Deep neural network-based website fingerprinting (WF) attacks have significantly threatened anonymous communication systems, like The Onion Router (Tor). WF defense methods based on adversarial learning techniques offer potential resistance against deep WF attacks. However, existing defenses primarily focus on packet-level perturbations, often neglecting fine-grained perturbations at the packet interval-level. In this paper, we present a novel WF defense method, called Amoeba, which is designed to generate traces that incorporate both packet-level and packet interval-level perturbations to defend against deep WF attacks while maintaining controllable overhead. Amoeba employs a two-stage framework: in the first stage, it learns the trace patterns by adversarial learning techniques with Wasserstein Generative Adversarial Networks (WGAN). The second stage introduces a dynamic cost control mechanism to pad dummy packets into normal traces to perturb trace patterns with low bandwidth overhead and time costs. Furthermore, we collect a new Tor trace dataset with Tor anonymous communication system and conduct extensive experiments on both a public dataset and our collected dataset. Experimental results demonstrate that Amoeba can reduce the classification accuracy of deep WF attacks from over 90% to below 40%, and outperform existing defenses. Moreover, Amoeba has over 4% bandwidth overhead and 13% time costs reduction than existing defenses, which shows the effectiveness and superiority of Amoeba in resisting deep WF attacks compared to existing methods.