human life. However, the challenges of cybersecurity are becoming increasingly severe, necessitating the implementation of effective security mechanisms. Cybersecurity situational awareness can effectively assess the network status, facilitating the formulation of better cybersecurity defense strategies. However, due to the low accuracy of existing situational assessment methods, situational assessment remains a challenge. In this study, a new situational assessment method, MSWOA-BiGRU, combining optimization algorithms and temporal neural networks, was proposed. Firstly, a scientific indicator system proposed in this research is used to calculate the values of each indicator. Then, the Analytic Hierarchy Process is used to derive the actual situation values, which serve as labels. Taking into account the temporal nature of network traffic, the BiGRU model is utilized for cybersecurity situational assessment. After integrating time-related features and network traffic characteristics, the situational assessment value is obtained. During the evaluation process, a whale optimization algorithm (MSWOA) improved with a mix of strategies proposed in this study was employed to optimize the model. The performance of the proposed MSWOA-BiGRU model was evaluated on publicly available real network security datasets. Experimental results indicate that compared to traditional optimization algorithms, the optimization performance of MSWOA has seen significant enhancement. Furthermore, MSWOA-BiGRU demonstrates superior performance in cybersecurity situational assessment compared to existing evaluation methods.

Historical carbon peak situation and its driving mechanisms in the commercial building sector in China

Evaluating the peak situation of commercial building carbon emissions (CBCE) at the provincial level and revealing their differentiated driving mechanisms are extremely significant for achieving the national “Dual carbon” target. In this study, a new provincial-level carbon peak situation assessment model is constructed to evaluate the historical CBCE peak situations in China by integrating conditional judgement function, Mann-Kendall trend test, and Tapio decoupling model. Furthermore, the STIRPAT model and grouped regression model are combined to investigate the driving mechanisms on CBCE under different carbon peaking situations. Results showed that the CBCE in Beijing have reached the peak, and Shandong and Inner Mongolia have entered the peak potential period. Beijing achieved the decoupling of CBCE from value added of tertiary sector and floor space per capita. The most important factors leading to different peak situations of CBCE are commercial floor space per capita and energy intensity. Urban population, industrial structure, value added of tertiary industry per capita and energy structure show different degrees of influence on different peak stages of CBCE. This study provides a general methodology for evaluating the carbon peak situation and its driving mechanism. It sets the foundation for the formulation of the differentiated carbon peak pathway and provides a reference for the carbon emission assessment of other countries with regional carbon peak differences in the world.

A Dynamic Air Combat Situation Assessment Model Based on Situation Knowledge Extraction and Weight Optimization

Air combat situation assessment is the basis of target assignment and maneuver decisions. The current air combat situation assessment models, whether nonparametric or parametric, ignore the continuity and timing of situation changes, making the situation assessment results lose tactical significance. Aimed at the shortcomings of current air combat situation assessment, a dynamic air combat situation assessment model based on situation knowledge extraction and weight optimization was proposed by combining a multiple regression model of hidden logic process, a weight optimization model based on grey prospect theory, a weight mapping model based on autoencoder and extreme learning machine (AE-ELM) and an air combat situation characteristic parameter prediction model based on dynamic weight online extreme learning machine (DWOSELM). Firstly, considering the timing and continuity of air combat situation change, a hidden logic process multiple regression model was introduced to realize the segmentation of air combat situation time series data and the extraction of air combat situation primitives. Secondly, the weight optimization method based on grey prospect theory was used to obtain the weight of the evaluation index under different air combat situations. On this basis, the dynamic mapping model between air combat situation characteristic parameters and the weight of index was constructed by using AE-ELM. Then, the dynamic weighted extreme learning machine was used to build the target maneuver trajectory prediction model, and the future position information of the target was predicted. On this basis, the future situation information between the enemy and us was obtained. Finally, the time weight calculation model based on normal cumulative distribution was used to determine the importance of the situation at each time. The situation information at multiple times in the air combat process was fused to obtain the comprehensive air combat situation assessment results at the current time. The simulation results show that the model can fully exploit the influence of historical information, effectively integrate the air combat situation information at multiple moments, and generate the air combat situation assessment results with practical tactical significance according to the individual differences of different pilots.

Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning

With the rapid development of the air transportation industry, the air traffic situation is becoming more and more complicated. Determining the situation of air traffic is of great significance to ensure the safety and smoothness of air traffic. The strong subjectivity of assessment criteria, inaccurate assessment results and weak systemic assessment method are the main problems in air traffic situation assessment research. The aim of our research is to present an objective and accurate situation assessment method for air traffic systems. The paper presents a model to assess air traffic situation based on the complex network theory and ensemble learning. The air traffic weighted network model was introduced to systematically describe the real state of an air traffic system. Assessment criteria based on the complex network analysis method can systematically reflect the operational state of an air traffic weighted network system. We transformed the air traffic situation assessment into a binary classification, which makes situation assessment objective and accurate. Ensemble learning was introduced to improve the classification accuracy, which further improves the accuracy of the situation assessment model. The model was trained and tested on the dataset of the East China air traffic weighted network in 2019. Its average classification accuracy is 0.98. The recall and precision rates both exceed 0.95. Experiments have confirmed that the situation assessment model can accurately output air traffic situation value and situation level. Furthermore, the assessment results are consistent with the real operational situation of the air traffic in East China.

UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

The unmanned combat aerial vehicle (UCAV) is a research hot issue in the world, and the situation assessment is an important part of it. To overcome shortcomings of the existing situation assessment methods, such as low accuracy and strong dependence on prior knowledge, a data-driven situation assessment method is proposed. The clustering and classification are combined, the former is used to mine situational knowledge, and the latter is used to realize rapid assessment. Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features. A convolution success-history based adaptive differential evolution with linear population size reduction-means (C-LSHADE-Means) algorithm is proposed. The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics. The LSHADE algorithm is used to initialize the center of the mean clustering, which overcomes the defect of initialization sensitivity. Comparing experiment with the seven clustering algorithms is done on the UCI data set, through four clustering indexes, and it proves that the method proposed in this paper has better clustering performance. A situation assessment model based on stacked autoencoder and learning vector quantization (SAE-LVQ) network is constructed, and it uses SAE to reconstruct air combat data features, and uses the self-competition layer of the LVQ to achieve efficient classification. Compared with the five kinds of assessments models, the SAE-LVQ model has the highest accuracy. Finally, three kinds of confrontation processes from air combat maneuvering instrumentation (ACMI) are selected, and the model in this paper is used for situation assessment. The assessment results are in line with the actual situation.

Enhanced Internet of Things Security Situation Assessment Model with Feature Optimization and Improved SSA-LightGBM

In this paper, an improved Internet of Things (IoT) network security situation assessment model is designed to solve the problems arising from the existing IoT network security situation assessment approach regarding feature extraction, validity, and accuracy. Firstly, raw data are dimensionally reduced using independent component analysis (ICA), and the weights of all features are calculated and fused using the maximum relevance minimum redundancy (mRMR) algorithm, Spearman’s rank correlation coefficient, and extreme gradient boosting (XGBoost) feature importance method to filter out the optimal subset of features. Piecewise chaotic mapping and firefly perturbation strategies are then used to optimize the sparrow search algorithm (SSA) to achieve fast convergence and prevent getting trapped in local optima, and then the optimized algorithm is used to improve the light gradient boosting machine (LightGBM) algorithm. Finally, the improved LightGBM method is used for training to calculate situation values based on a threat impact to assess the IoT network security situation. The research findings reveal that the model attained an evaluation accuracy of 99.34%, sustained a mean square error at the 0.00001 level, and reached its optimum convergence value by the 45th iteration with the fastest convergence speed. This enables the model to more effectively evaluate the IoT network security status.

Coordinated control of longitudinal and lateral movements considering dynamics for distributed drive electric vehicle platoon on curved roads

The control goal of the vehicle platoon is to maintain the same speed and desired distance. Most current studies are based on simplified vehicle models, and the leader’s state is also rarely considered. However, under complex working conditions, such as low adhesion or curves, the lateral stability of the platoon will be difficult to guarantee, and tracking errors of desired speed and spacing may further increase. To solve the above problems, a new hierarchical coordinated control strategy is proposed. Taking distributed drive electric vehicles (DDEVs) as research objects, the upper control level establishes a stability situation assessment model according to the vehicle’s dynamic characteristics. At the medium control level, variable weight model predictive control (MPC) coordinates conflicts between longitudinal tracking and lateral stability. A correction term is also introduced to revise the prediction model. At the same time, the weight of different control objectives of the leader and following vehicle was adjusted, respectively. Torque distribution is carried out at the lower level controller. Finally, the control strategy is tested on a hardware-in-the-loop (HIL) platform. The results show that the proposed control strategy can ensure lateral stability while improving the tracking performance of the vehicle platoon.

