Security threats and their mitigation in big data recommender systems
Big data recommender systems are very vulnerable to attacks, especially to profile injection attacks. So, we should use security mechanisms to protect big data recommender systems from different kinds of attacks. These vulnerabilities and attacks may decrease users' trust in accuracy of recommender systems. In addition, issues related to big data recommender systems and their security problems are based on security challenges in Hadoop architecture which is called Hadoop Distributed File System (HDFS). In this chapter, we investigate a number of known attack models, examine their influence and suggest some solutions to combat them. Furthermore, we represent different methods that are used by attackers to modify an attack so it is not recognized as an attack. We consider important issues in creating secure big data recommender systems, focusing on attack models and their effect on different big data recommender approaches. We know the general effect on systems' ability to predict accurately, and we also know the amount of knowledge that attackers need to know about the system to deploy a realistic attack. In this chapter, we show that the two approaches, i.e. user-based and item-based algorithms, are particularly vulnerable to attack patterns, but hybrid algorithms that are the combination of both user-based and item-based algorithms may present higher stability. Also, we study the basics of relevant research and advanced schemas, and discuss future thoughts.
Security threats and their mitigation in big data recommender systems, Page 1 of 2
< Previous page Next page > /docserver/preview/fulltext/books/pc/pbpc035f/PBPC035F_ch11-1.gif /docserver/preview/fulltext/books/pc/pbpc035f/PBPC035F_ch11-2.gif