A Survey of Poisoning Attacks and Countermeasures in Recommender Systems

A repository of poison attacks against recommender systems, as well as their countermeasures. This repository is associated with our systematic review, entitled Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures.

• Existing Surveys
• Taxonomy
• Poison Attacks
  • Model-agnostic
  • Model-intrinsic
• Countermeasures
  • Supervised
  • Semi Supervised
  • Unsupervised
• Datasets

A sortable version is available here: https://awesome-recsys-poisoning.github.io/

🔖 News!!!

📌 We are actively tracking the latest research and welcome contributions to our repository and survey paper. If your studies are relevant, please feel free to create an issue or a pull request.

📰 2024-10-09: Our paper Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures has been published by ACM Computing Surveys (CSUR) (IF 16.6, Top 2%, CORE A*) at here.

📰 2024-06-20: Our paper Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures has been accepted by ACM Computing Surveys (CSUR) (IF 16.6, Top 2%, CORE A*) in 2024.

Citation

If you find this work helpful in your research, welcome to cite the paper and give a ⭐.

Please read and cite our paper: https://dl.acm.org/doi/10.1145/3677328

Nguyen, T.T., Quoc Viet Hung, N., Nguyen, T.T., Huynh, T.T., Nguyen, T.T., Weidlich, M. and Yin, H., 2024. Manipulating recommender systems: A survey of poisoning attacks and countermeasures. ACM Computing Surveys.

@article{nguyen2024manipulating,
  title={Manipulating recommender systems: A survey of poisoning attacks and countermeasures},
  author={Nguyen, Thanh Toan and Quoc Viet Hung, Nguyen and Nguyen, Thanh Tam and Huynh, Thanh Trung and Nguyen, Thanh Thi and Weidlich, Matthias and Yin, Hongzhi},
  journal={ACM Computing Surveys},
  year={2024},
  publisher={ACM New York, NY}
}

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Existing Surveys

Paper Title	Venue	Year	Note
Poisoning Attacks against Recommender Systems: A Survey	arXiv	2024	Focus on benchmarking
Manipulating vulnerability: Poisoning attacks and countermeasures in federated cloud–edge–client learning for image classification	KBS	2023	Focus on federated learning
A Survey on Data Poisoning Attacks and Defenses	DSC	2022	Not focus on recommender systems
A survey of attack detection approaches in collaborative filtering recommender systems	Artificial Intelligence Review	2021	Classic heuristic attacks only
Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey	IEEE Access	2020	Classic heuristic attacks only
Shilling attacks against collaborative recommender systems: a review	Artificial Intelligence Review	2020	Classic heuristic attacks only
A Comparative Study on Shilling Detection Methods for Trustworthy Recommendations	SESC	2018	Classic heuristic attacks only
A comparative study of shilling attack detectors for recommender systems	ICSSSM	2015	Classic heuristic attacks only

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Taxonomy

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Poison Attacks

Poisoning attacks are the process of tampering with the training data of a machine learning (ML) model in order to corrupt its availability and integrity. Below figure presents the typical process of a poisoning attack compared to the normal learning process. In the latter case, an ML model is trained based on data, which is subsequently used to derive a recommendation. As such, the quality of the ML model depends on the quality of the data used for training. In a poisoning attack, data is injected into the training process, and hence the model, to produce unintended or harmful conclusions.

