Situation Assessment Method Research Articles

Joint operational situation assessment and firepower allocation method based on enhanced potential energy model

The assessment of battlefield situations and the allocation of firepower are pivotal prerequisites for conducting joint operational planning. In response to the challenges posed by multi-service joint operations, this paper presents a methodology based on enhanced operational potential energy model for the assessment of battlefield situations and the generation of optimal firepower allocation plan. We focuses on the three-fold factors influencing operational effectiveness: spatial, informational, and environmental factors, and completes the enhanced operational potential energy model by integrating the assessment of joint operational situations and the generation of optimal firepower allocation plans. Finally, the effectiveness of this methodology is demonstrated through operational simulations in a joint anti-ship combat scenario.

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 Awareness Technology Based on Security Intelligent Monitoring Technology

This paper uses data mining technology to dynamically monitor tobacco Industrial Enterprise' information systems. This paper builds an Internet security situation awareness system under a big data environment. The weight clustering method is used to classify users' network behavior. The spacing of weights is optimized to ensure the maximum difference in classification. Then, NAWL-ILSTM technology establishes a security situational awareness model for the Internet environment. In this project, the extended and short-memory Nadam optimal algorithm (NAWL) is used to realize data deep learning. Finally, the tobacco industry network security situation assessment method is designed to complete the dynamic monitoring of tobacco industry network security based on data mining. Simulation results show that the proposed method can effectively improve the safety evaluation performance of the system and reduce evaluation errors.

Network security situation assessment and prediction method based on multimodal transformation in edge computing

Network security play a critical role in ensuring the robustness and integrity of network systems. We propose a novel method based on multimodal transformation in edge computing for accurate and reliable data communication. The method leverages Graph Convolutional Networks (GCNs) to capture and analyze the complex relationships and dependencies among network entities, enabling enhanced prediction capabilities. By integrating multimodal transformation, diverse data sources are fused in the model. The assessments and comprehensive comparisons with established algorithms unequivocally establish the supremacy of our proposed approach, particularly with respect to metrics such as accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Our method exhibits promising performance, enabling proactive threat detection and mitigation in network security by more then 90% accuracy. In the future work, we have included the different multimodal transformation techniques, addressing interoperability challenges, scaling the method for large-scale networks, and adapting the model to dynamic network environments.

METHODOLOGY FOR SELECTION OF MILITARY AUTOMOTIVE EQUIPMENT BY QUALITY INDICATORS

The article analyses the current state of the military vehicle fleet and problematic issues and proposes how to provide the units of the Ukrainian Defence Forces (UDF) with new models of military vehicles in the context of russia’s war against Ukraine. The question arises as to what basic technical characteristics and design requirements the military vehicles should meet.
 It should be clearly understood that due to objective reasons (hostilities, destroyed economy, lack of money for the production of new models of military vehicles), in the near future, all the necessary nomenclature of modern military vehicles will not be produced in Ukraine, but the problem is to choose foreign models of military vehicles in terms of quality indicators that will best meet the needs of the Armed Forces in terms of tactical and technical characteristics, structure and mobility in difficult war conditions.
 Due to the natural and climatic conditions, difficult terrain and a wide range of weapons used by the enemy, there is a need to constantly renew the fleet of ATVs.
 The Rapid Situation Assessment (RSA) method will allow for a quick assessment and selection of military vehicles in a minimum of time. To process the obtained estimates, we will apply the method of hierarchy analysis, which is widely used to formalise the process of managerial decision-making.
 The purpose of the article is to use a methodological approach to the selection of military vehicles for the needs of the UDF using the hierarchy analysis method with consideration of qualitative criteria.
 The chosen scientific approach allows identifying and ranking the basic mobility properties of military vehicles in terms of importance, which in turn will help the evaluators to identify those that are currently the most important indicators for the selection of military vehicles. The criteria for selecting military vehicles are based on the current legal and regulatory framework for defence procurement with public funds.
 This analysis will allow us to formulate some proposals for improving the evaluation methodology for the procurement of military vehicles.
 For this purpose, first of all, it is necessary to define and formulate
 
 specialised structures for the selection and evaluation of military vehicles;
 algorithm, mechanisms and tools for its implementation;
 professional training (retraining) of personnel who are able to carry out a professional, informed choice of military vehicles.

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.

