Preventing Poisoning Attacks on AI based Threat Intelligence Systems

TL;DR

This paper presents an ensembled semi-supervised method to assess the credibility of social media posts, specifically Reddit, to prevent poisoning attacks on AI threat intelligence systems, enhancing their reliability in cybersecurity applications.

Contribution

It introduces a novel ensembled semi-supervised approach for credibility assessment of social media data to secure AI threat intelligence systems against malicious inputs.

Findings

Effective credibility estimation of Reddit posts.

Improved detection of malicious or incorrect information.

Enhanced security of AI-based threat analysis systems.

Abstract

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.