Paper	Venue	Year	Type	Data	Code
Poisoning GNN-based recommender systems with generative surrogate-based attacks	ACM TOIS	2023	Model-intrinsic	FR, ML, AMCP, LF	-
Shilling Black-Box Recommender Systems by Learning to Generate Fake User Profiles	IEEE Trans. Neural Netw. Learn. Syst	2022	Model-agnostic	ML, FT, YE, AAT	-
Knowledge enhanced Black-box Attacks for Recommendations	KDD	2022	Model-agnostic	ML, BC, LA	-
LOKI: A Practical Data Poisoning Attack Frameworkagainst Next Item Recommendations	TKDE	2022	Model-agnostic	ABT, St,GOW	-
Pipattack: Poisoning federated recommender systems for manipulating item promotion	WSDM	2022	Model-intrinsic	ML, AMCP	-
Poisoning Deep Learning based Recommender Model in Federated Learning Scenarios	IJCAI	2022	Model-intrinsic	ML, ADM	Python
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling	Arxiv	2022	Model-intrinsic	ML, ABT	Python
UA-FedRec: Untargeted Attack on Federated News Recommendation	Arxiv	2022	Model-intrinsic	MIND, Feeds	Python
Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems	KDD	2021	Model-agnostic	ML, FT	Python
Reverse Attack: Black-box Attacks on Collaborative Recommendation	CSS	2021	Model-agnostic	ML, NF, AMB & ADM, TW, G+, CIT	-
Attacking Black-box Recommendations via Copying Cross-domain User Profiles	ICDE	2021	Model-agnostic	ML, NF	-
Simulating real profiles for shilling attacks: A generative approach	KBS	2021	Model-agnostic	ML	-
Ready for emerging threats to recommender systems? A graph convolution-based generative shilling attack	IS	2021	Model-agnostic	DB, CI	Python
Data poisoning attacks on neighborhoodbased recommender systems	ETT	2021	Model-intrinsic	FT, ML, AMV	-
Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data	KDD	2021	Model-intrinsic	ML, AIV	-
Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction	RecSys	2021	Model-intrinsic	ML,St, ABT	Python
Poisoning attacks against knowledge graph-based recommendation systems using deep reinforcement learning	Neural. Comput. Appl.	2021	Model-intrinsic	ML, FTr	-
Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start	WWW	2021	Model-intrinsic	AMMN, TC	Python
Data poisoning attacks to deep learning based recommender systems	arXiv	2021	Model-intrinsic	ML, LA	-
Practical data poisoning attack against next-item recommendation	WWW	2020	Model-intrinsic	ABT	-
Influence function based data poisoning attacks to top-n recommender systems	WWW	2020	Model-intrinsic	YE, ADM	-
Attacking recommender systems with augmented user profiles	CIKM	2020	Model-agnostic	ML, FT, AAT	-
Poisonrec: an adaptive data poisoning framework for attacking black-box recommender systems	ICDE	2020	Model-agnostic	St, AMCP, ML	-
Revisiting adversarially learned injection attacks against recommender systems	RecSys	2020	Model-agnostic	GOW	Python
Adversarial attacks on an oblivious recommender	RecSys	2019	Model-intrinsic	ML	-
Targeted poisoning attacks on social recommender systems	GLOBECOM	2019	Model-intrinsic	FT	-
Data poisoning attacks on cross-domain recommendation	CIKM	2019	Model-intrinsic	NF, ML	-
Poisoning attacks to graph-based recommender systems	ACSAC	2018	Model-intrinsic	ML, AIV	-
Fake Co-visitation Injection Attacks to Recommender Systems	NDSS	2017	Model-intrinsic	YT, eB, AMV, YE, LI	-
Data poisoning attacks on factorization-based collaborative filtering	NIPS	2016	Model-intrinsic	ML	Python
Shilling recommender systems for fun and profit	WWW	2004	Model-agnostic	ML

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Countermeasures

In this section, we review detection methods in more detail, starting with supervised methods, before turning to semi-supervised methods and unsupervised methods

