CIRA: CYBER INTELLIGENT RISK ASSESSMENT USING RNN FOR PREDICTIVE THREAT ANALYSIS

Abstract

As cyber-attacks become more frequent and sophisticated, harmful systems will be needed to help predict when an attack may occur so that appropriate proactive measures can be adopted. The “Cyber Intelligence Risk Assessment Using RNN for Predicting Threat Analysis (CIRA)” project aims to create a more intelligent method for detecting cyber-attacks by combining both historical data and real-time network activity data to develop an intelligent risk assessment framework. A number of different machine learning models such as Linear Regression, Gaussian Naive Bayes, support vector machine, decision tree, random forest, and convolutional neural network have been tested as predictive technologies, and the convolutional neural network demonstrated the highest level of predictive accuracy such as precision, recall, F1, area under the curve than all other methods in predicting and detecting potentially malicious acts. After using these predictions to assess the level of risk posed by each predicted threat, CIRA will generate suggested preferences for mitigation, thus enabling organizations to improve their security and better protect themselves against the potential consequences of a cyber-attack. The ultimate goals of this initiative include increasing the accuracy of threat detection, assisting with rapid decision-making, mitigating damage as a result of cyber-attacks, and improving the overall security of networked systems.