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.
Highlights
Introduction e developing application of5G technology [1] improved the communication quality and made it possible to enable the perception and interconnection of infrastructure, personnel, and their environment
To solve the problems above, this study proposes a threat intelligence analysis method based on feature weighting and BERT-BiGRU for industrial Internet of things (IIoT)
2.1. reat Intelligence Analysis. reat intelligence analysis extracts unstructured data such as security warning notification, vulnerability notification, and threat notification from threat intelligence using natural language processing technology and helps the attacked analyze the behavior and vulnerabilities exploited by the attackers so that the attacked can make emergency defense decisions promptly
Summary
2.1. reat Intelligence Analysis. reat intelligence analysis extracts unstructured data such as security warning notification, vulnerability notification, and threat notification from threat intelligence using natural language processing technology and helps the attacked analyze the behavior and vulnerabilities exploited by the attackers so that the attacked can make emergency defense decisions promptly. Reat intelligence analysis extracts unstructured data such as security warning notification, vulnerability notification, and threat notification from threat intelligence using natural language processing technology and helps the attacked analyze the behavior and vulnerabilities exploited by the attackers so that the attacked can make emergency defense decisions promptly. Multilabel classification refers to separately analyzing the task text data with multiple labels. Yang et al [19] proposed a labeled implicit Dirichlet model based on subdividing the data to reduce the time complexity of the multilabel classification algorithm. Wehrmann et al [24] proposed a multilayer output neural network model for multilabel classification; this structure has an output layer at each hierarchical level and provides a global output layer for the entire network to track the label dependency in the hierarchy as a whole by optimizing the sum of the global and each level of a loss function
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
