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Massive multiple-input multiple-output (maMIMO) networks have gained significant attention in recent years with their capability to enable efficient communication through offering base stations (BS) access to an enormous number of antennas to serve multiple user equipment (UE) at a time. One of the key challenges in maMIMO networks is optimal power allocation among UEs keeping the total power constraint at the BS intact. Despite the fact that deep learning-based techniques offer rapid power allocation, they are vulnerable to adversarial attacks on input data, leading to incorrect resource allocation. To address the above problems, this current study proposes a model named Resilient Power Allocation against Universal Attacks in Massive MIMO using Multi-Stream and Component-Aware Graph Networks (PADOM-AKGNTO). To start with, input data in terms of user equipment (UE) positions along with their corresponding optimal power allocations is collected and pre-processed via an Adaptive Fast Desensitized Kalman Filter (AFDKF) for the purpose of filtering noise and uncertainty reduction. For predicting optimal power allocation, a Multi-Stream Fusion Spatiotemporal Graph Convolutional Network (MSF-STGCN) is used to determine spatial and temporal relations among UEs in a dynamic scenario. For providing robust defense against adversarial attacks capable of manipulating UE position data, a Multi-Component Attention Graph Convolutional Neural Network (MCAGCN) is utilized as a resilient detection and mitigation module. A Termite Life Cycle Optimizer (TLCO) is also utilized to optimize weights and parameters of the models MSF-STGCN and MCAGCN for the purpose of improving accuracy and reducing convergence speed. The tested and validated PADOM- AKGNTO model employs different performance metrics and attains a prediction accuracy of 99.99%, defense success rate of 98.50%, and lowers the attack success rate significantly to 5.00%. The outcomes confirm that the suggested method facilitates efficient, accurate, and stable power distribution in hostile environments, thereby ensuring secure and reliable communication in maMIMO systems.