Hi xingtianchunyan,
Following up with the completed pivot and full analysis report: ckb-intel/REPORT.md at main · FadhilMulinya/ckb-intel · GitHub
Summary of where things stand:
Data & pivot: All synthetic/bot-simulator code and data paths have been fully removed. The pipeline now runs exclusively on real CKB mainnet data and we used 251 labeled wallets in the current training run (91 human-like, 160 bot-like), pulled directly via the mainnet Explorer API.
Labels: Initially generated via a heuristic (is_special flag + lifetime tx count), then manually verified across the full 251-wallet trained set .Every label individually and manually reviewed against the wallet’s actual transaction history. Zero disagreements found between the heuristic and manual review.
Model - important correction: Your last message referenced “the neural network methodology” .
I want to flag directly that the deployed and evaluated model is a Random Forest, not a neural network. It was selected after cross-validating against Logistic Regression, Gradient Boosting, and SVM-RBF (full comparison table in §4.1 of the report); Random Forest won cleanly (95.2% CV accuracy, 0.986 ROC-AUC) and gives directly interpretable per-feature importance scores, which lines up with what you flagged as the grant’s core reusable value .The documented behavioral dimensions and weights (§5). If a neural architecture is specifically required rather than just implied, let me know and I’ll revisit ,but wanted to raise this now rather than let it stand uncorrected.
Architecture also slightly pivoted: the project is now split into two independently deployable services rather than one combined system . A Python classifier-service that does all the modeling (fetch history → extract features → classify), and a Node registry-service that resolves wallet identity and stores whatever label the classifier hands back, with no modeling logic of its own. Each runs standalone (Node has zero dependency on Python being up at all) and each publishes a live, interactive API reference atGET /api/v1/docs on both services .So anyone can invoke an endpoint directly and see exactly what’s there without reading code first. Full rationale for the split is documented in the repo README §6 GitHub - FadhilMulinya/ckb-intel · GitHub.
Results: 95.24% accuracy / 96.30% F1 on a held-out test split never touched during model selection. I also checked the model isn’t just exploiting wallets with very short transaction histories (§4.2) , accuracy holds at 93.75%+ even on wallets with 21+ transactions.
What’s not yet done: the model and API currently run locally only , there is no public endpoint, hosting, or demo video yet. Also worth flagging: of the four proposed behavioral dimensions (timing, frequency, wallet interaction graph, cell usage), timing and graph features are fully implemented and are in fact the strongest signals in the model (see §5); frequency is folded into timing rather than standalone, and cell usage currently covers transaction-flow shape but not cell-kind classification (SUDT/NFT/DAO, live/dead lifecycle).
Ask: Before I move on to public hosting and a demo, I want to confirm this aligns fully with what the committee expects at this stage . The real-mainnet pivot, the label verification approach, the two-service architecture, and the model choice in particular, given the earlier NN reference. If it’s aligned, I’ll proceed straight to hosting the model and servers and putting together the public demo. If anything needs revising first , or reconsidering the model architecture . Let me know and I’ll address it before moving forward.
Thanks for the guidance up to this point.
Best,
Fadhil