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Papers on Trustworthy Anomaly Detection

1. Surveys on Trustworthy AI

  • (arxiv'21) Trustworthy AI: A Computational Perspective. Liu Haochen, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Yaxin Li, Shaili Jain, Yunhao Liu, Anil K. Jain, and Jiliang Tang. link

  • (JAIR'21) Socially Responsible AI Algorithms: Issues, Purposes, and Challenges. Cheng Lu, Kush R. Varshney, and Huan Liu. link

  • (CSUR'21) Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges. Ashmore Rob, Radu Calinescu, and Colin Paterson. link

  • (arxiv'20) Toward trustworthy AI development: mechanisms for supporting verifiable claims. Brundage Miles, Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen Krueger, Gillian Hadfield, Heidy Khlaaf et al. link

2. Surveys on Anomaly Detection

  • (Proc. IEEE'21) A Unifying Review of Deep and Shallow Anomaly Detection. Ruff Lukas, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, and Klaus-Robert Müller. link

  • (CSUR'21) Deep Learning for Anomaly Detection: A Review. Pang Guansong, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel. link

  • (arxiv'21) Generalized out-of-distribution detection: A survey. Yang, Jingkang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu. link.

  • (arxiv'19) Deep Learning for Anomaly Detection: A Survey. Chalapathy Raghavendra, and Sanjay Chawla. link

2.1 Surveys on Specific Domains

  • (Euro S&P) SoK: Explainable Machine Learning for Computer Security Applications. Azqa Nadeem, Daniël Vos, Clinton Cao, Luca Pajola, Simon Dieck, Robert Baumgartner, and Sicco Verwer. link

  • (arxiv'23) SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability. Dipkamal Bhusal, Rosalyn Shin, Ajay Ashok Shewale, Monish Kumar Manikya Veerabhadran, Michael Clifford, Sara Rampazzi, and Nidhi Rastogi. link

  • (CSUR'22) Anomaly detection and failure root cause analysis in (micro) service-based cloud applications: A survey. Soldani Jacopo, and Antonio Brogi. link

  • (CSUR'21) A review on outlier/anomaly detection in time series data. Blázquez-García Ane, Angel Conde, Usue Mori, and Jose A. Lozano. link

  • (TKDE'21) A Comprehensive Survey on Graph Anomaly Detection with Deep Learning. Ma Xiaoxiao, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, Hui Xiong, and Leman Akoglu. link

  • (arxiv'20) Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art. Mohammad Braei, Sebastian Wagner. link

  • (CSUR'20) Anomaly detection in road traffic using visual surveillance: a survey. Santhosh Kelathodi Kumaran, Debi Prosad Dogra, and Partha Pratim Roy. link

  • (IoT J.'20) Anomaly Detection for IoT Time-Series Data: A Survey. Cook Andrew A., Göksel Mısırlı, and Zhong Fan. link

  • (arxiv'19) A Survey on GANs for Anomaly Detection. Di Mattia, Federico, Paolo Galeone, Michele De Simoni, and Emanuele Ghelfi. link

3. Interpretable Anomaly Detection

Surveys

  • (arxiv'23) Explainable Anomaly Detection in Images and Videos: A Survey. Yizhou Wang, Dongliang Guo, Sheng Li, Octavia Camps, Yun Fu. link

  • (VLDB J.'22) A survey on outlier explanations. Panjei Egawati, Le Gruenwald, Eleazar Leal, Christopher Nguyen, and Shejuti Silvia. link

  • (TKDD'23) A Survey on Explainable Anomaly Detection. Zhong Li, Yuxuan Zhu, Matthijs van Leeuwen link

Approaches

  • (KDD'22) Framing Algorithmic Recourse for Anomaly Detection. Debanjan Datta, Feng Chen, Naren Ramakrishnan. link

  • (CCS'21) DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications. Dongqi Han, Zhiliang Wang, Wenqi Chen, Ying Zhong, Su Wang, Han Zhang, Jiahai Yang, Xingang Shi, and Xia Yin. link

  • (PVLDB'21) Exathlon: a benchmark for explainable anomaly detection over time series. Vincent Jacob, Fei Song, Arnaud Stiegler, Bijan Rad, Yanlei Diao, and Nesime Tatbul. link

  • (ICLR'21) Explainable Deep One-Class Classification. Liznerski Philipp, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, and Klaus-Robert Müller. link

  • (BigData'21) InterpretableSAD: Interpretable Anomaly Detection in Sequential Log Data. Han Xiao, He Cheng, Depeng Xu, and Shuhan Yuan. link

  • (arxiv'21) Explainable Deep Few-shot Anomaly Detection with Deviation Networks. Pang Guansong, Choubo Ding, Chunhua Shen, and Anton van den Hengel. link

  • (ECCV'20) Attention Guided Anomaly Localization in Images. Venkataramanan Shashanka, Kuan-Chuan Peng, Rajat Vikram Singh, and Abhijit Mahalanobis. link

  • (ICML'20) Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure. John Sipple. link

  • (PR'20) Towards explaining anomalies: A deep Taylor decomposition of one-class models. Kauffmann Jacob, Klaus-Robert Müller, and Grégoire Montavon. link

