Abstract
The clients are facing the issues once the application is deployed in a production environment because while development, developers by mistake or wrongly coded without encrypting the sensitive information or even the testing team tested the functionality of the application but not looked into the database for what is stored in place of sensitive information. The sensitive information may be hacked by hackers or due to storing sensitive information in the database the customer’s important information is exposed to the outside world. To provide security in software applications various data science approaches are used like machine learning and natural language processing (NLP). In the current statement, proposing a technique uses machine learning and deep learning techniques which will scan the entire application code and databases used. The model is trained with machine learning algorithms. To identify sensitive information in all programming files in application, natural language processing term frequency–inverse document frequency (tf–idf) technique is used. By using tf–Idf, it will detect whether sensitive information is present in each document or total documents. The model will recognize the user’s sensitive information like password, mobile number, account number, card verification value (CVV), and all the sensitive information which client or customer feels it is sensitive information. As per the standards of security algorithms, all the sensitive information of customers is verified. When the sensitive data is not in encrypted format, model which is trained will automatically encrypt the data with encryption algorithms. By default, data encryption standard (DES) algorithm will be used for encryption. But this tool is designed in a more convenient way so that the client will choose different algorithms for encryption or key size which used in the algorithm. This tool can be used as a security measure for any software application developed before deploying in a production environment.
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Acknowledgements
The author wants to convey the thanks to SureIT solutions Inc, Dr. Aruna Varanasi, Sangeet Mohanty for supporting and providing guidance throughout the paper writing. SureIT solutions Inc provided the required infrastructure during writing a paper to test and develop the required code.
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Yaganteeswarudu, A., Varanasi, A., Mohanty, S. (2021). Security in Software Applications by Using Data Science Approaches. In: Shakya, S., Balas, V.E., Haoxiang, W., Baig, Z. (eds) Proceedings of International Conference on Sustainable Expert Systems. Lecture Notes in Networks and Systems, vol 176. Springer, Singapore. https://doi.org/10.1007/978-981-33-4355-9_27
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