Paper	Venue	Year	Type	Data	Code
Anti-FakeU: Defending Shilling Attacks on Graph Neural Network based Recommender Model	WWW	2023	UnSupervised	GOW, Yelp	-
An unsupervised detection method for shilling attacks based on deep learning and community detection	Soft Comput.	2021	UnSupervised	NF, AMV	-
Identification of Malicious Injection Attacks in Dense Rating and Co-Visitation Behaviors	TIFS	2020	UnSupervised	ML, AMB, LT, Trip	-
Recommendation attack detection based on deep learning	JISA	2020	Supervised	ML	-
Detecting shilling attacks with automatic features from multiple views	Secur. Commun. Netw.	2019	Supervised	NF, ML, AMV	-
Detecting shilling attacks in social recommender systems based on time series analysis and trust features	KBS	2019	Supervised	CI, EP	-
BS-SC: An Unsupervised Approach for Detecting Shilling Profiles in Collaborative Recommender Systems	TKDE	2019	UnSupervised	SYN, AMP	-
Detecting shilling attacks in recommender systems based on analysis of user rating behavior	KBS	2019	UnSupervised	NF, ML, AMP	-
Trustworthy and profit: A new value-based neighbor selection method in recommender systems under shilling attacks	DSS	2019	UnSupervised	BC, AMB	-
Quick and accurate attack detection in recommender systems through user attributes	RecSys	2019	UnSupervised	ML	-
UD-HMM: An unsupervised method for shilling attack detection based on hidden Markov model and hierarchical clustering	KBS	2018	UnSupervised	ML, NF, AMP	-
Shilling attack detection for recommender systems based on credibility of group users and rating time series	Plos One	2018	UnSupervised	ML	-
Spotting anomalous ratings for rating systems by analyzing target users and items	Neurocomputing	2017	UnSupervised	ML, AMP	-
Estimating user behavior toward detecting anomalous ratings in rating systems	KBS	2016	UnSupervised	ML	-
SVM-TIA a shilling attack detection method based on SVM and target item analysis in recommender systems	J. Neucom.	2016	Supervised	ML	-
Re-scale AdaBoost for attack detection in collaborative filtering recommender systems	KBS	2016	Supervised	ML	-
Catch the black sheep: unified framework for shilling attack detection based on fraudulent action propagation	IJCAI	2015	UnSupervised	AMV	-
Shilling attacks detection in recommender systems based on target item analysis	Plos One	2015	UnSupervised	ML, NF, EM	-
A novel item anomaly detection approach against shilling attacks in collaborative recommendation systems using the dynamic time interval segmentation technique	IS	2015	UnSupervised	ML	-
A novel shilling attack detection method	Procedia Comput. Sci.	2014	UnSupervised	ML	-
Detection of abnormal profiles on group attacks in recommender systems	SIGIR	2014	UnSupervised	NF, ML, EM	-
HHT–SVM: An online method for detecting profile injection attacks in collaborative recommender systems	KBS	2014	Supervised	ML	-
Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system	WWW	2013	Semi Supervised	ML	-
𝛽P: A novel approach to filter out malicious rating profiles from recommender systems	DSS	2013	UnSupervised	ML	-
A Meta-learning-based Approach for Detecting Profile Injection Attacks in Collaborative Recommender Systems	J. Comput.	2012	Supervised	ML	-
HySAD: A semi-supervised hybrid shilling attack detector for trustworthy product recommendation	KDD	2012	Semi Supervised	NF, ML	-
A clustering approach to unsupervised attack detection in collaborative recommender systems	ICDATA	2011	UnSupervised	ML	-
Unsupervised strategies for shilling detection and robust collaborative filtering	User Model. User-Adapt. Interact.	2009	UnSupervised	ML	-
Unsupervised retrieval of attack profiles in collaborative recommender systems	RecSys	2008	UnSupervised	ML	-
Lies and propaganda: detecting spam users in collaborative filtering	IUI	2007	UnSupervised	ML	-
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness	TOIT	2007	Supervised	ML	-
Classification features for attack detection in collaborative recommender systems	KDD	2006	Supervised	ML	-
Detecting profile injection attacks in collaborative recommender systems	CEC-EEE	2006	Supervised	ML	-
Attack detection in time series for recommender systems	KDD	2006	UnSupervised	ML	-
Preventing shilling attacks in online recommender systems	WIDM	2005	Supervised	ML	-

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Datasets

Dataset	Name
AAT	Amazon Automotive
ABT	Amazon Beauty
ADM	Amazon Digital Music
AIV	Amazon Instant Video
AMB	Amazon Book
AMMN	Amazon Men
AMV	Amazon Movie
AMCP	Amazon Cell-phone
AMP	Amazon Product
BC	Book-Crossing
CI	Ciao
CIT	Citation Network
DB	Douban
eB	eBay
EM	Eachmovie
EP	Epinions
Feeds	Microsoft news App
FR	FRappe - Mobile App Recommendations
FT	Film Trust
Ftr	Fund Transactions
G+	Google+
GOW	Gowalla
MIND	Microsoft News
LA	Last.fm
LI	Linkedin
LT	LibraryThing
ML	MovieLens
NF	Netflix
St	Steam
TC	Tradesy
Trip	TripAdvisor
TW	Twitter [Code]
YE	Yelp
YT	YouTube
SYNC	Synthetic datasets

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Disclaimer

Feel free to contact us if you have any queries or exciting news. In addition, we welcome all researchers to contribute to this repository and further contribute to the knowledge of this field.

If you have some other related references, please feel free to create a Github issue with the paper information. We will gladly update the repos according to your suggestions. (You can also create pull requests, but it might take some time for us to do the merge)