  • (CNS'19) GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection. Nguyen Quoc Phong, Kar Wai Lim, Dinil Mon Divakaran, Kian Hsiang Low, and Mun Choon Chan. link

  • (arxiv'19) Explaining Anomalies Detected by Autoencoders Using SHAP. Antwarg, Liat, Ronnie Mindlin Miller, Bracha Shapira, and Lior Rokach. link

  • (HPDC'18) Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection. Brown Andy, Aaron Tuor, Brian Hutchinson, and Nicole Nichols. link

  • (ECML-PKDD'18) Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features. Nguyen Minh-Nghia, and Ngo Anh Vien. link

4. Fair Anomaly Detection

Approaches

  • (PAKDD'23) Achieving Counterfactual Fairness for Anomaly Detection. Xiao Han, Lu Zhang, Yongkai Wu, and Shuhan Yuan. link

  • (FAccT'21) Towards Fair Deep Anomaly Detection. Zhang Hongjing, and Ian Davidson. link

  • (AIES'21) FairOD: Fairness-aware Outlier Detection. Shekhar Shubhranshu, Neil Shah, and Leman Akoglu. link

  • (KDD'21) Deep Clustering based Fair Outlier Detection. Song Hanyu, Peizhao Li, and Hongfu Liu. link

  • (ECAI'20) A Framework for Determining the Fairness of Outlier Detection. Davidson Ian, and Selvan Suntiha Ravi. link

  • (WISE'20) Fair Outlier Detection. Deepak P., and Savitha Sam Abraham. link

5. Robust Anomaly Detection

Surveys

  • (CSUR'22) Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain. Rosenberg, Ishai, Asaf Shabtai, Yuval Elovici, and Lior Rokach. link

  • (arxiv'19) The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey. Ibitoye, Olakunle, Rana Abou-Khamis, Ashraf Matrawy, and M. Omair Shafiq. link

Approaches

  • (PAKDD'22) IDSGAN: Generative Adversarial Networks for Attack Generation Against Intrusion Detection. Zilong Lin, Yong Shi, and Zhi Xue. link

  • (DTRAP'22) Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems. Apruzzese, Giovanni, Mauro Andreolini, Luca Ferretti, Mirco Marchetti, and Michele Colajanni. link

  • (arxiv'21) Adversarially Robust One-class Novelty Detection. Lo, Shao-Yuan, Poojan Oza, and Vishal M. Patel. link

  • (IJCAI'21) Robustness of Autoencoders for Anomaly Detection Under Adversarial Impact. Goodge, Adam, Bryan Hooi, See-Kiong Ng, and Wee Siong Ng. link

  • (TrustCom'21) An Approach for Poisoning Attacks Against RNN-Based Cyber Anomaly Detection. Xu, Jinghui, Yu Wen, Chun Yang, and Dan Meng. link

  • (IJCIP'21) Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems. Jia, Yifan, Jingyi Wang, Christopher M. Poskitt, Sudipta Chattopadhyay, Jun Sun, and Yuqi Chen. link

  • (S&P'20) Intriguing properties of adversarial ml attacks in the problem space. Pierazzi, Fabio, Feargus Pendlebury, Jacopo Cortellazzi, and Lorenzo Cavallaro. link

  • (ACSAC'20) Constrained concealment attacks against reconstruction-based anomaly detectors in industrial control systems. Erba, Alessandro, Riccardo Taormina, Stefano Galelli, Marcello Pogliani, Michele Carminati, Stefano Zanero, and Nils Ole Tippenhauer link

  • (ICLR'20) Robust anomaly detection and backdoor attack detection via differential privacy. Du, Min, Ruoxi Jia, and Dawn Song. link

  • (AISTATS'10) Online Anomaly Detection under Adversarial Impact. Kloft, Marius, and Pavel Laskov. link

6. Privacy Preserving Anomaly Detection

Perturbation-based Approaches

  • (SIGMOD'21) PCOR: Private Contextual Outlier Release via Differentially Private Search. Masoumeh Shafieinejad, Florian Kerschbaum, and Ihab F. Ilyas. link

  • (TII'21) Security and Privacy-Enhanced Federated Learning for Anomaly Detection in IoT Infrastructures. Cui, Lei, Youyang Qu, Gang Xie, Deze Zeng, Ruidong Li, Shigen Shen, and Shui Yu. link

  • (ECML-PKDD'15) Differentially private analysis of outliers. Okada, Rina, Kazuto Fukuchi, and Jun Sakuma. link

  • (ICDMW'13) Differentially Private Anomaly Detection with a Case Study on Epidemic Outbreak Detection. Fan, Liyue, and Li Xiong. link

Anonymization-based Approaches

  • (TSC'19) An Integrated Framework for Privacy-Preserving Based Anomaly Detection for Cyber-Physical Systems. Keshk, Marwa, Elena Sitnikova, Nour Moustafa, Jiankun Hu, and Ibrahim Khalil. link

Cryptographic-based approach

  • (ICDE'20) Privacy-preserving Real-time Anomaly Detection Using Edge Computing. Mehnaz, Shagufta, and Elisa Bertino. link

  • (KIS'15) Privacy-preserving LOF outlier detection. Li, Lu, Liusheng Huang, Wei Yang, Xiaohui Yao, and An Liu. link

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