Network Security Situation Awareness Model Research Articles

Establishment of nonlinear network security situational awareness model based on random forest under the background of big data

Abstract In order to explore the establishment of a nonlinear network security situational awareness model based on random forest in the context of big data, a multi-level network security knowledge system evaluation model based on random forest is proposed. This article proposes a multi-level CSSA analysis system and then uses random memory algorithm to create a CSSA evaluation model. Also, it proposes a CSSA multi-level analysis framework and then uses random forest algorithm to build a CSSA evaluation model. A random vector distribution of the same values is used for all forest trees. In this article, the interval [0,1] is used to quantitatively describe the weight of the security level. The training sample ratio of test samples is 110:40, in order to predict the security of the network, the prediction of knowledge is closer to the true value, and the complexity of multi-level security is predicted. Use unusual forests. The tree returns the most recommended part, which is a more realistic assessment of network security. The experimental results show that considering the network security situation, the prediction performance of this method is closer to the actual value, and the performance is better than the other two methods. Therefore, perception of multi-level security situations can be effectively predicted using random access memory. It is proved that random forest is faster and more efficient in network security.

Exploration of a network security situational awareness model based on multisource data fusion

Exploration of a network security situational awareness model based on multisource data fusion

Deep learning-based security situational awareness and detection technology for power networks in the context of big data

Abstract With the comprehensive promotion of “big data + energy”, new power network security threats are also more prominent, and the traditional security system mainly based on “protection” will face great challenges. Firstly, this paper proposes four kinds of network security situational awareness detection techniques based on distributed data analysis by combining the characteristics of big data in power networks. Secondly, the CRIT-LSTM power network security situational awareness model is constructed by improving its loss evaluation process using the cross entropy (CE) function and improving the LSTM unit structure using linear unit (ReLU). Finally, the performance of the three models is compared and analyzed under two aspects of neural network training and testing and various metrics to verify the models’ effectiveness. The results show that the improved CRIT-LSTM model based on deep learning, combining LSTM and ReLU algorithms, has an RMSE of 0.717 for the training set and 0.806 for the test set. 7.32% accuracy and 10.51% improvement in recall compared to the LSTM-only model. The power network security situational awareness model based on the CRIT-LSTM model proposed in this paper integrates various security system functions to maximize the defense against attacks and reduce unnecessary security risk losses.

A Network Security Situation Awareness Method Based on GRU in Big Data Environment

Aiming at the “bottleneck” problems of the traditional network security situation awareness model, such as large equipment limitations, single data source and poor integration ability, weak level of autonomous learning and data mining, a network security situation awareness framework suitable for big data is constructed. A gate recurrent unit (GRU) model is established to effectively extract features from the situation data set through the deep learning algorithm of big data. It is a method to automatically mine and analyze the hidden relationship and change trend of network security situation, realize the high-speed acquisition and fusion of massive multi-source heterogeneous data, and perceive the network security situation from an all-round perspective. The experimental results show that this method has a good awareness effect on network threats, and has strong representation ability in the face of network threats. It can effectively perceive the network threat situation without relying on data labels, which verifies that this method can effectively improve the efficiency and accuracy of security situation awareness.

APPLICATION OF FRACTAL NEURAL NETWORK IN NETWORK SECURITY SITUATION AWARENESS

The purposes are to accurately perceive the network security situations and predict the development trend and effectively defend against network attacks during the operation of the Internet. The Long Short-Term Memory (LSTM) network is adopted as the subject of the network security situation awareness and prediction model. Moreover, it is optimized by the Genetic Algorithm (GA) to improve its global search capability. Then, a Fractal Neural Network (FNN) is constructed in combination with fractal theory, which is utilized in network security situation awareness to avoid the exploding or vanishing gradient problems. The KDD CUP 99 standard dataset is applied to verify the performance of the proposed GA-LSTM FNN; results demonstrate that its accuracy of network security situation awareness can reach 90.22%. The experimental results confirm that using the fractal difference function as the activation function can deliver the gradient variation in a balanced and stable manner. Besides, it can improve the feasibility and effectiveness of the neural network structure for network security situation awareness and prediction. The FNN studied is of practical significance for assessing the current network security situation and predicting its evolution trend, providing a reference for protecting the operation of the Internet from network attacks.

Information Security Situation Awareness Based on Big Data and Artificial Intelligence Technology

The information security threats faced by enterprises are increasing rapidly, and the technology of security attacks is also diversified and developed at a high level. The application of big data technology and artificial intelligence technology in all walks of life continues to deepen. While greatly improving social productivity, it also brings certain information security issues. Security situation awareness technology has become a new research hotspot in the field of network security. Using big data-related technologies to analyze, filter, merge, and identify known and unknown security threats is a new research discovery. This research builds a new cascaded network security situational awareness model based on the traditional and fusion decision tree algorithms. We use an induction algorithm to generate a decision tree on the preprocessed data to classify the data according to the decision rules. Research shows that a new network security situation awareness model is constructed using decision tree calculations. Compared with the traditional model, the classification effect of this model is better.

