The nondedicated sensing capabilities of smart mobile devices contribute to Internet of Things (IoT) ecosystems with integral building blocks called mobile crowdsensing (MCS) systems. The distributed and nontrusted nature of MCS systems leads to various threats for devices and MCS platforms as well as for end users. Out of the many threats, fake tasks may lead to drained resources at the participating devices and clogged resources at the MCS platforms. Furthermore, when limited data are available, it becomes a further challenge to identify maliciously submitted fake tasks. In this article, we introduce possible solutions that leverage ensemble learning against fake tasks submitted to MCS platforms. More specifically, boosting-based solutions, namely adaptive boosting for binary classification (AdaBoost), gentle adaptive boosting (GentleBoost), and random under-sampling boosting (RUSBoost), form the basis for learning the legitimacy of tasks submitted to MCS platforms. Over a six-day observation window, one day was used for training while the remaining five days were used for testing to evaluate the performance under limited data in terms of training the machine learning (ML) models. Through extensive simulations, we have shown that GentleBoostbased ensemble learning can achieve promising performance in detecting fake/illegitimate tasks submitted to an MCS platform.

中文翻译：

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检测虚假移动人群感知任务：有限数据下的集成方法

智能移动设备的非专用传感功能有助于物联网 (IoT) 生态系统，其中包含称为移动人群感应 (MCS) 系统的集成构建块。MCS 系统的分布式和不可信特性导致对设备和 MCS 平台以及最终用户的各种威胁。在众多威胁中，虚假任务可能会导致参与设备的资源耗尽并阻塞 MCS 平台的资源。此外，当可用数据有限时，识别恶意提交的虚假任务成为进一步的挑战。在本文中，我们介绍了利用集成学习对抗提交给 MCS 平台的虚假任务的可能解决方案。更具体地说，基于 boosting 的解决方案，即二进制分类的自适应 boosting (AdaBoost)、温和的自适应 boosting (GentleBoost)、和随机欠采样提升 (RUSBoost)，构成了学习提交给 MCS 平台的任务的合法性的基础。在为期六天的观察窗口中，一天用于训练，其余五天用于测试，以评估在训练机器学习 (ML) 模型方面在有限数据下的性能。通过广泛的模拟，我们已经表明，基于 GentleBoost 的集成学习可以在检测提交给 MCS 平台的虚假/非法任务方面取得有希望的性能。一天用于训练，而剩下的五天用于测试，以评估在训练机器学习 (ML) 模型方面在有限数据下的性能。通过广泛的模拟，我们表明基于 GentleBoost 的集成学习可以在检测提交给 MCS 平台的虚假/非法任务方面取得良好的性能。一天用于训练，而剩下的五天用于测试，以评估在训练机器学习 (ML) 模型方面在有限数据下的性能。通过广泛的模拟，我们表明基于 GentleBoost 的集成学习可以在检测提交给 MCS 平台的虚假/非法任务方面取得良好的性能。