Praveen Ravula, University of Florida, United States of America
This paper presents the U.S. AI-driven Vulnerability Assessment and Containment Framework (US-AIVAC framework), which can be used to better defend and resilient U.S. critical infrastructure against increasing cybersecurity risks. The suggested system will incorporate intelligent preprocessing, risk assessment through machine learning, automated containment systems to detect and remove vulnerabilities in the infrastructure settings. First, data preprocessing methods (e.g. data cleaning, normalisation, etc.) are done to make the data consistent and reliable to be analysed. To accomplish the vulnerability risk assessment, the Deep Neural Network (DNN) model is used, and vulnerabilities related attributes are identified, and the vulnerability severity are predicted. Moreover, a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) network is employed to accommodate spatial and sequential rates of vulnerability data and thus detect intricate cyber-attack behaviours. The suggested US-AIVAC model is expected to enhance the priority of vulnerabilities, enhance threat detection and response to cybersecurity in a timely manner.
AI-Driven Cybersecurity, Vulnerability Risk Assessment, CNN–LSTM Threat Detection, Automated Containment Framework, U.S. Critical Infrastructure Protection.
