Hybrid energy and opcode sequence-based detection of automation attacks in online social networks using SSFCM classification

Key Points

• The research aims to develop a detection method for automation attacks targeting online social networks by utilizing hybrid energy and opcode sequences.
• Analyzed automation attack characteristics in online social networks.
• Developed a hybrid energy model and opcode sequence framework.
• Utilized SSFCM (Soft Scalar Fuzzy C-Means) classification to identify attacks.
• Successfully identified multiple types of automation attacks.
• Demonstrated improved detection accuracy compared to traditional methods.