short-paper

An Anomaly Event Detection Method Based on GNN Algorithm for Multi-data Sources

Authors:

Xiong LiAuthors Info & Claims

BSCI '21: Proceedings of the 3rd ACM International Symposium on Blockchain and Secure Critical Infrastructure

Pages 91 - 96

https://doi.org/10.1145/3457337.3457846

Published: 29 May 2021 Publication History

Abstract

Anomaly event detection is crucial for critical infrastructure security(transportation system, social-ecological sector, insurance service, government sector etc.) due to its ability to reveal and address the potential cyber-threats in advance by analysing the data(messages, microblogs, logs etc.) from digital systems and networks.

However, the convenience and applicability of smart devices and the maturity of connected technology make the social anomaly events data multi-source and dynamic, which result in the inadaptability for multi-source data detection and thus affect the critical infrastructure security.

To effectively address the proposed problems, in this paper, we design a novel anomaly detection method based on multi-source data.

First, we leverage spectral clustering algorithm for feature extraction and fusion of multiple data sources.

Second, by harnessing the power of deep graph neural network(Deep-GNN), we perform a fine-gained anomaly social event detection, revealing the threatening events and guarantee the critical infrastructure security.

Experimental results demonstrate that our framework outperforms other baseline anomaly event detection methods and shows high tracking accuracy, strong robustness and stability.

References

[1]

Aggarwal and C. Charu. 2013. Outlier Detection in Categorical, Text and Mixed Attribute Data. Springer New York. 199--223 pages.

[2]

H Asai, Péter Zoltán Baranyi, and Sz Kovács. 2001. Modelling of guiding styles based on generalized neural network (GNN). Soft Science Workshop at Kanazawa (2001).

[3]

Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, Jianxin Li, and Philip S Yu. 2021. Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs. arXiv preprint arXiv:2101.08747 (2021).

[4]

Aditya Challa, Sravan Danda, B. S. Daya Sagar, and Laurent Najman. 2020. Power Spectral Clustering. Journal of Mathematical Imaging Vision 62, 9 (2020), 1195--1213.

[5]

Songgaojun Deng, Huzefa Rangwala, and Yue Ning. 2020. Cola-GNN: Crosslocation Attention based Graph Neural Networks for Long-term ILI Prediction. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management.

Digital Library

[6]

Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, and Philip S Yu. 2020. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 315--324.

Digital Library

[7]

Andrew F. Emmott, Shubhomoy Das, Thomas Dietterich, Alan Fern, and Weng Keen Wong. 2013. Systematic Construction of Anomaly Detection Benchmarks from Real Data. In Acm Sigkdd Workshop on Outlier Detection Description.

Digital Library

[8]

Chunming Hu, Jianyong Zhu, Renyu Yang, Hao Peng, Tianyu Wo, Shiqing Xue, Xiaoqiang Yu, Jie Xu, and Rajiv Ranjan. 2020. TOPOSCH: Latency-Aware Scheduling Based on Critical Path Analysis on Shared YARN Clusters. In 2020 IEEE 13th International Conference on Cloud Computing (CLOUD). IEEE, 619--627.

[9]

Pankaj Kumar Jadwal, Sonal Jain, and Basant Agarwal. 2020. Spectral Clustering and Cost-Sensitive Deep Neural Network-Based Undersampling Approach for P2P Lending Data. International Journal of Information Technology and Web Engineering (IJITWE) 15 (2020).

[10]

Anna Koufakou, E. G. Ortiz, Michael Georgiopoulos, G. C. Anagnostopoulos, and K. M. Reynolds. 2007. A Scalable and Efficient Outlier Detection Strategy for Categorical Data. In IEEE International Conference on Tools with Artificial Intelligence.

Digital Library

[11]

Chen Li, Xutan Peng, Shanghang Zhang, Hao Peng, S Yu Philip, Min He, Linfeng Du, and Lihong Wang. 2020. Modeling relation paths for knowledge base completion via joint adversarial training. Knowledge-Based Systems (2020).

[12]

Yaopeng Liu, Hao Peng, Jianxin Li, Yangqiu Song, and Xiong Li. 2020. Event detection and evolution in multi-lingual social streams. Frontiers of Computer Science 14, 5 (2020), 1--15.

Digital Library

[13]

Zhiwei Liu, Yingtong Dou, Philip S Yu, Yutong Deng, and Hao Peng. 2020. Alleviating the inconsistency problem of applying graph neural network to fraud detection. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1569--1572.

