{"title":"DeepQCD: An end-to-end deep learning approach to quickest change detection","authors":"","doi":"10.1016/j.jfranklin.2024.107199","DOIUrl":null,"url":null,"abstract":"<div><div>This paper aims to generalize the quickest change detection (QCD) framework via a data-driven approach. To this end, a generic neural network architecture is proposed for the QCD task, composed of feature transformation, recurrent, and dense layers. The neural network is trained end-to-end to learn the change detection rule directly from data without needing the knowledge of probabilistic data models. Specifically, the feature transformation layers can perform a broad range of operations including feature extraction, scaling, and normalization. The recurrent layers keep an internal state summarizing the time-series data seen so far and update the state as new information comes in. Finally, the dense layers map the internal state into a decision statistic, defined as the posterior probability that a change has taken place. Comparisons with the existing model-based QCD algorithms demonstrate the power of the proposed data-driven approach, called DeepQCD, under several scenarios including transient changes and temporally correlated data streams. Experiments with real-world data illustrate superior performance of DeepQCD compared to state-of-the-art algorithms in real-time anomaly detection over surveillance videos and real-time attack detection over Internet of Things (IoT) networks.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of The Franklin Institute-engineering and Applied Mathematics","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0016003224006203","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper aims to generalize the quickest change detection (QCD) framework via a data-driven approach. To this end, a generic neural network architecture is proposed for the QCD task, composed of feature transformation, recurrent, and dense layers. The neural network is trained end-to-end to learn the change detection rule directly from data without needing the knowledge of probabilistic data models. Specifically, the feature transformation layers can perform a broad range of operations including feature extraction, scaling, and normalization. The recurrent layers keep an internal state summarizing the time-series data seen so far and update the state as new information comes in. Finally, the dense layers map the internal state into a decision statistic, defined as the posterior probability that a change has taken place. Comparisons with the existing model-based QCD algorithms demonstrate the power of the proposed data-driven approach, called DeepQCD, under several scenarios including transient changes and temporally correlated data streams. Experiments with real-world data illustrate superior performance of DeepQCD compared to state-of-the-art algorithms in real-time anomaly detection over surveillance videos and real-time attack detection over Internet of Things (IoT) networks.
期刊介绍:
The Journal of The Franklin Institute has an established reputation for publishing high-quality papers in the field of engineering and applied mathematics. Its current focus is on control systems, complex networks and dynamic systems, signal processing and communications and their applications. All submitted papers are peer-reviewed. The Journal will publish original research papers and research review papers of substance. Papers and special focus issues are judged upon possible lasting value, which has been and continues to be the strength of the Journal of The Franklin Institute.