Transactive Energy System (TES) is an emerging energy solution that ensures a dynamic balance between energy demand and supply. Energy trading among peers has the potential to drive future solutions that can provide a more secure, distributed, and sustainable power infrastructure.  The incorporation of peer-to-peer (P2P) to TES facilitates flexibility and minimizes pressure on the electrical grid. However, the system is vulnerable to cyberattacks due to the high volume of information exchanged across the energy network's communication channel. To mitigate the impact of cyber-attacks on the solution, it is necessary to explore the application of the Bayesian Price Clearing Auction (BPCA) model to enhance the resilience of TES against threats from adversaries.  The BPCA method is novel in TES in that it frames the market-clearing process as a statistical inference problem. This proposed model incorporates the uncertainty and heterogeneity factors in market behavior using volume-weighted averages and variances. The resulting equilibrium price is not just a deterministic mid-point, but a Bayesian estimate that “learns” from the offer’s distribution.