Controlling Runtime-Anomaly of A Web Application Using Advanced Machine Learning
Keywords:
Runtime anomaly, Machine learning, Web Application Monitoring, Autoencoders, SecurityAbstract
INTRODUCTION: Controlling run-time anomalies using machine learning in software will help us to increase product efficiency and reliability. In this field, we are still in a very early stage. In our case, for a Web-Application the anomaly detection and correction at run time is yet to be developed.OBJECTIVES: The goal of this study is to provide a method to Detect and Classify an anomaly and Fix that on run time using Machine Learning.
METHODS: A systematic step-by-step approach is launched with the study of 57 papers on anomaly detection algorithms and Runtime error detection and correction methodologies
RESULTS: The major number of papers are related to network anomaly detection algorithms (42%) and another real-time system (like flight/train, etc.) error detection and corrections techniques (18%). Papers related to software error detection are 30%, and papers our research focused on are 10%.
CONCLUSION: To detect an anomaly in the application log, we would be using the Pattern search and Local Outlier Factor to detect and classify the errors. Then finally, using the Generative AI, we will fix the detected errors.
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