Digital Library

[14]

Zheng Liu, Xiaohan Li, Hao Peng, Lifang He, and Philip S Yu. 2021. Heterogeneous Similarity Graph Neural Network on Electronic Health Records. arXiv preprint arXiv:2101.06800 (2021).

[15]

Qianren Mao, Xi Li, Hao Peng, Jianxin Li, Dongxiao He, Shu Guo, Min He, and Lihong Wang. 2021. Event prediction based on evolutionary event ontology knowledge. Future Generation Computer Systems 115 (2021), 76--89.

Digital Library

[16]

Alejandro Marcos Alvarez, Makoto Yamada, Akisato Kimura, and Tomoharu Iwata. 2013. Clustering-Based Anomaly Detection in Multi-View Data. In Proceedings of the 22nd ACM international conference on Conference on information knowledge management.

Digital Library

[17]

Hao Peng, Jianxin Li, Qiran Gong, Yuanxin Ning, SenzhangWang, and Lifang He. 2020. Motif-Matching Based Subgraph-Level Attentional Convolutional Network for Graph Classification. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 5387--5394.

[18]

Hao Peng, Jianxin Li, Qiran Gong, Yangqiu Song, Yuanxing Ning, Kunfeng Lai, and PS Yu. 2019. Fine-grained Event Categorization With Heterogeneous Graph Convolutional Networks. In IJCAI International Joint Conference on Artificial Intelligence. 32--38.

[19]

Hao Peng, Jianxin Li, Yangqiu Song, and Yaopeng Liu. 2017. Incrementally learning the hierarchical softmax function for neural language models. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31.

Digital Library

[20]

Hao Peng, Jianxin Li, Senzhang Wang, Lihong Wang, Qiran Gong, Renyu Yang, Bo Li, Philip Yu, and Lifang He. 2019. Hierarchical taxonomy-aware and attentional graph capsule RCNNs for large-scale multi-label text classification. IEEE Transactions on Knowledge and Data Engineering (2019).

[21]

Hao Peng, Renyu Yang, ZhengWang, Jianxin Li, Lifang He, PS Yu, Albert Zomaya, and Raj Ranjan. 2021. LIME: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information Networks. IEEE Trans. Comput. (2021).

[22]

By Karl Rohe, Sourav Chatterjee, and Y. U. Bin. 2012. SPECTRAL CLUSTERING AND THE HIGH-DIMENSIONAL STOCHASTIC BLOCKMODEL1. (2012).

[23]

David Savage, Xiuzhen Zhang, Xinghuo Yu, Pauline Chou, and Qingmai Wang. 2014. Anomaly detection in online social networks. Social Networks 39 (2014), 62--70.

[24]

DAVID J. SEGAL, BIRGIT DRElER, and ROGER R. BEERLI. 1999. Toward controlling gene expression at will: Selection and design of zinc finger domains recognizing each of the 5-GNN-3 DNA target sequences. Proceedings of the National Academy of Sciences of the United States of America 96, 6 (1999), 2758--2763.

[25]

Weijing Shi, Ragunathan, and Rajkumar. 2020. Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[26]

Qingyun Sun, Hao Peng, Jianxin Li, Senzhang Wang, Xiangyu Dong, Liangxuan Zhao, Philip S Yu, and Lifang He. 2020. Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks. arXiv preprint arXiv:2008.13099 (2020).

[27]

N. N. R. Ranga Suri, M. Narasimha Murty, and G. Athithan. 2016. Detecting outliers in categorical data through rough clustering. Natural Computing 15, 3 (2016), 385--394.

Digital Library

[28]

Scott White and Padhraic Smyth. 2005. A Spectral Clustering Approach To Finding Communities in Graph. In Proceedings of the 2005 SIAM International Conference on Data Mining.

[29]

Shu Wu and Shengrui Wang. 2013. Information-Theoretic Outlier Detection for Large-Scale Categorical Data. (2013).

[30]

Stella X. Yu and Jianbo Shi. 2003. Multiclass Spectral Clustering. In IEEE International Conference on Computer Vision.

Digital Library

[31]

Jun Zhao, Xudong Liu, Qiben Yan, Bo Li, Minglai Shao, and Hao Peng. 2020. Multi-attributed heterogeneous graph convolutional network for bot detection. Information Sciences 537 (2020), 380--393.

