Research on threat intelligence detection model based on deep neural network

Fangfang Dang; Lijing Yan; Shuai Li; Zhiqiang Jia

doi:10.1117/12.2674747

2 May 2023 Research on threat intelligence detection model based on deep neural network

Fangfang Dang, Lijing Yan, Shuai Li, Zhiqiang Jia

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126422S (2023) https://doi.org/10.1117/12.2674747
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

Threat Intelligence is the knowledge set and operational advice of a series of evidences including vulnerabilities, threats, characteristics and behaviors obtained through big data, distributed system or other specific collection methods. It can restore the network attacks that have happened and predict the future possible attacks, and provide reference for users to make decisions. Help users avoid or minimize losses caused by network attacks. However, the existing technologies cannot respond in time and defend in advance to threat behaviors in the network environment as a whole, and cannot simultaneously take into account the prediction efficiency and accuracy of threat prediction. Aiming at the deficiency of existing technologies, this paper builds a security defense model of industrial control system based on threat intelligence. Firstly, credible threat intelligence is extracted through the quality assessment model of deep neural network algorithm. Secondly, high-quality threat intelligence is extracted through the self-defined matching principle, and contextual data is extracted to analyze the attack intention and predict the attack behavior. Finally, by constructing an attack and defense game model based on the attacker and the defender, the mixed strategy Nash equilibrium is used to predict the attack behavior based on non-high quality threat intelligence. Through a series of experiments, the model has a good predictive effect in the industrial control system.

Citation Download Citation

Fangfang Dang, Lijing Yan, Shuai Li, and Zhiqiang Jia "Research on threat intelligence detection model based on deep neural network", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126422S (2 May 2023); https://doi.org/10.1117/12.2674747

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