Region-Aware Bagging and Deep Learning-Based Fake Task Detection in Mobile Crowdsensing Platforms

Zhiyan Chen, Murat Simsek, B. Kantarci
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引用次数: 1

Abstract

Mobile crowdsensing (MCS) is a distributed sensing concept that enables ubiquitous sensing services via various builtin sensors in smart devices. However, MCS systems are vulnerable because of being non-dedicated. Especially, submission of fake tasks with the aim of clogging participants device resources as well as MCS servers is a crucial threat to MCS platforms. In this paper, we propose an ensemble learning-based solution for MCS platforms to mitigate illegitimate tasks. Furthermore, we also integrate k-means-based classification with the proposed method to extract region-specific features as input to the machine learningbased fake task detection. Through simulations, we compare the ensemble method to a previously proposed Deep Belief Network (DBN)-based fake task detection, which is also shown to improve performance in terms of accuracy, F1 score, recall, precision and geometric mean score (G-mean) with the integration of regionawareness. Our validation results show that the ensemble machine learning-based detection can eliminate majority of the fake tasks, with up to 0.995 precision, 0.997 recall, 0.996 F1, 0.993 accuracy and 0.982 G-Mean. Furthermore, the proposed solution introduces savings up to 12.18% battery of mobile devices while reducing the impacted recruits to 0.25% and protecting up to 10.59% participants against malicious sensing tasks.
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基于区域感知装袋和深度学习的移动众测平台假任务检测
移动群体感知(MCS)是一种分布式感知概念,通过智能设备中的各种内置传感器实现无处不在的感知服务。然而,MCS系统由于非专用而容易受到攻击。特别是,以阻塞参与者设备资源和MCS服务器为目的的虚假任务提交是对MCS平台的重大威胁。在本文中,我们提出了一种基于集成学习的MCS平台解决方案,以减少非法任务。此外,我们还将基于k均值的分类与所提出的方法相结合,以提取特定区域的特征作为输入,用于基于机器学习的假任务检测。通过仿真,我们将集成方法与先前提出的基于深度信念网络(Deep Belief Network, DBN)的假任务检测方法进行了比较,结果表明,集成区域感知后,集成方法在准确率、F1分数、召回率、精度和几何平均分数(G-mean)方面都有所提高。我们的验证结果表明,基于集成机器学习的检测可以消除大部分假任务,精度高达0.995,召回率为0.997,F1为0.996,准确度为0.993,G-Mean为0.982。此外,提出的解决方案引入节省高达12.18%的移动设备电池,同时将受影响的新兵减少到0.25%,并保护高达10.59%的参与者免受恶意传感任务的影响。
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