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Mobile Crowdsensing systems are vulnerable to various attacks as they build on non-dedicated and ubiquitous properties. Machine learning (ML)-based approaches are widely investigated to build attack detection systems and ensure MCS systems security. However, adversaries that aim to clog the sensing front-end and MCS back-end leverage intelligent techniques, which are challenging for MCS platform and service providers to develop appropriate detection frameworks against these attacks. Generative Adversarial Networks (GANs) have been applied to generate synthetic samples, that are extremely similar to the real ones, deceiving classifiers such that the synthetic samples are indistinguishable from the originals. Previous works suggest that GAN-based attacks exhibit more crucial devastation than empirically designed attack samples, and result in low detection rate at the MCS platform. With this in mind, this paper aims to detect intelligently designed illegitimate sensing service requests by integrating a GAN-based model. To this end, we propose a two-level cascading classifier that combines the GAN discriminator with a binary classifier to prevent adversarial fake tasks. Through simulations, we compare our results to a single-level binary classifier, and the numeric results show that proposed approach raises Adversarial Attack Detection Rate (AADR), from $0\%$ to $97.5\%$ by KNN/NB, from $45.9\%$ to $100\%$ by Decision Tree. Meanwhile, with two-levels classifiers, Original Attack Detection Rate (OADR) improves for the three binary classifiers, with comparison, such as NB from $26.1\%$ to $61.5\%$., Comment: This paper contains pages, 4 figures which is accepted by IEEE ICC 2022

Taking the piezoelectric flexible plate as research object, this paper presents an efficient method to simulate and analyze intelligent structures with MATLAB and ANSYS applied. Firstly, this paper focuses on the development of modeling method for intelligent structures with distributed sensors and actuators. Modal analysis and transient analysis were done by using ANSYS 11.0 software, with the finite model of the intelligent plate constructed. Then based on an APDL (the design language of ANSYS) program, the vibration control experiments were done. By analyzing different control performance of different actuator assign position, the optimization criterion suggested by NASA for piezoelectric sensors and actuators arrangement was verified. While the optimal configuration was chosen, the control channel and primary path model of the vibration control system were obtained with MATLAB system identification toolbox employed. Finally according to the identified model an iterative learning control system was developed. The analysis and experimental results show that the analysis method for intelligent structures proposed in this paper is economical and effective and the designed controller has a good vibration suppression performance.