Behavioral Information Leakage in Darknet Traffic: A Multi-Channel Analysis Across Anonymity Networks

Behavioral leakage is concentrated in a small subset of flow-level descriptors rather than uniformly distributed across the feature space. Globally, S_payload_per_packet (normalized MI = 0.173) and R_silence_density (normalized MI = 0.132) are the dominant leakage sources. No descriptor dominates across all networks: I2P leaks primarily through S_forward_packet_share (MI = 0.069) and R_burst_frequency_proxy (MI = 0.056), while FreeNet and ZeroNet rely on R_packet_tempo (MI = 0.066 and 0.101 respectively).

§4.2, Table 2, Figure 3 detection traffic-shapeml-classifier generic

A leave-one-network-out evaluation shows that behavioral classifiers trained on three anonymity networks (Tor, I2P, FreeNet, ZeroNet) fail to generalize to the held-out network, with all Macro-F1 scores remaining below 0.1822 regardless of whether structural, rhythmic, or combined descriptors are used. The highest individual result (Rhythmic, Tor held out: 0.1822) is still far below intra-network performance. This demonstrates that behavioral leakage signatures are architecture-specific rather than universal.

§4.1, Table 1 detection traffic-shapeml-classifier generic

Structural vs. rhythmic dominance is network-dependent: Tor traffic leakage is structurally dominated (S_byte_directional_dominance NMI = 0.258 vs. R_byte_tempo 0.198; dominance index DRF positive), while FreeNet and ZeroNet show rhythmic dominance with R_packet_tempo ranking as the strongest descriptor (NMI = 0.066 and 0.101). I2P shows near-balanced dominance. The Service Variability Index further reveals that video has consistent cross-network separability, whereas chat and email vary considerably across anonymity-network pairs.

§4.2, §4.4, Table 2 detection traffic-shapeml-classifier generic

Combining structural and rhythmic descriptors into a unified S+R representation consistently yields the highest within-network Macro-F1 across all four anonymity networks, providing evidence of complementary information in the two behavioral channels. However, the same fusion does not improve cross-network transferability: S+R performs similarly to or slightly worse than the best individual channel under leave-one-network-out evaluation (e.g., Tor held-out: Combined 0.1175 vs. Rhythmic 0.1822). This asymmetry indicates that structural–rhythmic interactions encode network-architecture-specific rather than universally transferable behavioral information.

§4.1, §4.3, Table 1 evaluation traffic-shapeml-classifier generic

Tor exhibits the highest service separability among the four evaluated anonymity networks, achieving a Macro-F1 of 0.7165 and a cumulative normalized behavioral leakage of 3.9461 under combined structural–rhythmic classification. FreeNet exhibits the lowest combined leakage (0.8744) and the lowest intra-network Macro-F1 (0.4510). The primary leakage drivers in Tor are directional byte-exchange asymmetry (S_byte_directional_dominance, normalized MI = 0.258) and byte tempo (R_byte_tempo, normalized MI = 0.198).

§4.2–4.3, Table 2–3 detection traffic-shapeml-classifier generic