An intruder detection approach based on infrequent rating pattern mining
José María Luna, Aurora Ramírez, José Raúl Romero, Sebastián Ventura · 2010
This work presents a novel proposal for incremental intruder detection in collaborative recommender systems. We explore the use of rare association rule mining to reveal the existence of a suspected raid of attackers that would alter the normal behaviour of a rating-based system. In this position paper we have extended our previous G3PARM algorithm, which has already proven to serve as a solid method for extracting frequent association rules. G3PARM is an evolutionary algorithm that uses G3P (Grammar Guided Genetic Programming), which provides expressiveness and flexibility enough to adapt and apply the base context-free grammar to each specific problem or domain. We fully outline, moreover, the complete exploration and detection model, which includes some further post-analysis steps. Finally, as a proof of concept, we validate the scalability, efficiency and accuracy of our proposal showing the results obtained when different malicious intruders want to attack an on line recommender system.