SBOA-Inspired Particle Swarm Optimization of XGBoost for False Data Injection Detection in P2P Energy Trading

Peer-to-peer (P2P) energy trading depends on the integrity of smart-meter measurements and coordinator messages. This paper presents an XGBoost detector for false data injection attacks and a hybrid hyperparameter-search mechanism combining particle swarm optimization (PSO) with three hunting-inspired operators from the secretary bird optimization algorithm (SBOA). A common candidate-evaluation budget enables comparison of fixed XGBoost, PSO-XGBoost and SBOA-PSO-XGBoost. The evaluation covers four Austin dataset settings, two attack scenarios and nine classifiers, using accuracy, precision, recall, F1 and computational cost. The equal-scenario mean F1 scores are 90.06%, 94.47% and 96.94% for the three XGBoost variants, respectively. The hybrid adds 2.47 percentage points over PSO with a 5.16% increase in total computation time, while its batch-prediction cost remains close to that of PSO-XGBoost. These comparisons characterize the detection-cost trade-off and the role of hybrid search under a shared evaluation protocol.