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Khurana, N., Mittal, S., Piplai, A., Joshi, A..  2019.  Preventing Poisoning Attacks On AI Based Threat Intelligence Systems. 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP). :1—6.

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.

Wang, Shi-wen, Xia, Hui.  2018.  A Reputation Management Framework for MANETs. 2018 IEEE Symposium on Privacy-Aware Computing (PAC). :119—120.
Resistance to malicious attacks and assessment of the trust value of nodes are important aspects of trusted mobile ad hoc networks (MANETs), and it is therefore necessary to establish an effective reputation management system. Previous studies have relied on the direct monitoring of nodes, recommendations from neighbors or a combination of these two methods to calculate a reputation value. However, these models can neither collect trust information effectively, nor cooperate to resist an attack, instead increasing the network load. To solve these problems, this paper proposes a novel reputation management framework that collects trust information and calculates the reputation value of nodes by selecting special nodes as management nodes. This framework can effectively identify malicious information and improve the credibility of a reputation value.