The project aims to detect the correct AWS key using five different approaches. Through these approaches, the model can analyze the patterns of the AWS keys and detect the correct one. Additionally, the model employs camel case splitting and other logical notes to further improve the detection process. As a result, the model achieved a 75% accuracy rate in detecting and correctly identifying the AWS keys. This is a significant improvement in the accuracy of detecting AWS keys, which can help prevent security breaches and ensure the proper functioning of AWS systems.
- Programming Languages: Python
- Frameworks: NLTK Toolkit, Tensorflow, Scikit-Learn
- Methods: EDA, Data Analysis
