In this article, a comprehensive approach to information sharing framework aimed at increasing network security and survivability is presented. Currently, in order to defend complex cyber-attacks and sophisticated cybercrime, comprehensive means of ensuring network security and survivability are highly demanded. Comprehensive is understood as at least technical, human, organizational, and regulatory dimensions, whereas technical means that both on-line and off-line aspects should be carefully taken into account. The focus of this article is on the technical dimension and, in particular, on information exchange mechanisms for on-line and off-line dimension. Firstly, the on-line information exchange mechanism dedicated for interconnected federated networks is described. Then, a proposition of the off-line network information sharing approach regarding results of the risk assessment, cybersecurity audits, threat ranking, etc., is discussed. Developments and results of information sharing mechanisms in both on-line and off-line network security dimensions are also described.

Comprehensive approach to information sharing for increased network security and survivability

Most of the existing network security situation prediction mechanisms are only directed by the prediction of the situation value, but these methods do not reveal the problem of the dynamics features of the network situational factors. For these issues, this paper proposes the network security situation prediction mechanism based on the complex network. By this mechanism, we can easily and intuitively trace the dynamics features in the value fluctuations of the network security situation prediction. After that, this paper proposes Markov prediction method based on the complex network, so that we can achieve an effective prediction of the Security Status. Through simulation analysis, the network security situation prediction mechanism based on the complex network can reflect the essence behavior of the system to some extent. At the same time this mechanism can precisely predict the Security Status in the complex network.

The Research of the Network Security Situation Prediction Mechanism Based on the Complex Network

Network security situation automatic prediction model based on accumulative CMA-ES optimization

Protecting intellectual property and confidential customer details from network based attacks is becoming increasingly difficult in modern times due to a dramatic increase in online based attacks. For companies such as The NPD Group, protecting this confidential information is key in keeping a positive perceived image while also doing its utmost to protect vital I. P. This paper proposes an architecture that will enable a company to perform a proactive risk assessment of their network to mitigate any possible chance of data leaks or damage to the network. It also performs an abstraction of the performance metrics gained from various data providers to allow for easily understandable metrics pertaining to the risk level of the network at large while also maintaining a level of granularity that can be used by technical experts within the company. SAVIOR is one algorithm within this architecture that uses machine learning mechanisms to perform abstraction of performance metrics gained from a data provider, Nexpose, while also performing an analysis of assets in terms of one area of risk, vulnerability.

S. A. V. I. O. R: Security Analytics on Asset Vulnerability for Information Abstraction and Risk Analysis

with the complexity of the power system and the increasingly severe network security environment, the industry has urgently needed to improve the risk prediction ability of the power system security and the potential safety hazards brought about by the disposal. According to the experience and the features attributes of historical data, K-means unsupervised learning clustering is carried out. For supervised learning classification, this paper chooses SVM-KNN, and the risk assessment portrait after business disposal is constructed. After establishment and operation of the model, effective and rapid analysis and output of disposal recommendations and corresponding risk levels are carried out, and the original experience is intellectualized and rationalized to the relevant people. In order to make sure the stable, efficient and safe operation of the power monitoring system, model could give prompt safety advice as an expert.

Research on Risk Assessment Technology of Power Monitoring System Based on Machine Learning

With the development and application of network technology, the issues of network security has become prominent increasingly. Network security risk assessment has become the key process in solve network security. Support Vector Machine(SVM)is one of novel learning machine methods, its advantages are simple structure, strong compatibility, global optimization, least raining time and better generalization. So it has superiority to apply it into network security risk assessment. This paper describes the content and the evaluation indicators of network security risk assessment and the classification of the support vector machine in detail. And then an assessment method of network security risk based on support vector machine is proposed in this paper. Experiment results show that the method Is feasible and effective.

Xionggang Tu

Papers

Network security risk assessment based on support vector machine