Visible to the public Preventing Poisoning Attacks On AI Based Threat Intelligence Systems

TitlePreventing Poisoning Attacks On AI Based Threat Intelligence Systems
Publication TypeConference Paper
Year of Publication2019
AuthorsKhurana, N., Mittal, S., Piplai, A., Joshi, A.
Conference Name2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
Date PublishedOct. 2019
ISBN Number978-1-7281-0824-7
KeywordsAI Poisoning, AI systems, artificial intelligence, computer security, cybersecurity domain, Engines, ensembled semi-supervised approach, Human Behavior, learning (artificial intelligence), malicious information, online social media, poisoning attacks prevention, pubcrawl, resilience, Resiliency, Scalability, security analysts, security of data, social networking (online), Support vector machines, threat intelligence systems, Twitter, Web sites

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.

Citation Keykhurana_preventing_2019