research-article

Suspicious Behavior Detection: Current Trends and Future Directions

Authors:

Christos FaloutsosAuthors Info & Claims

IEEE Intelligent Systems, Volume 31, Issue 1

Pages 31 - 39

https://doi.org/10.1109/MIS.2016.5

Published: 01 January 2016 Publication History

Abstract

Different real-world applications have varying definitions of suspicious behaviors. Detection methods often look for the most suspicious parts of the data by optimizing scores, but quantifying the suspiciousness of a behavioral pattern is still an open issue.

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Pratelli MPetrocchi MSaracco FDe Nicola R(2023)Swinging in the States: Does disinformation on Twitter mirror the US presidential election system?Companion Proceedings of the ACM Web Conference 202310.1145/3543873.3587638(1395-1403)Online publication date: 30-Apr-2023
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Shi HJi LLiu SWang KHu X(2022)Collusive anomalies detection based on collaborative markov random fieldIntelligent Data Analysis10.3233/IDA-21628726:6(1469-1485)Online publication date: 1-Jan-2022
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cover image IEEE Intelligent Systems

IEEE Intelligent Systems Volume 31, Issue 1

Jan.-Feb. 2016

98 pages

ISSN:1541-1672

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IEEE Educational Activities Department

United States

Publication History

Published: 01 January 2016

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Cited By

Imad MCleland IMcAllister PNugent C(2024)Improving the accuracy of Anomaly Detection in Multimodal Sensors using 1D-CNNProceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3652037.3652052(212-221)Online publication date: 26-Jun-2024
https://dl.acm.org/doi/10.1145/3652037.3652052
Pratelli MPetrocchi MSaracco FDe Nicola R(2023)Swinging in the States: Does disinformation on Twitter mirror the US presidential election system?Companion Proceedings of the ACM Web Conference 202310.1145/3543873.3587638(1395-1403)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587638
Shi HJi LLiu SWang KHu X(2022)Collusive anomalies detection based on collaborative markov random fieldIntelligent Data Analysis10.3233/IDA-21628726:6(1469-1485)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/IDA-216287
Zhu Y(2022)A Deep Learning-Based Model for Analyzing Social Public IssuesSecurity and Communication Networks10.1155/2022/73996002022Online publication date: 1-Jan-2022
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Kawintiranon KSingh LBudak C(2022)Traditional and context-specific spam detection in low resource settingsMachine Language10.1007/s10994-022-06176-x111:7(2515-2536)Online publication date: 1-Jul-2022
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Chou DJiang M(2021)A Survey on Data-driven Network Intrusion DetectionACM Computing Surveys10.1145/347275354:9(1-36)Online publication date: 8-Oct-2021
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Liu RLiu RPugliese ASubrahmanian V(2020)STARSACM Transactions on Intelligent Systems and Technology10.1145/339746311:5(1-25)Online publication date: 24-Jul-2020
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Rajabi ZShehu APurohit H(2019)User Behavior Modelling for Fake Information Mitigation on Social WebSocial, Cultural, and Behavioral Modeling10.1007/978-3-030-21741-9_24(234-244)Online publication date: 9-Jul-2019
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