GLM-Based Fake Cybersecurity Threat Intelligence Detection Models and Algorithms
Source abstract
Abstract
Deepfakes, a form of artificial intelligence-generated content, represent a primary method for creating fake cybersecurity threat intelligence (CTI) and are a key source for data poisoning attacks. To effectively assess the authenticity of the cybersecurity threat intelligences (CTIs) in the open-source community, this study presents a novel algorithm for fake-CTI mining, aimed at identifying fake CTIs. Initially, we develop a generalized language model (GLM)-based system for fake CTI generation (GLM-based FCTIG) that adapts a public GLM to the cybersecurity domain using parameter-efficient fine-tuning. We then integrate these fabricated CTIs with authentic ones to simulate data poisoning attacks, evaluating the outcomes of data poisoning attacks from various perspectives. Additionally, we propose a hybrid classification model based on a fine-tuned BERT encoder and a TextCNN head (FCTICM-TC) to discern the authenticity of the CTIs. Finally, a comprehensive FCTICM-TC-based FCTIM method for mining the fake CTIs is presented. The experimental results demonstrate that the proposed GLM-based FCTIG scheme and FCTICM-TC model can effectively generate convincing fake CTI-like texts, while the FCTICM-TC-based FCTIM method is capable of identifying the fake CTIs efficiently.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Spam and Phishing Detection · Advanced Malware Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Funding
the Smart Manufacturing New Model Application Project, Ministry of Industry and Information Technology
References (37)
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