Elephant flow identification is a fundamental task in network measurements. Currently, the mainstream methods generally employ sketch data structure Sketch to quickly count network traffic and efficiently find elephant flows. However, the rapid influx of numerous packets will significantly decrease the identification accuracy of elephant flows under network traffic jitters. To this end, this study proposes an elastic identification method for elephant flows supporting network traffic jitters, which is named RobustSketch. This method first designs a stretchable mice flow filter based on the cyclic Sketch chain, and adaptively increases and reduces the number of Sketch in real-time packet arrival rates. As a result, it always completely records all arrived packets within the current period to ensure accurate mice flow filtering even under network traffic jitters. Subsequently, this study designs a scalable elephant flow record table based on dynamic segmented hashing, which adaptively increases and reduces segments according to the number of candidate elephant flows filtered out by the mice flow filter. Finally, this can fully record all candidate elephant flows and keep high storage space utilization. Furthermore, the error bounds of the proposed mice flow filter and elephant flow recording table are provided by theoretical analysis. Finally, experimental evaluation is conducted on the proposed elephant flow identification method RobustSketch with real network traffic samples. Experimental results indicate that the identification accuracy of elephant flows of the proposed method is significantly higher than that of the existing methods, and can stably keep high accuracy of over 99% even under network traffic jitters. Meanwhile, its average relative error is reduced by more than 86%, which enhances the accuracy and robustness of elephant flow identification.