# Interactive System-wise Anomaly Detection

arXiv: https://arxiv.org/abs/2304.10704 · HTML: https://mlsanity.com/paper/2304.10704/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Builds on (most cited first, 21 of 21)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning](https://mlsanity.com/paper/1805.10000/) (2018)
- [Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series](https://mlsanity.com/paper/1809.04758/) (2018)
- [Experience Replay Optimization](https://mlsanity.com/paper/1906.08387/) (2019)
- [TODS: An Automated Time Series Outlier Detection System](https://mlsanity.com/paper/2009.09822/) (2020)
- [Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks](https://mlsanity.com/paper/1710.09207/) (2017)
- [Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning](https://mlsanity.com/paper/2009.07415/) (2020)
- [Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network](https://mlsanity.com/paper/1703.09752/) (2017)
- [Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure](https://mlsanity.com/paper/2007.10088/) (2020)
- [Out-of-Distribution Detection using Multiple Semantic Label Representations](https://mlsanity.com/paper/1808.06664/) (2018)
- [PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning](https://mlsanity.com/paper/2003.05602/) (2020)
- [Contextual Outlier Interpretation](https://mlsanity.com/paper/1711.10589/) (2017)
- [Generic Outlier Detection in Multi-Armed Bandit](https://mlsanity.com/paper/2007.07293/) (2020)
- [Detecting Abnormal Profiles in Collaborative Filtering Recommender Systems](https://mlsanity.com/paper/1506.05752/) (2015)

## Abstract (arXiv)

Anomaly detection, where data instances are discovered containing feature patterns different from the majority, plays a fundamental role in various applications. However, it is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. Appropriate interactions are needed to interact with the systems and identify those with abnormal responses. Detecting system-wise anomalies is a challenging task due to several reasons including: how to formally define the system-wise anomaly detection problem; how to find the effective activation signal for interacting with systems to progressively collect the data and learn the detector; how to guarantee stable training in such a non-stationary scenario with real-time interactions? To address the challenges, we propose InterSAD (Interactive System-wise Anomaly Detection). Specifically, first, we adopt Markov decision process to model the interactive systems, and define anomalous systems as anomalous transition and anomalous reward systems. Then, we develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings, and a policy network to generate effective activation for separating embeddings of normal and anomaly systems. Finally, we design a training method to stabilize the learning process, which includes a replay buffer to store historical interaction data and allow them to be re-sampled. Experiments on two benchmark environments, including identifying the anomalous robotic systems and detecting user data poisoning in recommendation models, demonstrate the superiority of InterSAD compared with state-of-the-art baselines methods.