hrcak mascot   Srce   HID

Izvorni znanstveni članak
https://doi.org/10.20532/cit.2018.1003948

An Obfuscated Attack Detection Approach for Collaborative Recommender Systems

Saakshi Kapoor ; Department of Computer Science and Engineering, UIET, Panjab University, Chandigarh, India
Vishal Gupta ; Department of Computer Science and Engineering, UIET, Panjab University, Chandigarh, India
Rohit Kumar ; Department of Computer Science and Engineering, UIET, Panjab University, Chandigarh, India

Puni tekst: engleski, pdf (416 KB) str. 45-56 preuzimanja: 259* citiraj
APA 6th Edition
Kapoor, S., Gupta, V. i Kumar, R. (2018). An Obfuscated Attack Detection Approach for Collaborative Recommender Systems. Journal of computing and information technology, 26 (1), 45-56. https://doi.org/10.20532/cit.2018.1003948
MLA 8th Edition
Kapoor, Saakshi, et al. "An Obfuscated Attack Detection Approach for Collaborative Recommender Systems." Journal of computing and information technology, vol. 26, br. 1, 2018, str. 45-56. https://doi.org/10.20532/cit.2018.1003948. Citirano 28.02.2021.
Chicago 17th Edition
Kapoor, Saakshi, Vishal Gupta i Rohit Kumar. "An Obfuscated Attack Detection Approach for Collaborative Recommender Systems." Journal of computing and information technology 26, br. 1 (2018): 45-56. https://doi.org/10.20532/cit.2018.1003948
Harvard
Kapoor, S., Gupta, V., i Kumar, R. (2018). 'An Obfuscated Attack Detection Approach for Collaborative Recommender Systems', Journal of computing and information technology, 26(1), str. 45-56. https://doi.org/10.20532/cit.2018.1003948
Vancouver
Kapoor S, Gupta V, Kumar R. An Obfuscated Attack Detection Approach for Collaborative Recommender Systems. Journal of computing and information technology [Internet]. 2018 [pristupljeno 28.02.2021.];26(1):45-56. https://doi.org/10.20532/cit.2018.1003948
IEEE
S. Kapoor, V. Gupta i R. Kumar, "An Obfuscated Attack Detection Approach for Collaborative Recommender Systems", Journal of computing and information technology, vol.26, br. 1, str. 45-56, 2018. [Online]. https://doi.org/10.20532/cit.2018.1003948

Sažetak
In recent times, we have loads and loads of information available over the Internet. It has become very cumbersome to extract relevant information out of this huge amount of information available. So to avoid this problem “Recommender Systems” came into play, they can predict outcomes according to user’s interests. Although Recommender Systems are very effective and useful for users but the mostly used type of Recommender System i.e. Collaborative Filtering Recommender System suffers from shilling/profile injection attacks in which fake profiles are inserted into the database in order to bias its output. With this problem in mind we propose an approach to detect attacks on Recommender Systems using Random Forest Classifier and found that when tested at 10% attack, our approach outperformed earlier proposed approaches.

Ključne riječi
collaborative recommender systems; obfuscated attack; random forest classifier; SVM

Hrčak ID: 203982

URI
https://hrcak.srce.hr/203982

Posjeta: 384 *