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International Journal of Basic Science and Technology

A publication of the Faculty of Science, Federal University Otuoke, Bayelsa State

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Archive | ISSUE: , Volume: Oct-Dec-2023

A Deep Learning Model for the Protection of Web Applications Using Behavioural Biometrics


Author:Eleje, B. C.; Ohia, O.

published date:2023-Oct-03

FULL TEXT in - | page 145 - 153

Abstract

Web applications have become a crucial platform for billions of users, serving various purposes and facilitating access to numerous online platforms. Unfortunately, the increasing popularity of web apps has made them a prime target for cybercriminals, who carry out attacks such as malicious scripts, logic injection, denial of service, etc. In light of the prevalent web application attacks, this study proposes an effective methodology. The approach involves training an LSTM model on a dataset comprising diverse web application attacks. However, the dataset is highly unbalanced, so the Random Over Sampling technique is employed to address this issue. After resolving the dataset's imbalance, it is pre-processed by cleaning and tokenizing the data. The tokenized data is then converted into an array and used as input for the RNN model. The proposed LSTM model undergoes training for 50 epochs, with each epoch providing accuracy and loss values for both the training and testing data. Following the training, the model is tested 18,401 times. The test results indicate that the model achieves an accuracy result of 99.21% for training and 90.1% for testing. This shows that the model is in good performance.

Keywords: Web applications; Behavioural metrics; Deep learning;,,,,

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