EA-POT: An Explainable AI Assisted Blockchain Framework for HoneyPot IP Predictions

Abstract

The culpable cybersecurity practices that threaten leading organizations are logically prone to establishing countermeasures, including HoneyPots, and bestow research innovations in various dimensions such as ML-enabled threat predictions. This article proposes an explainable AI-assisted permissioned blockchain framework named EA-POT for predicting potential defaulters' IP addresses. EA-POT registers the predicted defaulters based on the suggestions levied by explainable AI and the approval of IP authorizers to blockchain database to enhance immutability. Experiments were carried out at IoT Cloud Research laboratory using three prediction models such as Random Forest Modeling (RFM), Linear Regression Modeling (LRM), and Support Vector Machines (SVM); and, the observed experimental results for predicting the AWS HoneyPots were explored. The proposed EA-POT framework revealed the procedure to include interpretable knowledge while blacklisting IPs that reach HoneyPots.