Advance Security: Anomaly Detection in Mobile Crowd Sensing Using Machine Learning Techniques

Reddy, Sheri Mahender; Kakulapati, V.; Prince, Appiah

doi:10.2139/ssrn.3754180

Cited by 2 publications

(1 citation statement)

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“…But the new sensing paradigm requires an inventive protective solution. This research [22] investigates advanced mobile crowd sensing security using SVM (support vector machine) and ANN (artificial neural network) approaches. The author used full-blown implementation and experimental evaluation approaches, focusing on precision and false alarm rate.…”

Section: Literature Reviewmentioning

confidence: 99%

IOTA-Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit-Boosted Machine Learning Algorithms

Hameed

Yang

Ghafoor

et al. 2022

Wireless Communications and Mobile Computing

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In the Internet of Things (IoT) era, the mobile crowd sensing system (MCS) has become increasingly important. The Internet of Things Auto (IOTA) has evolved rapidly in practically every technology field over the last decade. IOTA-based mobile crowd sensing technology is being developed in this study using machine learning to detect and prevent mobile users from engaging in fake sensing activities. It has been determined through testing and evaluation that our method is effective for both quality estimation and incentive allocation. Using the IOTA Bottleneck dataset, multiple performance metrics were used to demonstrate how well logit-boosted algorithms perform. After applying logit-boosted algorithms on the dataset for the classification, Logi-XGB scored 95.7 percent accuracy, while Logi-GBC scored 90.8 percent accuracy. As a result of this, Logi-ABC had an accuracy rate of 89%. Logi-CBC, on the other hand, got the highest accuracy of 99.8%. Logi-LGBM and Logi-HGBC both scored 91.37 percent accuracy, which is identical. On the given dataset, our Logi-CBC algorithm outperforms earlier Logit-boosted algorithms in terms of accuracy. Using the new IoTA-Botnet 2020 dataset, a new proposed methodology is tested. In comparison to prior Logit-boosted algorithms, the new model Logi-CBC has a highest detection accuracy of 99.8%.

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Section: Literature Reviewmentioning

confidence: 99%

IOTA-Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit-Boosted Machine Learning Algorithms

Hameed

Yang

Ghafoor

et al. 2022

Wireless Communications and Mobile Computing

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Data sharing and exchanging with incentive and optimization: a survey

Liu,

Han

2024

Discov Data

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As the landscape of big data evolves, the paradigm of data sharing and exchanging has gained paramount importance. Nonetheless, the transition to efficient data sharing and exchanging is laden with challenges. One of the principal challenges is incentivizing diverse users to partake in the data sharing and exchange process. Users, especially those in potential competitive positions, often exhibit reluctance towards sharing or exchanging their data, particularly if they perceive the rewards as inadequate. Given this context, it’s imperative to institute an incentive mechanism that’s not only computationally efficient and secure but also provides both monetary and trustworthy inducements. This study introduces a taxonomy of incentive-based data sharing and exchanging, structured around its lifecycle, and elucidates the challenges inherent in each phase. We classify incentive mechanisms into monetary and non-monetary categories, postulating that the concomitant use of both types of incentives is more effective for data sharing and exchanging applications. Subsequent sections provide an overview of extant literature pertinent to each phase of the data sharing and exchanging lifecycle. In conclusion, we underscore the prevailing challenges in this domain and advocate for intensified efforts to refine the design of incentive mechanisms in data sharing and exchanging.

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Advance Security: Anomaly Detection in Mobile Crowd Sensing Using Machine Learning Techniques

Cited by 2 publications

References 0 publications

IOTA-Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit-Boosted Machine Learning Algorithms

IOTA-Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit-Boosted Machine Learning Algorithms

Data sharing and exchanging with incentive and optimization: a survey

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