Our AI Detects Your AI — Revealing the Secret Blockchain DApp World of Bots (Part 1 — EOS)


Key Insights

Figure 1: Model Prediction Results For Bot Prevalence in Top 10 EOS DApps — Unique Accounts (left), Transaction Volume (right)
Figure 2: Bot vs. Human Accounts (left) and Transaction Activity (right)

Why does blockchain bot detection matter ?

Figure 3: Internet Bot Research Report — Distill Networks, 2019 [1]
Figure 4: Bitwise‘s report shows trading bots found in several crypto exchanges [2].

Background: Blockchain Ecosystem and DApp

Figure 5: Comparison of Top 3 DApp-Friendly Blockchains (EOS, TRON, ETH). April 2019 Dapp.Review — Full Version in Appendix
Figure 6: TokenInsight’s Q1 2019 DApp Report
Figure 7: DApp Hot 20 EOS Board. March 2019, TokenInsight.

Blockchain Bot vs. Human Behaviors

Figure 8: EOS DApp Bot Blockchain Transactions (addresses made anonymous)
Figure 9: Typical Blockchain DApp Bot Behavior
Figure 10: Typical Human Account Behavior

Four Blockchain Bot Behavior Sophistication Levels

Figure 11: Hyperactive bots (24x7, nonstop, relatively linear)
Figure 12 : Regular patterns — runs every 4 hours (left), runs every hour (right)
Figure 13: Daily Active Bots Employing Perturbation Techniques
Figure 14: Blockchain APT [3] hacker group identified in a blockchain DApp in Aug 2018. Each dot in this massive graph represents a blockchain address, and the center is the targeted DApp, Fomo3D.

Why are blockchain bots challenging to detect?

Bad Bot vs Good Bot in Blockchain World

What can we do as an industry?

How does AnChain.AI detect bots?

Figure 15: AnChain.AI convolutional neural network (CNN) model architecture
Figure 16: Deep learning model performance on training (blue) and validation (orange) set. Achieved 99%+ accuracy on the validation set.
Figure 17: Random forest machine learning model for bot detection. 99%+ ROC score.


About AnChain.AI



Full Stats from DApp.Review website.

Blockchain data analytics firm providing security, risk, and compliance solutions.

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