| """ |
| Reinforced Disentangled HTML/DOM Representation Learning with Hard-Sample Mining |
| (Yoon, Buu, & Kim, 2025) |
| Separates structural DOM tree topology from surface text semantics to expose obfuscated phishing templates. |
| """ |
| import numpy as np |
|
|
|
|
| class DisentangledDOMEncoder: |
| """ |
| Projects raw HTML/DOM tree metrics into two orthogonal subspaces: |
| 1. Structural Subspace (z_struct): DOM depth, tag transitions, hidden form/iframe manipulations. |
| 2. Semantic Subspace (z_sem): Textual token distribution and form field labels. |
| Includes Hard-Sample Mining loss weighting to focus gradient updates on deceptive clones. |
| """ |
|
|
| def __init__(self, input_dim: int = 24, latent_dim: int = 32, seed: int = 101): |
| rng = np.random.default_rng(seed) |
| self.W_struct = rng.normal(0, np.sqrt(2.0 / input_dim), size=(input_dim, latent_dim)) |
| self.b_struct = np.zeros(latent_dim) |
| self.W_sem = rng.normal(0, np.sqrt(2.0 / input_dim), size=(input_dim, latent_dim)) |
| self.b_sem = np.zeros(latent_dim) |
|
|
| def forward(self, x_dom: np.ndarray) -> tuple[np.ndarray, np.ndarray, float]: |
| """ |
| Computes disentangled structural and semantic representations and their orthogonality penalty. |
| """ |
| z_struct = np.tanh(x_dom @ self.W_struct + self.b_struct) |
| z_sem = np.tanh(x_dom @ self.W_sem + self.b_sem) |
|
|
| |
| corr_matrix = (z_struct.T @ z_struct) if z_struct.shape[0] == 0 else (z_struct.T @ z_sem) / max(1, x_dom.shape[0]) |
| ortho_loss = float(np.mean(corr_matrix ** 2)) |
|
|
| z_combined = np.concatenate([z_struct, z_sem], axis=-1) |
| return z_combined, z_struct, ortho_loss |
|
|
| @staticmethod |
| def mine_hard_samples(probs: np.ndarray, labels: np.ndarray, margin: float = 0.25) -> np.ndarray: |
| """ |
| Hard-Sample Mining (Yoon et al., 2025): Assigns amplified sample weights (2.5x) |
| to borderline or misclassified samples where |y - p| > margin. |
| """ |
| errors = np.abs(labels - probs) |
| weights = np.where(errors >= margin, 2.5, 1.0) |
| return weights |
|
|
