Locally reconfigurable Self Organizing Feature Map for high impact malicious tasks submission in Mobile Crowdsensing

Chen, Xuankai; Şimşek, Murat; Kantarcı, Burak

doi:10.1016/j.iot.2020.100297

Cited by 11 publications

(3 citation statements)

References 32 publications

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“…Crowdsensing is location-based, and many attacks can be produced when sensing the data. The authors [22] proposed a smart strategy for attacks' location identification to deal with this attack. It relies on a local configuration of the Self-Organizing Feature Map (SOFM) algorithm to locate where the fake tasks are centered.…”

Section: B Spatio-temporal Mcs Distributionmentioning

confidence: 99%

Trust-3DM: Trustworthiness-Based Data-Driven Decision-Making Framework Using Smart Edge Computing for Continuous Sensing

et al. 2022

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Mobile Edge Computing (MEC) has been proposed as an efficient solution for Mobile crowdsensing (MCS). It allows the parallel collection and processing of data in real time in response to a requested task. A sensing task can be one-time or continuous, with multiple readings collected over time. Integrating MEC and continuous sensing in MCS is challenging due to many factors, including workers' mobility, edge node placement, task location, Reputation, and data quality. In addition, guarantying cooperative communication in the presence of Anomalous data while maintaining a high quality of service (QoS) is a fundamental issue in continuous sensing. A stability-based edge node selection and anomaly detectionbased decision-making framework for worker recruitment in continuous sensing is proposed to address these challenges. It can a) Select the most stable edge nodes in the area of interest (AoI), b) Dynamically cluster the workers according to their movement in the AoI, c) Locally detect and eliminate anomalies within the sensing data, and d) Adopt a feedback mechanism that ensures the cooperation between the edge nodes to eliminate untrustworthy workers in the whole sensing period and future tasks. A real-life dataset is used to evaluate the efficiency of the proposed framework. Results show that the framework outperforms the baselines by achieving higher QoS while introducing lower delay, energy consumption, and less resource consumption.

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Section: B Spatio-temporal Mcs Distributionmentioning

confidence: 99%

Trust-3DM: Trustworthiness-Based Data-Driven Decision-Making Framework Using Smart Edge Computing for Continuous Sensing

et al. 2022

View full text Add to dashboard Cite

show abstract

“…For instance, a Q-learning and convolutional neural networkbased method was proposed for malware identification [17]. In order to detect illegitimate tasks, self-organizing feature mapbased models are implemented in [5], [22].…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…Results in Table VII describe average loss in static is critically lower than dynamic. The root cause is that the reputation is always less than "1" based on (5) if N is set to 200. It results in aggregation value S i for task i in (6) should be less than 5 even if all devices predict this task as legitimate (5 devices model designed here).…”

Section: B Federated Learning-based Systemmentioning

confidence: 99%

Federated Learning-Based Risk-Aware Decision toMitigate Fake Task Impacts on CrowdsensingPlatforms

Chen¹,

Şimşek²,

Kantarcı³

2021

Preprint

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Mobile crowdsensing (MCS) leverages distributed and non-dedicated sensing concepts by utilizing sensors imbedded in a large number of mobile smart devices. However, the openness and distributed nature of MCS leads to various vulnerabilities and consequent challenges to address. A malicious user submitting fake sensing tasks to an MCS platform may be attempting to consume resources from any number of participants' devices; as well as attempting to clog the MCS server. In this paper, a novel approach that is based on horizontal federated learning is proposed to identify fake tasks that contain a number of independent detection devices and an aggregation entity. Detection devices are deployed to operate in parallel with each device equipped with a machine learning (ML) module, and an associated training dataset. Furthermore, the aggregation module collects the prediction results from individual devices and determines the final decision with the objective of minimizing the prediction loss. Loss measurement considers the lost task values with respect to misclassification, where the final decision utilizes a risk-aware approach where the risk is formulated as a function of the utility loss. Experimental results demonstrate that using federated learning-driven illegitimate task detection with a risk aware aggregation function improves the detection performance of the traditional centralized framework. Furthermore, the higher performance of detection and lower loss of utility can be achieved by the proposed framework. This scheme can even achieve 100% detection accuracy using small training datasets distributed across devices, while achieving slightly over an 8% increase in detection improvement over traditional approaches.

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AI-Driven Attack Modeling and Defense Strategies in Mobile Crowdsensing: A Special Case Study on Fake Tasks

2023

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Locally reconfigurable Self Organizing Feature Map for high impact malicious tasks submission in Mobile Crowdsensing

Cited by 11 publications

References 32 publications

Trust-3DM: Trustworthiness-Based Data-Driven Decision-Making Framework Using Smart Edge Computing for Continuous Sensing

Trust-3DM: Trustworthiness-Based Data-Driven Decision-Making Framework Using Smart Edge Computing for Continuous Sensing

Federated Learning-Based Risk-Aware Decision toMitigate Fake Task Impacts on CrowdsensingPlatforms

AI-Driven Attack Modeling and Defense Strategies in Mobile Crowdsensing: A Special Case Study on Fake Tasks

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