Network Situation Assessment Method Based on Improved BP Neural Network

Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to analyze the impact of the information on the whole network, and network security situation assessment can grasp the network security situation information in real time. However, the existing situation assessment methods have low assessment accuracy, and most of the studies focus on traditional networks, while there are few situation assessment studies in the SDN environment. In this paper, by summarizing the important index parameters of SDN, a network security situation assessment model based on the improved back propagation (BP) neural network (based on the cuckoo search algorithm) is proposed, and the step factor of the cuckoo search algorithm (CS) was improved to improve the search accuracy. The model maps the situation elements to the layers of the neural network, and optimizes the weights and thresholds of the BP neural network through the cuckoo search algorithm to obtain the global optimal solution; it finally realizes the purpose of situation assessment and the comprehensive rating of the SDN environment. In this paper, the evaluation model was verified on the network set up in Mininet. The experimental results show that the situation assessment curve of this model is closer to the real situation value, and the accuracy rate is 97.61%, with good situation assessment results.

Autonomous Maneuver Decision of UCAV Air Combat Based on Double Deep Q Network Algorithm and Stochastic Game Theory

Aiming at the problem that unmanned combat aerial vehicle (UCAV) is difficult to quickly and accurately perceive situation information and make maneuvering decision autonomously in modern air combat, which is easily affected by complex factors, a maneuvering decision algorithm of UCAV combined with deep reinforcement learning and game theory is proposed in this paper. Firstly, through the UCAV dynamics model and maneuver library, a reasonable air combat situation assessment model and advantage reward function are established, and the sample data of situation assessment indicators are constructed using the structure entropy weight method. Secondly, the convolutional neural network (CNN) is used to process the high-dimensional continuous situation features of UCAV in air combat, eliminate the correlation and redundancy between situation features, and train the neural network to approximate the action-value function. Then, the double deep Q network (DDQN) algorithm in reinforcement learning (RL) is introduced to train the agent by the interaction with the environment and combined with Minimax algorithm in stochastic game theory to solve the optimal value function in each specific state, and the optimal maneuver decision of UCAV is obtained. Air combat simulation results show that UCAV can choose maneuvers autonomously under different situations and occupy a dominant position quickly by this method, which greatly improves the combat effectiveness of UCAV.

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 show that 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.

Cooperative maneuver decision making for multi-UAV air combat based on incomplete information dynamic game

Cooperative autonomous air combat of multiple unmanned aerial vehicles (UAVs) is one of the main combat modes in future air warfare, which becomes even more complicated with highly changeable situation and uncertain information of the opponents. As such, this paper presents a cooperative decision-making method based on incomplete information dynamic game to generate maneuver strategies for multiple UAVs in air combat. Firstly, a cooperative situation assessment model is presented to measure the overall combat situation. Secondly, an incomplete information dynamic game model is proposed to model the dynamic process of air combat, and a dynamic Bayesian network is designed to infer the tactical intention of the opponent. Then a reinforcement learning framework based on multi-agent deep deterministic policy gradient is established to obtain the perfect Bayes-Nash equilibrium solution of the air combat game model. Finally, a series of simulations are conducted to verify the effectiveness of the proposed method, and the simulation results show effective synergies and cooperative tactics.

UCAV Air Combat Maneuver Decisions Based on a Proximal Policy Optimization Algorithm with Situation Reward Shaping

Autonomous maneuver decision by an unmanned combat air vehicle (UCAV) is a critical part of air combat that requires both flight safety and tactical maneuvering. In this paper, an unmanned combat air vehicle air combat maneuver decision method based on a proximal policy optimization algorithm (PPO) is proposed. Firstly, a motion model of the unmanned combat air vehicle and a situation assessment model of air combat was established to describe the motion situation of the unmanned combat air vehicle. An enemy maneuver policy based on a situation assessment with a greedy algorithm was also proposed for air combat confrontation, which aimed to verify the performance of the proximal policy optimization algorithm. Then, an action space based on a basic maneuver library and a state observation space of the proximal policy optimization algorithm were constructed, and a reward function with situation reward shaping was designed for accelerating the convergence rate. Finally, a simulation of air combat confrontation was carried out, which showed that the agent using the proximal policy optimization algorithm learned to combine a series of basic maneuvers, such as diving, climb and circling, into tactical maneuvers and eventually defeated the enemy. The winning rate of the proximal policy optimization algorithm reached 62%, and the corresponding losing rate was only 11%.

