A Crowdsourcing Game-theoretic Intrusion Detection and Rating System
| Title | A Crowdsourcing Game-theoretic Intrusion Detection and Rating System |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Saab, Farah, Elhajj, Imad, Kayssi, Ayman, Chehab, Ali |
| Conference Name | Proceedings of the 31st Annual ACM Symposium on Applied Computing |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-3739-7 |
| Keywords | app market, app rating, Collaboration, composability, crowdsourcing, expert systems, game theoretic security, game theory, Human Behavior, IDS, Metrics, privacy, pubcrawl, Resiliency, Scalability |
| Abstract | One of the main concerns for smartphone users is the quality of apps they download. Before installing any app from the market, users first check its rating and reviews. However, these ratings are not computed by experts and most times are not associated with malicious behavior. In this work, we present an IDS/rating system based on a game theoretic model with crowdsourcing. Our results show that, with minor control over the error in categorizing users and the fraction of experts in the crowd, our system provides proper ratings while flagging all malicious apps. |
| URL | http://doi.acm.org/10.1145/2851613.2851933 |
| DOI | 10.1145/2851613.2851933 |
| Citation Key | saab_crowdsourcing_2016 |