More Web Proxy on the site http://driver.im/

research-article

Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach

Authors:

Tony Q. S. QuekAuthors Info & Claims

ACM Transactions on Internet of Things, Volume 3, Issue 1

Article No.: 4, Pages 1 - 23

https://doi.org/10.1145/3480172

Published: 27 October 2021 Publication History

Abstract

The advances in deep neural networks (DNN) have significantly enhanced real-time detection of anomalous data in IoT applications. However, the complexity-accuracy-delay dilemma persists: Complex DNN models offer higher accuracy, but typical IoT devices can barely afford the computation load, and the remedy of offloading the load to the cloud incurs long delay. In this article, we address this challenge by proposing an adaptive anomaly detection scheme with hierarchical edge computing (HEC). Specifically, we first construct multiple anomaly detection DNN models with increasing complexity and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network. We also incorporate a parallelism policy training method to accelerate the training process by taking advantage of distributed models. We build an HEC testbed using real IoT devices and implement and evaluate our contextual-bandit approach with both univariate and multivariate IoT datasets. In comparison with both baseline and state-of-the-art schemes, our adaptive approach strikes the best accuracy-delay tradeoff on the univariate dataset and achieves the best accuracy and F1-score on the multivariate dataset with only negligibly longer delay than the best (but inflexible) scheme.

References

[1]

Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. 2015. Scheduled sampling for sequence prediction with recurrent neural networks. In Proc. NIPS. MIT Press, Cambridge, MA, 1171–1179.

Digital Library

[2]

Raghavendra Chalapathy and Sanjay Chawla. 2019. Deep learning for anomaly detection: A survey. arxiv:1901.03407 [cs.LG]. Retrieved from https://arxiv.org/abs/1901.03407.

[3]

Zhuo Chen et al. 2017. An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance. In Proc. ACM/IEEE SEC. Article 14, 14 pages.

Digital Library

[4]

Lasse Espeholt, Raphael Marinier, Piotr Stanczyk, Ke Wang, and Marcin Michalski. 2020. SEED RL: Scalable and efficient deep-RL with accelerated central inference. arxiv:1910.06591 [cs.LG]. Retrieved from https://arxiv.org/abs/1910.06591.

[5]

Manish Gupta, Jing Gao, Charu C. Aggarwal, and Jiawei Han. 2013. Outlier detection for temporal data: A survey. IEEE Trans. Knowl. Data Eng. 26, 9 (2013), 2250–2267.

Digital Library

[6]

Song Han, Huizi Mao, and William J. Dally. 2016. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. In Proc. ICLR.

[7]

Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. In Proce NIPS 2014 Deep Learning Workshop.

[8]

Yiping Kang, Johann Hauswald, Cao Gao, Austin Rovinski, Trevor Mudge, Jason Mars, and Lingjia Tang. 2017. Neurosurgeon: Collaborative intelligence between the cloud and mobile edge. SIGARCH Comput. Archit. News 45, 1 (Apr. 2017), 615–629.

Digital Library

[9]

E. Keogh, J. Lin, and A. Fu. 2005. HOT SAX: Efficiently finding the most unusual time series subsequence. In Proc. IEEE ICDM.

Digital Library

[10]

Quang Duy La, Mao V. Ngo, Thinh Quang Dinh, Tony Q. S. Quek, and Hyundong Shin. 2019. Enabling intelligence in fog computing to achieve energy and latency reduction. Dig. Commun. Netw. 5, 1 (2019), 3–9.

[11]

H. Li, K. Ota, and M. Dong. 2018. Learning IoT in edge: Deep learning for the internet of things with edge computing. IEEE Netw. 32, 1 (2018), 96–101.

[12]

Ji Lin, Yongming Rao, Jiwen Lu, and Jie Zhou. 2017. Runtime neural pruning. In Advances in Neural Information Processing Systems, Vol. 30.

Digital Library

[13]

T. Luo and S. G. Nagarajan. 2018. Distributed anomaly detection using autoencoder neural networks in WSN for IoT. In Proc. IEEE ICC.

[14]

Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. 2016. LSTM-based encoder-decoder for multi-sensor anomaly detection. In Proc. ICML Workshop.