A Model of Network Security Situation Assessment Based on BPNN Optimized by SAA-SSA

In order to address the problems that the accuracy and convergence of current network security situation assessment models need to be improved, a model of network security situation assessment based on SAA-SSA-BPNN is proposed. Using the characteristics of sparrow search algorithm (SSA) optimized by simulated annealing algorithm (SAA) with good stability, fast convergence speed and is not easy to fall into local optimum to improve the BP neural network (BPNN), so as to find the best fitness individual, and obtain the optimal weight and threshold, then assign them to the BP neural network as the initial values. The preprocessed index data is input into the improved BP neural network model for training, and finally the threat degree of the network system is assessed based on the trained model. Comparative experimental results show that this assessment model has higher accuracy and faster convergence than other situation assessment models based on improved BP neural network.

Assessment of Jiangsu Regional Logistics Space Nonequilibrium Situation by Boosting and Bagging Algorithms

This study aims to solve the problem of unbalanced regional economic development in Jiangsu and deeply excavates the relevant theories of regional logistics and the unbalanced spatial situation. The classification of regional logistics and spatial disequilibrium and the dimensions involved are studied. At present, the development status of Jiangsu logistics has locked the main reason for the unbalanced situation of the regional logistics space in Jiangsu. The basic principles, application scope, and methods of Bagging and Boosting algorithms are deeply studied. Then, the Jiangsu logistics space nonequilibrium situation assessment model is established based on the Bagging and Boosting algorithms, and the input data is sorted and summarized. Finally, the results are obtained through experiments evaluating the model and analyzed and summarized. The core issues, the factors affecting development, and the uneven development situation have been comprehensively assessed. The conclusion drawn is as follows: the imbalance of the logistics productivity of various regions in Jiangsu will inevitably lead to the imbalance of the regional logistics space in Jiangsu, which affects the coordinated and healthy development of the regional logistics in Jiangsu. These conclusions are significant to the inherent causes and effects of regional logistics spatial disequilibrium and can promote the coordination, synergy, and common development of Jiangsu logistics regions.

A cloud Bayesian network approach to situation assessment of scouting underwater targets with fixed‐wing patrol aircraft

AbstractThe battlefield situation changes rapidly because underwater targets' are concealment and the sea environment is uncertain. So, a great number of situation information greatly increase, which need to be dealt with in the course of scouting underwater targets. Situation assessment in sea battlefield with a lot of uncertain information is studied, and a new situation assessment method of scouting underwater targets with fixed‐wing patrol aircraft is proposed based on the cloud Bayesian network, which overcomes the deficiency of the single cloud model in reasoning ability and the defect of Bayesian network in knowledge representation. Moreover, in the method, the cloud model knowledge deal with the input data of Bayesian network reasoning, and the advantages in knowledge representation of cloud theory and reasoning of Bayesian network are applied; also, the fuzziness and stochasticity of cloud theory in knowledge expression, the reasoning ability of Bayesian network, are combined. Then, the situation assessment model of scouting underwater targets with fixed‐wing patrol aircraft is established. Hence, the directed acyclic graph of Bayesian network structure is constructed and the assessment index is determined. Next, the cloud model is used to deal with Bayesian network, and the discrete Bayesian network is obtained. Moreover, after CPT of each node and the transformation between certainty degree and probability are accomplished; the final situation level is obtained through a probability synthesis formula. Therefore, the target type and the operational intention of the other side are deduced to form the battlefield situation. Finally, simulations are carried out, and the rationality and validity of the proposed method are testified by simulation results. By this method, the battlefield situation can be gained. And this method has a wider application range, especially for large sample data processing, and it has better practicability.

A network security situation assessment model based on BP neural network optimized by D-S evidence theory

Abstract Traditional networks rely heavily on the distribution of expert experience when assessing complex network security situations, resulting in low assessment accuracy, which has been unable to adapt to the current network security needs of the big data era, and has unavoidable problems such as low efficiency and poor flexibility. In response to these problems, this paper proposes a network security situation assessment method based on D-S evidence theory to optimize neural networks. First establish the CS-BP neural network model, enhance the local search ability of the cuckoo algorithm through conjugate gradient calculation, and then introduce it into the BP neural network to improve the training convergence speed and overcome the local minimum problem; finally, in order to reduce the basic probability distribution (BPA) subjective impact, using DS evidence theory to optimize the CS-BP neural network, determine the degree of impact of each attack, and evaluate the value of the network security situation. The experimental results show that the network situation assessment model of CS-BP neural network optimized based on D-S evidence theory can effectively assess the network security situation in the environment of trusted equipment.