Image analysing to differentiate human users or bots using Convolutional Neural Network (CNN) / Muhamad Anif Ikmal Rusdi
In today's digital landscape, telling the difference between human users and bots has become tricky. To tackle this issue, research focuses on creating a system that uses image analysis to identify and classify entities as either human users or bots. The approach involves collecting a dataset o...
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| Format: | Thesis |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/96290/1/96290.pdf |
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| Summary: | In today's digital landscape, telling the difference between human users and bots has become tricky. To tackle this issue, research focuses on creating a system that uses image analysis to identify and classify entities as either human users or bots. The approach involves collecting a dataset of images, processing the data, and training a model—like a Convolutional Neural Network (CNN)—to accurately distinguish between the two. The study demonstrates the effectiveness of using image analysis, particularly CNNs, in achieving high accuracy rates across various scenarios. The main tasks include gathering data, implementing image analysis techniques, training the model, and evaluating performance. The results emphasize the potential of image analysis-based systems for reliable differentiation, contributing to improved online security measures and prevention of malicious activities. This research aims to provide a straightforward solution to the challenge of distinguishing between human users and bots, with the ultimate goal of enhancing online security, particularly in the context of cybersecurity in Malaysia. |
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