Collaborative Self Organizing Map with DeepNNs for Fake Task Prevention in Mobile Crowdsensing
Author:
Affiliation:
1. University of Ottawa,School of Electrical Engineering and Computer Science,Ottawa,ON,Canada,K1N 6N5
Funder
Natural Sciences and Engineering Research Council of Canada
Canada Research Chairs
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9837954/9838246/09838920.pdf?arnumber=9838920
Reference23 articles.
1. Utility-Aware Legitimacy Detection of Mobile Crowdsensing Tasks via Knowledge-Based Self Organizing Feature Map
2. Detecting Fake Mobile Crowdsensing Tasks: Ensemble Methods Under Limited Data
3. Ensemble Learning Against Adversarial AI-driven Fake Task Submission in Mobile Crowdsensing
4. Locally reconfigurable Self Organizing Feature Map for high impact malicious tasks submission in Mobile Crowdsensing
5. Self Organizing Feature Map-Integrated Knowledge-Based Deep Network Against Fake Crowdsensing Tasks
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1. A crowd cooperative defense model for mitigating DDoS attacks in Mobile Crowdsensing networks;Computer Communications;2023-07
2. AI-Driven Attack Modeling and Defense Strategies in Mobile Crowdsensing: A Special Case Study on Fake Tasks;Wireless Networks;2023
3. Prior Knowledge based Advanced Persistent Threats Detection for IoT in a Realistic Benchmark;GLOBECOM 2022 - 2022 IEEE Global Communications Conference;2022-12-04
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