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 $61.5\%$.

翻译：移动传感系统很容易受到各种攻击,因为这些系统是建立在非专用和无所不在的特性之上的。对机械学习(ML)方法进行了广泛的调查,以建立攻击探测系统并确保监控监系统的安全。然而,旨在堵塞感知前端和监控监后端的智能技术的对手,这些技术对监控监平台和服务提供者制定适当的检测框架以对付这些攻击具有挑战性。生成反动网络(GANs)被用于生成合成样品,这些样品与真实样品极为相似,对合成样品的分类师进行欺骗,例如合成样品无法与原始样品分辨。以前的工作表明,基于GAN的攻击的破坏力比实验性设计的攻击样品更为严重,导致MCS平台的检测率较低。考虑到这一点,本文件的目的是通过整合基于GAN的模型来检测设计出不合法的检测服务请求。为此,我们建议用两个级的正向级分类器,将GAN解分解器和硬质分类器组合起来,以防止对等值的合成样品进行不易变的标值。通过模拟,我们用双级的SLA(A)将结果升级到单级,我们向单一的RDRRR),我们用一个结果显示一次的结果。