The Situation Assessment of UAVs Based on an Improved Whale Optimization Bayesian Network Parameter-Learning Algorithm

To realize unmanned aerial vehicle (UAV) situation assessment, a Bayesian network (BN) for situation assessment is established. Aimed at the problem that the parameters of the BN are difficult to obtain, an improved whale optimization algorithm based on prior parameter intervals (IWOA-PPI) for parameter learning is proposed. Firstly, according to the dependencies between the situation and its related factors, the structure of the BN is established. Secondly, in order to fully mine the prior knowledge of parameters, the parameter constraints are transformed into parameter prior intervals using Monte Carlo sampling and interval transformation formulas. Thirdly, a variable encircling factor and a nonlinear convergence factor are proposed. The former and the latter enhance the local and global search capabilities of the whale optimization algorithm (WOA), respectively. Finally, a simulated annealing strategy incorporating Levy flight is introduced to enable the WOA to jump out of the local optimum. In the experiment for the standard BNs, five parameter-learning algorithms are applied, and the results prove that the IWOA-PPI is not only effective but also the most accurate. In the experiment for the situation BN, the situations of the assumed mission scenario are evaluated, and the results show that the situation assessment method proposed in this article is correct and feasible.

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.

AHP-Based Network Security Situation Assessment for Industrial Internet of Things

The Industrial Internet of Things (IIoT) is used in various industries to achieve industrial automation and intelligence. Therefore, it is important to assess the network security situation of the IIoT. The existing network situation assessment methods do not take into account the particularity of the IIoT’s network security requirements and cannot achieve accurate assessment. In addition, IIoT transmits a lot of heterogeneous data, which is subject to cyber attacks, and existing classification methods cannot effectively deal with unbalanced data. To solve the above problems, this paper first considers the special network security requirements of the IIoT, and proposes a quantitative evaluation method of network security based on the Analytic Hierarchy Process (AHP). Then, the average under-/oversampling (AUOS) method is proposed to solve the problem of unbalance of network attack data. Finally, an IIoT network security situation assessment classifier based on the eXtreme Gradient Boosting (XGBoost) is constructed. Experiments show that the situation assessment method proposed in this paper can more accurately characterize the network security state of the IIoT. The AUOS method can achieve data balance without generating too much data, and does not burden the training of the model. The classifier constructed in this paper is superior to the traditional classification algorithm.

An Algorithm for Network Security Situation Assessment Based on Deep Learning

Aiming at the problems that the existing assessment methods are difficult to solve, such as the low efficiency and uncertainty of network security situation assessment in complex network environment, by constructing the characteristic elements of network security big data, a typical model based on deep learning, long short-term memory (LSTM), is established to assess the network security situation in time series. The hidden relationship and change trend of network security situation are automatically mined and analyzed through the deep learning algorithm of big data, which greatly improves the prediction accuracy of security situation. Experimental analysis shows that this method has a better assessment effect on network threats, has higher learning efficiency than the traditional network situation assessment methods, and has strong representation ability in the face of network threats. It can more accurately and effectively assess the changing trend of big data security situation in the future.

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.

Research and Development of Environmental Radiation Situation Assessment Procedures and Methods Following Serious Nuclear Accidents

The environmental effects of severe nuclear accidents pose a great threat to the environment and to living organisms. Unfortunately, there have been several examples of this over time. In our work, we present development solutions and opportunities which a nuclear power plant or other nuclear facility can implement nuclear environmental monitoring even under extreme conditions. We will discuss systems and tools that can be used to increase the efficiency of the radiation situation assessment, helping decision-makers to make quick and optimal decisions, thus helping to minimise the environmental impact.

Situation assessment in air combat considering incomplete frame of discernment in the generalized evidence theory

For situation assessment in air combat, there may be incomplete information because of new technologies and unknown or uncertain targets and threats. In this paper, an improved method of situation assessment for air combat environment considering incomplete frame of discernment in the evidence theory is proposed to get a more accurate fusion result for decision making in the battlefield environment. First, the situation in air combat is assessed with knowledge. Then, the incomplete frame of discernment in the generalized evidence theory, which is an extension of Dempster–Shafer evidence theory, is adopted to model the incomplete and unknown situation assessment. After that, the generalized combination rule in the generalized evidence theory is adopted for fusion of situations in intelligent air combat. Finally, real-time decision-making in situation assessment can be reached for actions to take. Experiments in situation assessment of air combat with incomplete and uncertain situations show the rationality and effectiveness of the proposed method.