[15]

Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, and Puneet Agarwal. 2015. Long short term memory networks for anomaly detection in time series. In Proc. ESANN. 89.

[16]

M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani. 2018. Deep learning for IoT big data and streaming analytics: A survey. IEEE Commun. Surv. Tutor. 20, 4 (4Q. 2018), 2923–2960.

Digital Library

[17]

Mao V. Ngo, Hakima Chaouchi, Tie Luo, and Tony Q. S. Quek. 2020. Adaptive anomaly detection for IoT data in hierarchical edge computing. In AAAI’20 workshops. Retrieved from https://arxiv.org/abs/2001.03314.

[18]

Mao V. Ngo, Tie Luo, Hakima Chaouchi, and Tony Q. S. Quek. 2020. Contextual-bandit anomaly detection for IoT data in distributed hierarchical edge computing. In Proc. IEEE ICDCS Demo Track.

[19]

M. V. Ngo, T. Luo, H. T. Hoang, and T. Q. S. Quek. 2020. Coordinated container migration and base station handover in mobile edge computing. In GLOBECOM. 1–6.

[20]

Akash Singh. 2017. Anomaly Detection for Temporal Data Using Long Short-term Memory (LSTM). Master’s thesis.

[21]

Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, and Dan Pei. 2019. Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In Proc. ACM SIGKDD. 2828–2837.

Digital Library

[22]

Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Proc. NIPS.

Digital Library

[23]

Richard S. Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour. 2000. Policy gradient methods for reinforcement learning with function approximation. In Proc. NIPS. 1057–1063.

Digital Library

[24]

Ben Taylor, Vicent Sanz Marco, Willy Wolff, Yehia Elkhatib, and Zheng Wang. 2018. Adaptive deep learning model selection on embedded systems. In ACM SIGPLAN Notices, Vol. 53. ACM, 31–43.

Digital Library

[25]

S. Teerapittayanon, B. McDanel, and H. T. Kung. 2016. BranchyNet: Fast inference via early exiting from deep neural networks. In Proc. ICPR. 2464–2469.

[26]

Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung. 2017. Distributed deep neural networks over the cloud, the edge and end devices. In Proc. IEEE ICDCS. 328–339.

[27]

Ronald J. Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8, 3–4 (1992), 229–256.

Digital Library

[28]

Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, and Rogerio Feris. 2018. Blockdrop: Dynamic inference paths in residual networks. In Proc. CVPR. 8817–8826.

[29]

J. Zhou, Y. Wang, K. Ota, and M. Dong. 2019. AAIoT: Accelerating artificial intelligence in IoT systems. IEEE Wireless Commun. Lett. 8, 3 (2019), 825–828.

Cited By

Kayan HHeartfield RRana OBurnap PPerera C(2024)CASPER: Context-Aware IoT Anomaly Detection System for Industrial Robotic ArmsACM Transactions on Internet of Things10.1145/36704145:3(1-36)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1145/3670414
Tan SHe DChan SGuizani M(2024)FlowSpotter: Intelligent IoT Threat Detection via Imaging Network FlowsIEEE Network10.1109/MNET.2023.332137238:4(268-274)Online publication date: Jul-2024
https://doi.org/10.1109/MNET.2023.3321372
Pan CZhang CNgai ELiu JLi B(2024)HALO: HVAC Load Forecasting With Industrial IoT and Local–Global-Scale TransformerIEEE Internet of Things Journal10.1109/JIOT.2024.340123611:17(28307-28319)Online publication date: 1-Sep-2024
https://doi.org/10.1109/JIOT.2024.3401236
Show More Cited By

Index Terms

Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. n-tier architectures
  2. Embedded and cyber-physical systems
    1. Sensor networks
2. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents

Recommendations

Analysis of Deep Learning Models for Anomaly Detection in Time Series IoT Sensor Data
IC3-2022: Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing

The anomaly detection in Internet of Things (IoT) sensor data has become an important research area because of the possibility of noise and unavailability of labels in the sensors readings. The conventional machine learning algorithms cannot detect the ...
An edge computing based anomaly detection method in IoT industrial sustainability
Abstract
In recent years, the evolving Internet of Things (IoT) technology has been widely used in various industrial scenarios, whereby massive sensor data involving both normal data and anomalous data are generated. However, the anomalies ...
Highlights
- The anomalies involved in sensor data from time series might have a great impact on the industrial sustainability.
A Statistical-Based Anomaly Detection Method for Connected Cars in Internet of Things Environment
IOV 2015: Proceedings of the Second International Conference on Internet of Vehicles - Safe and Intelligent Mobility - Volume 9502

A connected car is the most successful thing in the era of Internet of Things IoT. The connections between vehicles and networks grow and provide more convenience to users. However, vehicles become exposed to malicious attacks from outside. Therefore, a ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Internet of Things

ACM Transactions on Internet of Things Volume 3, Issue 1

February 2022

201 pages

EISSN:2577-6207

DOI:10.1145/3492447

Editors:
Schahram Dustdar
TU Wien, Austria
,
Gian Pietro Picco
University of Trento, Italy

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 27 October 2021

Accepted: 01 August 2021

Revised: 01 April 2021

Received: 01 June 2020

Published in TIOT Volume 3, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Funding Sources

National Research Foundation, Singapore
Infocomm Media Development Authority
National Science Foundation (NSF)

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
691
Total Downloads

Downloads (Last 12 months)113
Downloads (Last 6 weeks)10

Reflects downloads up to 15 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Kayan HHeartfield RRana OBurnap PPerera C(2024)CASPER: Context-Aware IoT Anomaly Detection System for Industrial Robotic ArmsACM Transactions on Internet of Things10.1145/36704145:3(1-36)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1145/3670414
Tan SHe DChan SGuizani M(2024)FlowSpotter: Intelligent IoT Threat Detection via Imaging Network FlowsIEEE Network10.1109/MNET.2023.332137238:4(268-274)Online publication date: Jul-2024
https://doi.org/10.1109/MNET.2023.3321372
Pan CZhang CNgai ELiu JLi B(2024)HALO: HVAC Load Forecasting With Industrial IoT and Local–Global-Scale TransformerIEEE Internet of Things Journal10.1109/JIOT.2024.340123611:17(28307-28319)Online publication date: 1-Sep-2024
https://doi.org/10.1109/JIOT.2024.3401236
DeMedeiros KHendawi AAlvarez M(2023)A Survey of AI-Based Anomaly Detection in IoT and Sensor NetworksSensors10.3390/s2303135223:3(1352)Online publication date: 25-Jan-2023
https://doi.org/10.3390/s23031352
Pandey S(2023)Advanced Abnormality Recognition in IoT Networks Using LSTM-RNNs for Dynamic Security Enhancement2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI)10.1109/ICCSAI59793.2023.10420960(1010-1014)Online publication date: 23-Nov-2023
https://doi.org/10.1109/ICCSAI59793.2023.10420960
Varshney NMadan PShrivastava ASrivastava AKUMAR CAkhilesh Khan K(2023)Real-Time Anomaly Detection in IoT Healthcare Devices With LSTM2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI)10.1109/ICAIIHI57871.2023.10489823(1-6)Online publication date: 29-Dec-2023
https://doi.org/10.1109/ICAIIHI57871.2023.10489823
Trang NTruong H(2023)Context-aware, Composable Anomaly Detection in Large-scale Mobile Networks2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC57700.2023.00032(183-192)Online publication date: Jun-2023
https://doi.org/10.1109/COMPSAC57700.2023.00032
Nguyen LKiet PLee SYeo HSon Y(2023)Comprehensive Survey of Sensor Data Verification in Internet of ThingsIEEE Access10.1109/ACCESS.2023.327754511(50560-50577)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3277545
Novoa-Paradela DFontenla-Romero OGuijarro-Berdiñas B(2023)Fast deep autoencoder for federated learningPattern Recognition10.1016/j.patcog.2023.109805143:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.patcog.2023.109805
Blair SCotter J(2021)An Analysis of Data Processing for Big Data AnalyticsJournal of Computing and Natural Science10.53759/181X/JCNS202101019(130-138)Online publication date: 5-Oct-2021
https://doi.org/10.53759/181X/JCNS202101019

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents