Online social network automation attack detection methods for energy analysis and consumption modelling

Preventing the spread of Domain Name System (DNS) tunnelling and the use of automation infrastructure is one of the key problems that the idea of a contemporary smart OSN (online social network) must address. The proposed technique focuses on online social network automation attacks (OSNAA) based on smart social network devices (SSND) energy consumption (Ecomp) analysis based on user’s selection modes (USM) for the detection of OSNAA and Man-in-the-Middle (MiTM) attacks. Automated opcode sequence analysis (OSA) is used to improve the accuracy of OSNAA detection and localise MiTM attacks. It analyses the performance of the SSND for MiTM attacks by localising suspicious automated tools on these devices with applicable accuracy and is based on the monitoring of energy consumption, which helps to analyse and categorise the behaviour of the SSND in the state of normal or in compromised conditions by the threat agents. The suggested technique enables high-efficiency detection of SSND threats, such as MiTM attacks, at a level of about 99.91% and localization of suspicious automation tools on these machines with an accuracy of around 99.73%.