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Locally reconfigurable Self Organizing Feature Map for high impact malicious tasks submission in Mobile Crowdsensing
Xuankai Chen1, Murat Simsek1, Burak Kantarci1
1School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward Ave,Ottawa, ON K1N 6N5, Canada.
Internet of Things (Amsterdam, Netherlands)
|April 15, 2024
Summary
This study introduces novel algorithms using Self-Organizing Feature Maps (SOFM) to detect fake task submissions in Mobile Crowdsensing (MCS) systems. These refined SOFM models significantly improve the identification of attack locations, enhancing system security.
Area of Science:
- Computer Science
- Cybersecurity
- Artificial Intelligence
Background:
- Mobile Crowdsensing (MCS) systems are vulnerable to location-based clogging attacks.
- These attacks submit fake tasks to deplete device resources like batteries and processors.
Purpose of the Study:
- To develop intelligent strategies for modeling fake task submissions.
- To identify attack locations in MCS systems for effective defense.
Main Methods:
- Exploiting Self-Organizing Feature Maps (SOFM) to model fake task submissions.
- Introducing refined SOFM algorithms to improve attack location identification.
Main Results:
- The proposed SOFM-based model effectively identifies attack locations.
- Simulation results show up to 139.9% impact improvement compared to former models.
- Refined SOFM architectures enhance the modeling of clogging attacks.
Conclusions:
- The refined SOFM approach offers a more effective defense against location-based clogging attacks in MCS.
- Intelligent modeling of fake tasks is crucial for securing crowdsensing platforms.
Keywords:
Artificial neural networksClogging attacksFake task submissionMobile CrowdsensingSelf-Organizing Feature MapMore Related Videos
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