Şilin Ataklarının Tek Sınıflı Destek Vektör Makinaları ile Tespiti

Year 2023, , 246 - 256, 31.12.2023

Halil İbrahim Ayaz , Zehra Kamışlı Öztürk

https://doi.org/10.47112/neufmbd.2023.22

Abstract

Öneri sistemleri çeşitli çevrimiçi platformlarda hayati bir rol oynar ve kullanıcıların tercihlerini göz önünde bulundurarak yeni ürünler, hizmetler ve içerikler keşfetmelerine yardımcı olur. Bununla birlikte, bu sistemler, kötü niyetli kullanıcıların derecelendirmeleri yapay olarak şişirdiği veya söndürdüğü ve önyargılı önerilere yol açtığı şilin saldırıları yoluyla manipülasyona karşı savunmasızdır. Bu saldırıları araştırmanın, anlamanın ve hafifletmenin önemini vurgulamak çok önemlidir. Bu tür saldırıları tespit etmek, tavsiye sistemlerinin bütünlüğünü ve etkinliğini korumak için çok önemlidir. Literatürde, şilin saldırılarını tespit etmek için birçok çalışma sunulmuştur. En iyi bilinen kümeleme yöntemleri farklı saldırı modelleri için uyarlanmıştır. Bu makalede, şilin saldırılarını tespit etmek için gürbüz bir teknik olarak Tek Sınıflı Destek Vektör Makineleri kullanımını araştırıyoruz. Tek Sınıflı Destek Vektör Makinaları, öncelikle anomali tespiti ve aykırılık tespiti görevleri için tasarlanmış geleneksel Destek Vektör Makinelerinin özel bir çeşididir. Önerilen yöntemi doğrulamak için MovieLens100K veri kümesi kullanılmıştır. Sonuç olarak, farklı boyut ve doluluk oranlı saldırılar için hassasiyet ve geri çağırma değerleri verilmiştir.

Keywords

Öneri sistemleri, Şilin atakları, Tek sınıflı destek vektör makinaları

Supporting Institution

Eskişehir Teknik Üniversitesi

Project Number

20DRP026

Shilling Attack Detection with One Class Support Vector Machines

Abstract

Recommender systems play a vital role in various online platforms, assisting users in discovering new products, services, and content considering their preferences. However, these systems are vulnerable to manipulation through shilling attacks, where malicious users artificially inflate or deflate ratings, leading to biased recommendations. It is crucial to emphasize the importance of researching, understanding, and mitigating these attacks. Detecting such attacks is crucial to maintaining the integrity and effectiveness of recommender systems. In the literature, lots of studies are presented to detect shilling attacks. The most well-known clustering methods are adapted for different attack models. This paper explores using One-Class Support Vector Machines (OCSVM) as a robust technique for detecting shilling attacks. One-Class SVMs are a specialized variant of the traditional Support Vector Machines, primarily designed for anomaly and novelty detection tasks. MovieLens100K dataset is used to validate the proposed method. As a result, precision and recall values are given for different attack and filler sizes.

Keywords

Recommender systems, Shilling attacks, OCSVM

Project Number

20DRP026

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