Preventing Poisoning Attacks on AI based Threat Intelligence Systems
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Abstract (arXiv)
As AI systems become more ubiquitous, securing them becomes an emerging challenge. Over the years, with the surge in online social media use and the data available for analysis, AI systems have been built to extract, represent and use this information. The credibility of this information extracted from open sources, however, can often be questionable. Malicious or incorrect information can cause a loss of money, reputation, and resources; and in certain situations, pose a threat to human life. In this paper, we use an ensembled semi-supervised approach to determine the credibility of Reddit posts by estimating their reputation score to ensure the validity of information ingested by AI systems. We demonstrate our approach in the cybersecurity domain, where security analysts utilize these systems to determine possible threats by analyzing the data scattered on social media websites, forums, blogs, etc.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jul 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 1.
4 papers.
- 2021Generating Fake Cyber Threat Intelligence Using Transformer-Based Modelscited by 28
- 2023Impacts and Risk of Generative AI Technology on Cyber Defensecited by 8
- 2024TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent Systemcited by 6
- 2023AI Security Threats against Pervasive Robotic Systems: A Course for Next Generation Cybersecurity Workforcecited by 1
- 2024TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System14 Dec 2024
- 2023Impacts and Risk of Generative AI Technology on Cyber Defense22 Jun 2023
- 2023AI Security Threats against Pervasive Robotic Systems: A Course for Next Generation Cybersecurity Workforce15 Feb 2023
- 2021Generating Fake Cyber Threat Intelligence Using Transformer-Based Models8 Feb 2021