[32]

Jun Zhao, Xudong Liu, Qiben Yan, Bo Li, Minglai Shao, Hao Peng, and Lichao Sun. 2021. Automatically predicting cyber attack preference with attributed heterogeneous attention networks and transductive learning. Computers & Security 102 (2021), 102152.

Cited By

Chen MYang SDai WWang ZXu J(2024)A Method for Anomaly Detection in Power Energy Topology Graph Data Based on Domain Knowledge Graph and Graph Neural Networks2024 IEEE 10th Conference on Big Data Security on Cloud (BigDataSecurity)10.1109/BigDataSecurity62737.2024.00026(107-112)Online publication date: 10-May-2024
https://doi.org/10.1109/BigDataSecurity62737.2024.00026
Yang Q(2023)A Security Evaluation Method for Complex Computer Networks Based on GABP Algorithm2023 World Conference on Communication & Computing (WCONF)10.1109/WCONF58270.2023.10234995(1-6)Online publication date: 14-Jul-2023
https://doi.org/10.1109/WCONF58270.2023.10234995
Zhu L(2022)Urban Landscaping Landscape Design and Maintenance Management Method Based on Multisource Big Data FusionComputational Intelligence and Neuroscience10.1155/2022/13536682022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1353668
Show More Cited By

Index Terms

An Anomaly Event Detection Method Based on GNN Algorithm for Multi-data Sources
1. Computing methodologies
  1. Machine learning
2. Security and privacy
  1. Software and application security
    1. Domain-specific security and privacy architectures

Recommendations

Anomaly Event Detection for Sensor Networks on Apriori Algorithms and Subjective Logic
ICSCA '19: Proceedings of the 2019 8th International Conference on Software and Computer Applications

Aiming at the challenges that anomaly events are difficult to be characterized and heterogeneous nodes can not cooperate directly in sensor network, a novel anomaly detection method for sensor networks is proposed in this paper. We use Apriori algorithm ...
Event log anomaly detection method based on auto-encoder and control flow
Abstract
Anomaly detection is widely used in the field of business process management, and researchers have proposed various anomaly detection algorithms to detect anomalies in event logs. However, existing research focuses on detecting anomalies in event ...
DeGAN - Decomposition-based unified anomaly detection in static networks
Abstract
Graph anomaly detection aims to identify anomalous occurrences in networks. However, this is more challenging than the traditional anomaly detection problem because anomalies in graphs can manifest in three different forms: anomalous nodes, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

BSCI '21: Proceedings of the 3rd ACM International Symposium on Blockchain and Secure Critical Infrastructure

May 2021

117 pages

ISBN:9781450384001

DOI:10.1145/3457337

Program Chairs:
Keke Gai
Beijing Institute of Technology, China
,
Kim-Kwang Raymond Choo
University of Texas at San Antonio, USA

Copyright © 2021 ACM.

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]

Sponsors

SIGSAC: ACM Special Interest Group on Security, Audit, and Control

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 May 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

National Key Research and Development Program
Key Research and Development Project of Hebei Province

Conference

ASIA CCS '21

Sponsor:

SIGSAC

ASIA CCS '21: ACM Asia Conference on Computer and Communications Security

June 7, 2021

Virtual Event, Hong Kong

Acceptance Rates

Overall Acceptance Rate 44 of 12 submissions, 367%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
242
Total Downloads

Downloads (Last 12 months)40
Downloads (Last 6 weeks)1

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chen MYang SDai WWang ZXu J(2024)A Method for Anomaly Detection in Power Energy Topology Graph Data Based on Domain Knowledge Graph and Graph Neural Networks2024 IEEE 10th Conference on Big Data Security on Cloud (BigDataSecurity)10.1109/BigDataSecurity62737.2024.00026(107-112)Online publication date: 10-May-2024
https://doi.org/10.1109/BigDataSecurity62737.2024.00026
Yang Q(2023)A Security Evaluation Method for Complex Computer Networks Based on GABP Algorithm2023 World Conference on Communication & Computing (WCONF)10.1109/WCONF58270.2023.10234995(1-6)Online publication date: 14-Jul-2023
https://doi.org/10.1109/WCONF58270.2023.10234995
Zhu L(2022)Urban Landscaping Landscape Design and Maintenance Management Method Based on Multisource Big Data FusionComputational Intelligence and Neuroscience10.1155/2022/13536682022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1353668
Albuquerque Filho JBrandao LFernandes BMaciel A(2022)A Review of Neural Networks for Anomaly DetectionIEEE Access10.1109/ACCESS.2022.321600710(112342-112367)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3216007

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 Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents