Preventing Poisoning Attacks on AI based Threat Intelligence Systems
July 19, 2018 Β· Declared Dead Β· π International Workshop on Machine Learning for Signal Processing
"No code URL or promise found in abstract"
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Authors
Nitika Khurana, Sudip Mittal, Anupam Joshi
arXiv ID
1807.07418
Category
cs.SI: Social & Info Networks
Cross-listed
cs.CR,
cs.LG
Citations
41
Venue
International Workshop on Machine Learning for Signal Processing
Last Checked
3 months ago
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.
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