Naslov
Detection of Shilling Attacks on Collaborative Filtering Recommender Systems by Combining Multiple Random Forest Models

Autori
Grozdanić, Vjeran ; Vladimir, Klemo ; Delač, Goran ; Šilić, Marin ;

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the International Conference on Computers in Technical Systems MIPRO 2023 Opatija / Skala, Karolj - Opatija : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2023, 1112-1116

Skup
46th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2023)

Mjesto i datum
Opatija, Hrvatska, 22.05.2023. - 26.05.2023

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
recommender systems ; collaborative filtering ; attack-resistant recommender systems ; shilling attacks ; attack detection

Sažetak
Collaborative filtering recommender systems are one of the essential recommender systems and are still widely used in combination with other algorithms to make predictions for users. However, they are vulnerable to shilling attacks, and if there isn’t any detection system to prevent those attacks, original recommendations can be heavily influenced to benefit the attackers. Designing attack-resistant recommendation systems is not an easy task, and many researchers have tried to tackle that problem. In this paper, a new approach that combines multiple random forest models is proposed. Each of the random forest models is specialized in detecting one group of shilling attacks, and then all the models are combined into an ensemble model. Experimental results show that the proposed ensemble is capable of detecting attack profiles at high rate without causing significant bias in the original recommendation system.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA