% Table 2: Comparative matrix of detection techniques
\begin{table*}[!htbp]
\centering
\caption{Comparative matrix of anomaly- and fraud-detection techniques for blockchain networks. For each technique family we list the supervision regime (Unsupervised / Semi-supervised / Supervised), the primary data type consumed (Time-series, Graph, Mixed), principal strengths and limitations relevant to financial-infrastructure deployment, and representative scholarly references. This matrix is intended to assist practitioners in matching a detection technique to the data and operational constraints of a specific use case; the strengths and limitations are qualitative summaries of the balance of evidence in the cited literature, not absolute rankings.}
\label{tab:techniques}
\small
\begin{tabular}{@{}p{2.6cm}p{1.9cm}p{1.5cm}p{3.0cm}p{3.0cm}p{2.2cm}@{}}
\toprule
\textbf{Technique} & \textbf{Supervision} & \textbf{Data} & \textbf{Strengths} & \textbf{Limitations} & \textbf{References} \\
\midrule
ARIMA / SARIMA & Unsupervised & Time-series & Interpretable; low compute & Assumes stationarity; poor on sudden regime shifts & Ahmed et al. 2016 \\
K-means / DBSCAN clustering & Unsupervised & Feature vectors & Label-free; scalable & Sensitive to feature engineering & Pham \& Lee 2017 \\
Mahalanobis distance & Unsupervised & Feature vectors & Accounts for covariance & Gaussian assumption; degenerates in high dim & Pham \& Lee 2017 \\
Isolation Forest & Unsupervised & Feature vectors & Good high-dim behaviour; fast & Black-box scoring & Shayegan \& Sabor 2021 \\
Autoencoder reconstruction & Semi-supervised & Mixed & Learns complex benign manifold & Hyperparameter-sensitive; explainability gap & Taher et al. 2024 \\
Graph Neural Networks (GCN, GAT) & Supervised / Semi & Graph & Captures relational structure & Requires labels or proxies; compute-heavy & Li et al. 2019 \\
Recurrent / transformer sequence models & Supervised & Time-series & Captures long-range dependencies & Label scarcity; overfitting risk & Kim et al. 2021 \\
Ensemble + XAI & Supervised & Mixed & Accuracy + interpretability for compliance & Label requirement; moderate compute & Taher et al. 2024 \\
Bayesian game models & (formal) & N/A & Captures strategic interaction & Hard to parameterise realistically & Baqer et al. 2016 \\
Mechanism design & (formal) & N/A & Preventive; removes incentive & Requires protocol-level change & -- \\
Hash / signature forensic analysis & Supervised & On-chain & Legal-evidence admissibility & Compute for large-window tracing & Ron \& Shamir 2013 \\
Transaction-graph forensics & Unsupervised & Graph & Traces fund flows across chains & Breaks on mixers; cross-chain gaps & Maesa et al. 2018; Ober et al. 2013 \\
\bottomrule
\end{tabular}
\end{table*}
