<p>Website fingerprinting attacks are critical for extracting website information and identifying illegal websites visited by users in anonymous networks such as Tor. However, existing attacks struggle to extract effective features from unmonitored websites due to the diversity. Although increasing unmonitored training data can improve effectiveness, it also increases attacker costs. To address this, we propose a novel website fingerprinting attack that leverages unsupervised Out-of-Distribution detection. We exclusively use monitored website data for model training, eliminating the need for extensive unmonitored samples. For feature extraction, we utilize a combination of Long Short-Term Memory and Convolutional Neural Networks for robust feature extraction of each monitored website. We also introduce a new loss function to maximize differentiation between features of various websites. Furthermore, we employ Singular Value Decomposition to effectively segregate monitored from unmonitored websites. It allows the model to focus on dominant components in the feature vectors, facilitating a clear distinction between monitored and unmonitored website traffic. The experimental results confirm that our method outperforms existing techniques without requiring unmonitored training data.</p>