Multimodal Anomaly Detection and Probabilistic Risk Prognostication for IT Infrastructure: A Hyperparameter Tuned Random Forest Regressor a Probabilistic Regression Model

In this technological era, it has been a hurdle for organizations to maintain their assets and when it is called asset, it ranges from physical to logical assets. Apart from assets, it is also an uphill climb to track the risk arising from those assets. It has always been a milestone for IT organizations to maintain as well as predict risks associated with their organization to be a proactive level of management. But it has been a discretion between asset management and risk identification. The motive of this project is to incorporate both the tasks in a single shot and the main goal of the project is to deliver an intelligent IT asset and risk management system that uses machine learning technology to predict and assess risk associated with IT assets. The system incorporates a comprehensive neural network and regression model, which has high precision rate to analyze the dataset while the traditional application uses linear and classification model which analyses the data in inefficient way and it is prone to overfitting and underfitting. The main elements of the system include the web interface for user's asset data entry, data storage history and machine learning models for asset management testing and further sustenance. Users can easily upload asset data in CSV or Excel format, making integration with existing asset management systems easy. The significance of this project is that it changes the entire IT asset management process by introducing risk. By identifying and addressing risks before they escalate, organizations can reduce the impact of security incidents, improve resource allocation, and improve their overall resilient IT infrastructure.