정보 수준을 이용한 강건한 시빌공격 방어 알고리즘 설계 및 분석

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 8. 김종권.추천 시스템(Recommender System, RS)은 궁극적인 소비자 (즉, 추천 시스템 사용자)에게 상업적인 아이템들을 추천해 주는 것이 주요 기능이다. 추천 시스템에서 정확한 정보를 제공하는 것은 추천 서비스 공급자와 시스템 사용자 모두에게 중요하다. 온라인 소셜 네트워크의 확산으로 추천 시스템의 영향력은 급격히 증가하고 있다. 반면에 추천 시스템의 의도와는 반대로 정보를 조작하는 거짓 아이덴터티들을 사용한 악의적인 사용자들의 추천 시스템에 대한 공격이 증가하고 있다. 이러한 거짓 아이덴터티들을 활용한 공격을 시빌(Sybil) 공격이라 부른다. 본 논문에서는 다른 연구에서 소개된 적이 없는 어드미션 통제 개념을 활용한 RobuRec이라 불리는 새로운 강건한 추천 시스템을 제안한다. 어드미션 통제라는 강력한 개념을 활용하여 정직한 사용자가 생성한 평가인지 혹은 시빌 아이덴터티들을 활용한 악의적인 평가인지에 관계없이 고신뢰 수준의 추천을 예측할 수 있다. RobuRec 시스템의 성능을 보이기 위해, 본 논문에서는 여러가지 가능한 시빌 공격 시나리오는 물론 다양한 데이터셋을 활용하여 광범위한 실험을 수행하였다. RobuRec은 실험 및 분석을 통해 RobuRec과 비교 가능한 PCA (Principal Component Analysis) 방식 및 LTSMF (Least Trimmed Squared Matrix Factorization) 방식보다 프리딕션 쉬프트 (Prediction Shift, PS) 및 적중 비율(Hit Ratio, HR)에서 월등한 성능을 보여 주었다.As the major function of Recommender Systems (RSs) is recommending commercial items to potential consumers (i.e., system users), providing correct information of RS is crucial to both RS providers and system users. The influence of RS over Online Social Networks (OSNs) is expanding rapidly, whereas malicious users continuously try to attack the RSs with fake identities (i.e., Sybils) by manipulating the information in the RS adversely. In this thesis, we propose a novel robust recommendation algorithm called RobuRec which exploits a distinctive feature, admission control. RobuRec provides highly Trusted recommendation results since RobuRec predicts appropriate recommendations regardless of whether the ratings are given by honest users or by Sybils thanks to the power of admission control. To demonstrate the performance of RobuRec, we have conducted extensive exper iments with various datasets as well as diverse attack scenarios. The evaluation results confirm that RobuRec outperforms the comparable schemes such as Principal Component Analysis (PCA) and Least Trimmed Squared Matrix Factorization (LTSMF) significantly in terms of Prediction Shift (PS) and Hit Ratio (HR).Chapter 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . 1 1.2 Goal and Contribution . . . . . . . . . . . . . . 3 1.3 Thesis Organization . . . . . . . . . . . . . . . 6 Chapter 2 Related Work 7 2.1 RS approaches . . . . . . . . . . . . . . . . . . 7 2.2 Sybil Attack Defense . . . . . . . . . . . . . . 9 2.3 Robust RS Approaches . . . . . . . . . . . . . . 10 Chapter 3 System Model 13 3.1 Target Applications . . . . . . . . . . . . . . 17 3.2 Strong Attacker . . . . . . . . . . . . . . . . 17 3.3 Attack Model . . . . . . . . . . . . . . . . . . 18 3.4 Model Assumptions . . . . . . . . . . . . . . . 21 Chapter 4 RobuRec Design 23 4.1 Algorithm Intuition . . . . . . . . . . . . . . 23 4.2 Initialization Phase . . . . . . . . . . . . . . 25 4.3 Admission Control Phase . . . . . . . . . . . . 26 4.4 Rating Prediction Phase . . . . . . . . . . . . 30 4.5 Dynamic Parameter Control . . . . . . . . . . . 35 4.5.1 Simplifying Control Parameters . . . . . . . . 36 4.5.2 Dynamic Cmax Control . . . . . . . . . . . . . 37 4.5.3 Dynamic Global and Local Control . . . . . . 42 Chapter 5 Evaluation and Analysis 45 5.1 Evaluation Metrics . . . . . . . . . . . . . . . 45 5.2 Parameter (alpha) Study . . . . . . . . . . . . 47 5.3 Datasets and Setup . . . . . . . . . . . . . . . 48 5.4 Results and Analysis . . . . . . . . . . . . . . 52 5.4.1 Performance on PS . . . . . . . . . . . . . . 52 5.4.2 Impact of Filler Size . . . . . . . . . . . . 55 5.4.3 Impact of Target Selection Strategy . . . . . 58 5.4.4 Dynamic Parameter Control . . . . . . . . . . 59 5.4.5 Performance on HR . . . . . . . . . . . . . . 62 5.4.6 Analysis on Escaping Probability . . . . . . . 63 Chapter 6 Conclusion 67Docto

Unsupervised Retrieval of Attack Profiles in Collaborative Recommender Systems

Trust, reputation and recommendation are key components of successful ecommerce systems. However, ecommerce systems are also vulnerable in this respect because there are opportunities for sellers to gain advantage through manipulation of reputation and recommendation. One such vulnerability is the use of fraudulent user profiles to boost (or damage) the ratings of items in an online recommender system. In this paper we cast this problem as a problem of detecting anomalous structure in network analysis and propose a novel mechanism for detecting this anomalous structure. We present an evaluation that shows that this approach is effective at uncovering the types of recommender systems attack described in the literature.Science Foundation Irelan