A new situation assessment method for aerial targets based on linguistic fuzzy sets and trapezium clouds

Situation assessment has attracted much attention. The chief aim of this paper is to research the problem from the perspective of multicriteria group decision-making (MCGDM). First of all, this paper expresses the threat information with linguistic fuzzy sets, including Atanassov’s intuitionistic fuzzy sets (AIFSs), Atanassov’s interval-valued intuitionistic fuzzy sets (AIVIFSs), and interval intuitionistic uncertain linguistic sets (IIULSs). Then, some relevant concepts and aggregation operators of trapezium clouds are proposed, meanwhile, a new approach that calculates the weights considering subjectivity and objectivity is presented with multi-objective programming. Next, the conversion methods between AIFSs, AIVIFSs, IIULSs and trapezium clouds are derived. Finally, the algorithm process is presented and an illustrative example is employed to prove the validity. Also, the comparison analysis with other widely used methods is conducted to verify the feasibility of the proposed algorithm.

Research on situation assessment of active distribution networks considering cyberattacks

With the rapid development of integrated energy system, the large-scale and high-permeability access of distributed generations (DGs) is making the distribution networks develop into active distribution networks (ADNs). The increased complexity of ADNs also increases the vulnerabilities for cyberattacks. It is a new challenge how to evaluate the situation of an ADN so as to support the decision-making of grid control policies in the condition of cyberattacks probably occur. Hence, in this paper, we proposed a method of situation assessment for ADNs considering cyberattacks. This method is aggregated by two parts. 1) An index system is presented, which includes the indexes of DGs stability, the indexes of security risk considering cyberattacks along with the traditional safety indexes. 2) The entropy weight method is used to assign weights to each index, and taking the normal operation status of ADNs as the reference scenario, an operating situation assessment method for ADNs is proposed based on grey correlation analysis method. Finally, in order to verify the effectiveness of the proposed index system and assessment method, 12 attack scenarios are established from three categories: attacks on DGs, attacks on controllable loads and attacks on both of them, and the situation of the ADN, a case based on IEEE 33-node standard distribution system, is evaluated under each scenario.

A BIPMU-based network security situation assessment method for wireless network

With the rapid development of information technology, wireless network has been accepted by the public and widely used in the daily life and work. Especially with the comprehensive deployment of 5G network technology, the application scope of wireless network is continuously increasing, which can greatly reduce the IT infrastructure overhead and human resource investment. Although the tremendous advantages, wireless network inevitably suffers from some severe security challenges. For example, the network data is characterized by large volumes, variety and high dimensions, which would greatly affect the efficiency and accuracy of network security situation assessment (NSSA). To solve this problem, we first design a novel model based on parsimonious memory unit (PMU), namely, bidirectional parsimonious memory unit (BIPMU). Compared with PMU, BIPMU can not only learn and characterize data through its time series relationship, but also comprehensively and effectively manage the potential connection of long-term and short-term dependence on time series data. Subsequently, we adopt BIPMU to design a novel NSSA method to evaluate the real-time security situation of wireless network. Finally, we develop a prototype implementation of our proposed NSSA method and provide the performance evaluation. The experimental results demonstrate that compared with the previous NSSA methods, our proposed NSSA method is much more attractive in efficiency and accuracy.

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.

Navigation Situation Assessment of Autonomous Surface Vehicles in a Cooperative Hunting Environment

Abstract This paper proposes a navigation situation assessment method for autonomous surface vehicles (ASVs) in a cooperative hunting environment. By virtue of the repulsion function expressed in the artificial potential field, the navigation situation of hunting ASVs and target ASVs is firstly described. And the hunting situation is also constructed to describe the cooperative hunting. Based on the navigation situation and the hunting situation, a navigation situation assessment method for cooperative hunting of multiple ASVs is designed, where the number of hunting vehicles and the hunting radius can be successfully computed. Simulation results show that this proposed situation assessment method can give an optimised formation pattern and provide an effective reference for cooperative hunting of ASVs.

Evaluation and Prediction Method of System Security Situational Awareness Index Based on HMM Model

In recent years, with the continuous development and progress of information technology and science and technology, big data has entered all walks of life, integrated into the lives of the public, and has become a necessity for social operation; the gradual development of artificial intelligence has also made life in modern times. People in society are more and more convenient. However, the development of science and technology is also accompanied by corresponding problems, and the war in information has gradually started. This paper simulates the possible information security through the hidden Markov model and then verifies the feasibility and effectiveness of the situation assessment method and the situation prediction method, in order to effectively evaluate the relevant information security level and effectively predict the accuracy of the situation value. The experimental results show that the fluctuation of the situation value corresponds to the different attack behaviors carried out by the attacker, accurately describes the information security status of the system, and verifies the effectiveness and accuracy of the situational awareness method proposed in this paper, while the situation prediction method based on ARIMA predictable short-term changes in situational values can be used for short-term forecasts that require high accuracy.

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.