A hierarchical architectural model for network security exploring situational awareness

Almeida, Ricardo Borges; Covalski, Victor; Machado, Roger; Rosa, Diórgenes Yuri Leal da; Yamin, Adenauer; Donato, Lucas Medeiros; Pernas, Ana Marilza

doi:10.1145/3297280.3297417

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…Dutt et al [12] proposed an IBLT model based on tools to resist security attacks, which can identify some behaviors in the network environment, thereby effectively improving the accuracy of evaluation. Almeida et al [13] connected modular components in a multilayered manner, proposing a situational awareness model based on EXEHDA-ISSA. Zhang and He [14] used big data analysis and combined forecasting methods to predict the security situation of smart grids; Liu [15] improved the ability of D-S evidence theory to confront conflict by proposing predictive models based on improved D-S.…”

Section: Introductionmentioning

confidence: 99%

Development of Network Security Based on the Neural Network PSD Algorithm

Jiang

2022

Computational Intelligence and Neuroscience

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The more frequent occurrence of network security incidents has an impact on network security. Through the research on network security situational awareness, this paper constructs a multilevel network security situation evaluation index system from various aspects and uses the Elman neural network optimized by the genetic algorithm to evaluate network security situation. Aiming at the disadvantage of subjective dependence in the traditional assignment method of basic probability assignment function, Elman neural network is used to obtain the basic probability assignment function to increase its objectivity, and it is optimized with the PSD algorithm. In addition, the neural network is further improved by the genetic algorithm. In the traditional D-S evidence theory, an evidence correction step is added to optimize the situation that the final judgment result is incorrect due to evidence conflict. Finally, the fusion rules of the D-S evidence theory are used to fuse the support degrees of the four first-level situations to different security levels to obtain the final network security situation assessment result. The results show that the prediction accuracy of the GA-Elman neural network model is as high as 80%, which is significantly higher than that of the traditional D-S model, indicating that the model proposed in this paper has improved the accuracy of the assessment and prediction results. In conclusion, this study provides feasible theoretical prediction guidance for the accurate assessment of network security posture, reveals the improvement ideas for network security development, and is of great significance for the maintenance of network environment security.

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Section: Introductionmentioning

confidence: 99%

Development of Network Security Based on the Neural Network PSD Algorithm

Jiang

2022

Computational Intelligence and Neuroscience

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Intelligent Cybersecurity Situational Awareness Model Based on Deep Neural Network

You

Wang

et al. 2020

2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

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Design and implementation of computer network security protection system based on artificial intelligence technology

Liu

2023

Applied Mathematics and Nonlinear Sciences

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With the rapid development of the Internet, security issues are becoming more and more prominent, and since most information is transmitted through the Internet today, Internet security is particularly important. When the Internet was designed, only mutual compatibility and interoperability between networks were considered, and security issues were not fully considered. As a result, as the Internet continues to grow, security issues are becoming more and more serious. One of the more difficult attacks is the Distributed Denial of Service (DDoS) attack, which has many forms of attacks, is harmful, and is difficult to identify and defend. Therefore, building a global Internet security protection system to achieve effective protection against DDoS attacks is the main work of this research paper. In this paper, we propose an artificial intelligence DDoS attack protection system, which implements a controller and switch auto-detection model by extending the protocol and establishing an optimization model to realize a low-load and low-latency traffic monitoring scheme; for DDoS attacks. We propose the attack inspection algorithm SCVAE based on Variational Encoder (VAE) and Spectral Clustering. in order to mitigate DDoS attack traffic, the protection system uses the QoS traffic control method, builds the application flow hierarchy model, and filters the attack traffic endured by the system by setting the application flow bandwidth limit as well as the traffic priority dual policy. Finally, a Mininet-based simulation test environment is built to evaluate the model, and different test indexes are set for different system modules to evaluate their actual performance. The results of this paper show that in the network traffic monitoring test, the artificial intelligence DDoS attack protection algorithm can respond to the attack more quickly by reducing the average 73ms per sampling compared with other algorithms; in the attack traffic identification test, the comparison accuracy (P) is improved by 15.14%, the accuracy (AC) is improved by 13.26%, the recall (R) is reduced by 9.23%, and the F1 measurement criteria improved by 23%. The test verifies that the artificial intelligence DDoS attack protection system can achieve real-time monitoring of each performance parameter and also illustrates the feasibility and practicality of the research content of this paper, which strengthens the construction of the technical means of Internet security protection and further enhances the Internet security defense capability.

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A hierarchical architectural model for network security exploring situational awareness

Cited by 3 publications

References 3 publications

Development of Network Security Based on the Neural Network PSD Algorithm

Development of Network Security Based on the Neural Network PSD Algorithm

Intelligent Cybersecurity Situational Awareness Model Based on Deep Neural Network

Design and implementation of computer network security protection system based on artificial intelligence technology

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