Enhancing intrusion detection and Kerberos attack prevention with an integrated blockchain and AI-based approach

Abstract

In today's linked digital world, securing computer networks as well as systems is critical. The increasing complexity as well as the regularity of network attacks demand creative and effective intrusion detection solutions to protect against possible threats. Kerberos, an established token-based authentication technology, is notable for its cryptographic method, privacy assurance, and data protection whenever identifying eligible users. At the same time, it fails to offer proper channel protection for transmitting user credentials between the client and server pathways. This study presents an integrated approach for detecting Kerberos attacks and intrusions within computing systems and networks. It combines artificial intelligence and Blockchain technology with a proxy re-encryption scheme to enhance security measures. After pre-processing, the input data is recorded on the blockchain, subjected to proxy re-encryption, and stripped of noise. The utilization of threshold proxy re-encryption in the consensus process eliminates dependence on third-party central service providers. As proxy service nodes, a number of consensus nodes within the blockchain network re-encrypt data and combine translated ciphertext. Throughout the process, no personal information is revealed. In this study, the methods of Principal Component Analysis and Chi-square Test are used to reduce the dimension of the main components with the greatest variation and to discover and pick the most relevant features from the target variable. To detect the normal and attack systems, all selected important features have been categorized utilizing the KNN classifier. Throughout the investigation, the proposed approach was used to evaluate the openly available dataset KDD-99.