A crowd cooperative defense model for mitigating DDoS attacks in Mobile Crowdsensing networks

Mobile crowdsensing (MCS) is a combination of crowdsourcing ideas and mobile sensing devices, designed to enable rational allocation of resources at scale. However, the MCS platform is highly vulnerable to injection attacks from malicious participants and fake tasks that interfere with platform service capabilities and sensing activities. To this end, the participant and task submission process is modeled as a multivariate time series, and a detection model for malicious participants and fake tasks (MP-FTD) with a Gaussian prior on the attentional mechanism and a two-stage adversarial training process is proposed. The attention mechanism was corrected using Gaussian bias, and then the corrected attention mechanism was used to obtain the correlation discrepancies between the data. Using the adversarial training method of Generative Adversarial Networks (GAN), the output of the correlation discrepancy reconstruction phase is transformed into a focus score, to amplify the reconstruction error in the output of the focus score reconstruction phase, and to improve the differentiation between the injected data and normal data of malicious attackers. The detection of these malicious attackers will effectively improve the robustness of the sensing platform. Experiments on six real-world datasets showed that the average F1-score reached 93.44%, outperforming the current baseline method, and resulting in an average 12.07% improvement in participant assignment accuracy and an average 12.25% improvement in task assignment accuracy in task assignment experiments.

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A crowd cooperative defense model for mitigating DDoS attacks in Mobile Crowdsensing networks

Cited by 3 publications

References 29 publications

Velocity tracking control of nodes for the nonlinear complex dynamical networks associated with outgoing links subsystem

Velocity tracking control of nodes for the nonlinear complex dynamical networks associated with outgoing links subsystem

A cooperative detection scheme for malicious nodes based on D-S trust evidence reasoning in mobile crowdsensing networks

Malicious Participants and Fake Task Detection Incorporating Gaussian Bias

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