Software Development and Design of Network Security System Under Big Data Analysis

To explore the prediction effect of network security situational awareness on network vulnerabilities and attacks under the background of big data, this study constructs a predictive index system based on the network security situational awareness model. Based on the improved cuckoo algorithm, the cuckoo search radial basis function neural network is used to predict the situation. The weight value in the model is determined by the hierarchical analysis method, vulnerability simulation is conducted by Nessus software and network attack simulation is conducted by Snort software, and then the situation is evaluated by a fuzzy comprehensive evaluation method. Finally, Jquery and Bootstrap software is used to develop the system. The results show that the cuckoo search radial basis function model proposed in this study could predict network security situations more accurately than the radial basis function model, cuckoo search back-propagation neural network model, genetic algorithm radial basis function model and Support vector machine model based on particle swarm optimization model.

Situation prediction of large-scale Internet of Things network security

The Internet of Things (IoT) is a new technology rapidly developed in various fields in recent years. With the continuous application of the IoT technology in production and life, the network security problem of IoT is increasingly prominent. In order to meet the challenges brought by the development of IoT technology, this paper focuses on network security situational awareness. The network security situation awareness is basic of IoT network security. Situation prediction of network security is a kind of time series forecasting problem in essence. So it is necessary to construct a modification function that is suitable for time series data to revise the kernel function of traditional support vector machine (SVM). An improved network security situation awareness model for IoT is proposed in this paper. The sequence kernel support vector machine is obtained and the particle swarm optimization (PSO) method is used to optimize related parameters. It proves that the method is feasible by collecting the boundary data of a university campus IoT network. Finally, a comparison with the PSO-SVM is made to prove the effectiveness of this method in improving the accuracy of network security situation prediction of IoT. The experimental results show that PSO-time series kernel support vector machine is better than the PSO-Gauss kernel support vector machine in network security situation prediction. The application of the Hadoop platform also enhances the efficiency of data processing.

Graph database-based network security situation awareness data storage method

With the rapid development of the Internet, network security situation awareness has attracted tremendous attention. In large-scale complex networks, network security situation awareness data presents the characteristics of large-scale, multi-source, and heterogeneous. Recently, much research work have been done on network security situation awareness. However, most of the existing methods store different types of data in different ways, which makes data query and analysis inefficient. To solve this problem, we propose a graph database-based hierarchical multi-domain network security situation awareness data storage method. In our scheme, we build a hierarchical multi-domain network security situation awareness model to divide the network into different domains, which can collect and dispose the awareness data more efficiently. Meanwhile, to unify our storage mode, we also define network security situation awareness data storage rules and methods based on graph database. Finally, extensive experiments on real datasets show that our proposed method is efficient compared to state-of-the-art storage models.

Network Security Situation Awareness Based on Semantic Ontology and User-Defined Rules for Internet of Things

Internet of Things (IoT) brings the third development wave of the global information industry, which makes users, network, and perception devices cooperate more closely. However, if IoT has security problems, it may cause a variety of damage and even threaten human lives and properties. To improve the abilities of monitoring, providing emergency response, and predicting the development trend of IoT security, a new paradigm called network security situation awareness (NSSA) is proposed. However, it is limited by its ability to mine and evaluate security situation elements from multi-source heterogeneous network security information. To solve this problem, this paper proposes an IoT network security situation awareness model using a situation reasoning method based on semantic ontology and user-defined rules. Ontology technology can provide a unified and formalized description to solve the problem of semantic heterogeneity in the IoT security domain. In this paper, four key sub-domains are proposed to reflect an IoT security situation: context, attack, vulnerability, and network flow. Furthermore, user-defined rules can compensate for the limited description ability of ontology, and hence can enhance the reasoning ability of our proposed ontology model. The examples in real IoT scenarios show that the ability of the network security situation awareness that adopts our situation reasoning method is more comprehensive and more powerful reasoning abilities than the traditional NSSA methods.

Constructing general cube to be aware of network security situation

Concerning the problems of limited current network security situation assessment scope,single information source,higher time and space complexity and larger deviation of the accuracy,a method was put forward to construct general cube,which can be aware of the network security situation. The continuous situation factor data monitored can be pretreated by discretizing by "3σ rule"and aggregated in the general built cube,that fused into component security situation vertically and merged into the network security situation from component security situation using statistical methods horizontally. It can provide reliable reference to enhance network security. Finally,making full use of network data,the network security situation awareness model and algorithm proposed are verified and the experimental results show correctness of this method.

