Side-channel attack, which exploits time information leakage during the digital signature generation process, poses a severe threat to the confidentiality and integrity of transactions in blockchain payment channels. Currently, Transformer-based detection methods are effective at capturing long-term dependencies, while Long Short-Term Memory (LSTM) networks excel at modeling short-term dynamic time-series features. However, existing approaches struggle to uniformly model both long-term and short-term time-series features, limiting their performance in anomaly detection for complex transaction sequences. In this article, we propose TLD-SCA, a novel side-channel attack detection model that innovatively integrates Transformer’s capability for global dependency modeling with LSTM’s advantage in capturing local time-series dynamics. This enables long-term and short-term time-series analysis of transaction timing data. Experimental results demonstrate that TLD-SCA significantly outperforms existing methods in terms of accuracy (99.5%), precision (99.2%), and recall (98.3%), thereby providing a higher level of security assurance for blockchain payment channels.