Enhancing security in social computing systems through knowledge learning techniques

Abstract

Social computing systems (SCS) integrate social behaviour with computational hardware to enable conversations among technologies and individuals. To protect the integrity of behavioural integration from illegal device communications, it is essential to ensure that security is maintained inside SCS. This study aims to provide a Coherent Authorization and Authentication Technique (CA2T) specifically designed to ensure the safety of SCS connections. CA2T uses various user credentials to approve interactions while guaranteeing that authentication is not replicated. Computed and behavioural data authentications are separated through knowledge-based learning to reduce the amount of security overheads. Credential verification that is adaptable and based on different calculation needs is the initial step in the device authorization process. After that, end-to-end authentication uses a lightweight signature based on two factors for verifying interactions between devices. The experimental findings show that when compared to the leading baseline approach, NTSC, CA2T reduces false positives by 9.48 %, computational overhead by 12.28 %, authentication time by 11.38 %, and failure rates by 11.48 %. With these improvements CA2T has emerged as much more effective than previous security frameworks for protecting SCS environments; it remains scalable, has minimal latency, and can adapt to new environments.