MONITORING REMOTE SERVICE PLATFORMS USING ARTIFICIAL INTELLIGENCE-BASED DISTRIBUTED INTRUSION DETECTION

Abstract

Due to their flexibility, remote support platforms are ideal for contributing to companies' digital strategy. Simultaneously, this flexibility of use cases makes it difficult to reliably detect attacks on the network infrastructure. This paper presents a proposal for the detection of fraud patterns on remote service platforms through artificial intelligence. A blockchain-based approach will be used to adapt these attack signatures to the specific use cases of remote service platform users. By employing a blockchain-based attack signature selection mechanism, remote service platform users will be able to adjust the attack signatures flexibly and in a tamper-proof manner.