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Machine learning based file type classifier designing in IoT cloud

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Abstract

With the increase interest and number of the users in Social media, the file handling has also increased. To manage the load, cloud servers are being used by the service providers. To identify and cluster file is a difficult task that is important in the domain of computer science. Various traditional approaches for identification exists that uses design features. The problem with these methods is get that they can be easily spoofed. To resolve the issue, in this paper, a hybrid algorithm combining the features of Random Forest with AdaBoost is proposed. The algorithm Internet of Thing (IoT) data file formatting (IDFF) classifies data as (text, image, audio and video) and gives better accuracy. Our proposed research obtains better Accuracy (93%), Precision (95%), Recall (95%), F-Measure (95%), and G-Mean (96%).

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Authors and Affiliations

  1. GLA University, Mathura, UP, 201406, India

    Puneet Sharma, Manoj Kumar & Ashish Sharma

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  1. Puneet Sharma
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  2. Manoj Kumar
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All authors contributed to the study conception and design. Material pre-pration,data collection and analysis were performed by [PS]. The first draft of the manuscript as written by [PS]. All authors read and approved the final manuscript.

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Correspondence to Puneet Sharma.

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Sharma, P., Kumar, M. & Sharma, A. Machine learning based file type classifier designing in IoT cloud. Cluster Comput 27, 109–117 (2024). https://doi.org/10.1007/s10586-022-03816-8

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