A Threat Intelligence Analysis Method Based on Feature Weighting and BERT-BiGRU for Industrial Internet of Things. (25th February 2022)

• Record Type: Journal Article
• Authors: Yan, Jingchen; Du, Zhe; Li, Jifang; Yang, Shiduo; Li, Jinghao; Li, Jianbin
• Other Names: Yi Xun Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-02-25
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/7729456
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : The combination of 5G technology and the industrial Internet of things (IIoT) makes it possible to realize the interconnection of all things. Still, it also increases the risk of attacks such as large-scale DDoS attacks and IP spoofing attacks. Threat intelligence is a collection of information causing potential and nonpotential harm to the industrial Internet. Extracting network security entities and their relationships from threat intelligence text and constructing structured threat intelligence information are particularly important for IIoT security protection. However, threat intelligence is mostly text reports, which means the value information needs to be extracted manually by security analysts, and it is highly dependent on personnel experience. Therefore, this study proposes an IIoT threat intelligence analysis method based on feature weighting and BERT-BiGRU. In this method, BERT-BiGRU is used to classify attack behavior and attack strategy. Then, the attack behavior is weighted to make the classified result more accurate according to the relationship between attack strategy and attack behavior in ATT&CK for ICS knowledge. Finally, the possibility of attack and the harm degree of attack are calculated to form the threat value of the attack. The security analysts can judge the emergency response sequence by the threat value to improve the accuracy and efficiency of emergency response. The results indicate that the proposed method in this study is more accurate than the other standard methods and is more suitable for the unstructured threat intelligence analysis of IIoT.