Immune-Based Systems on Network Security Situation Awareness and Risk Prediction Model

In this paper, the process of the developments and changes of the network intrusion behaviors were analyzed. An improved epidemic spreading model was proposed to study the mechanisms of aggressive behaviors spreading, to predict the future course of an outbreak and to evaluate strategies to control a network epidemic. Based on Artificial Immune Systems, the concepts and formal definitions of immune cells were given. And in this paper, the forecasting algorithm based on Markov chain theory was proposed to improve the precision of network risk forecasting. The data of the Memory cells were analyzed directly and kinds of state-spaces were formed, which can be used to predict the risk of network situation by analyzing the cells status and the classification of optimal state. Experimental results show that the proposed model has the features of real-time processing for network situation awareness.

A Network Security Situational Awareness Model Based on Information Fusion

Security situational awareness has become a hot topic in the area of network securityresearch in recent years. The existing security situational awareness methods are analyzed and compared in details, and thus a newnetwork security situational awareness model based on information fusion is proposed. This modelfuses multi-source information from a mass of logs by introducing the modified D-S evidence theory,gets the values of nodes security situational awareness by situational factors fusion using attacks threat,and vulnerability information which network nodes have and successful attacks depend on, computesthe value of network security situational awareness by nodes situation fusion using service informationof the network nodes, and draws the security-situation-graph of network. Then, it analyzes the timeseries of the computing results by ARMA model to forecast the future threat in network security.Finally an example of actual network datasets is given to validate the network security situationalawareness model and algorithm. The results show that this model and algorithm is more effective andaccurate than the existing security situational awareness methods.

A New Network Security Evaluation Conceptual Model

Network security situation awareness model is the research basis in network security situation awareness. Through building this model, we can measure the relationships among system components and that between the components and environments. We put forward the conceptual model of network security situation awareness, and establish the model of current network security situation evaluation and future network security situation prediction.

Network security situation awareness model based on autonomic computing

Concerning the complexity of network security management and the absence of self-adaptation on situation awareness process,a Network Security Situation Awareness Model(NSSAM) based on autonomic computing was proposed.The situation extraction was analyzed in real-time by an autonomic feedback law.From the perspectives of attack and defense,a multi-level and multi-angle network security situation assessment model employing Analytic Hierarchy Process(AHP) was established according to the extracted situation information.The model of future network security situation prediction adopting improved genetic neural network was built on the basis of the past and current network security situation.Test results show that NSSAM with autonomic feedback mechanism can effectively enhance self-adaptation of the system.

Network Security Situation Awareness Model Based on Multi-Period Assessment

This article proposes a new model which is called hierarchical network security situation awareness model. The model emphasizes the time dimension in the assessment, it divides the assessment into two parts: short-term and long-term. The data source is the logs, flow data and others generated by equipments. These original data through the data fusion model and the situation evaluation model to obtain the short-term result, this result is the foundation of long-term. Combine the static indicators with dynamic which is calculated by short-term to figure out the long-term result. This model is based on multi-source data, and expand quantitative assessment in the different levels of time dimension, it makes up the security situation assessment in multi-period assessment and has advantage in network security situation prediction.

Research on the Network Security Situation Awareness Model for the Electric Power Industry Internal and Boundary Network

Research on the Network Security Situation Awareness Model for the Electric Power Industry Internal and Boundary Network

A Network Security Situation Awareness Model Based on Artificial Immune

Network security situation awareness is a new technology for network security monitoring and early warning, and is a hot spot in the area of network security. In this paper, the artificial immune technology is applied to the study of network security situation awareness, and a model is presented and implemented. The paper adopts the artificial immune-based intrution detection technology to real-timely monitor network attacks, and introduces vaccination mechanism to effectively improve the real-time network attack detection abilities of network hosts. In addition,the paper puts forward the concept of network group according to idiotypic immune network theory, and establishes a real-time and quantitive network risk assessment sub-model based on antibody concentration. The changes of antibody concentration are not only related to intrusion intensity, but also to interactions with antibodies in the same network group. To predict future network security situation trends,the paper adopts time-series prediction mechanism based on cloud models. Theoretical analysis and experimental results show that the model is effective to network security situation awareness with advantages of real-time and high accuracy.

Network Security Situation Awareness Model-Inspired by Immune

Network security situation awareness is the pivotal technology of building the next generation active defensible network, which has been got widespread concerns by experts and scholars. Inspired-by artificial immune, an immune-inspired network security situation awareness model, referred as Ineim, was given. The real-time network security situation awareness equations were built up, which can exactly compute security situation awareness of the host and network. Both the theory analysis and experimental results prove that Ineim provides a positive and active network security method.

A Network Security Situational Awareness Model Based on Log Audit and Performance Correction

This paper analyzes and compares the existing situational awareness methods and proposes a network security situational awareness model based on log audit and performance correction algorithm.First,nodes theoretic security threat is got by log audit and the value of nodes security situation is computed by performance correction algorithm.Then the value of network security situation is computed using service information,the future threat is predicted by several prediction models,and the Security Situational Graph(SSG) is drawn.Finally an example is given to validate the network security situational awareness model and algorithm by simulation software.The example proves that the model is more effective and accurate to reflect the network security situational and its trends than traditional methods.