提出新防御框架，在保持高检测率的同时将计算开销降至1.76倍。

The deployment of robust malware detection systems in big data environments requires careful consideration of both security effectiveness and computational efficiency. While recent advances in adversarial defenses have demonstrated strong robustness improvements, they often introduce computational overhead ranging from 4x to 22x, which presents significant challenges for production systems processing millions of samples daily. In this work, we propose a novel framework that combines Trust-Raw Override (TRO) with Confidence-Adaptive Bit-Depth Reduction (CABDR) to explicitly optimize the trade-off between adversarial robustness and computational efficiency. Our approach leverages adaptive confidence-based mechanisms to selectively apply defensive measures, achieving 1.76x computational overhead - a 2.3x improvement over state-of-the-art smoothing defenses. Through comprehensive evaluation on the EMBER v2 dataset comprising 800K samples, we demonstrate that our framework maintains 91 percent clean accuracy while reducing attack success rates to 31-37 percent across multiple attack types, with particularly strong performance against optimization-based attacks such as C and W (48.8 percent reduction). The framework achieves throughput of up to 1.26 million samples per second (measured on pre-extracted EMBER features with no runtime feature extraction), validated across 72 production configurations with statistical significance (5 independent runs, 95 percent confidence intervals, p less than 0.01). Our results suggest that practical adversarial robustness in production environments requires explicit optimization of the efficiency-robustness trade-off, providing a viable path for organizations to deploy robust defenses without prohibitive infrastructure costs.