Our AI Detects Your AI — Revealing the Secret World of Blockchain Bots (Part 2: TRON)

Key discovery:

Blockchain bots contributed to 30.7% of the 62000+ unique accounts, accounting for over $270 million USD of transaction volume in the top 10 gambling DApps on the TRON network in 2019Q1. We also identified a new bot strategy that is unique to Tron, likely due to its DPOS consensus implementation.


1. AnChain.AI is NOT affiliated with TRON, EOS, or Ethereum foundation.


AnChain.AI leverages artificial intelligence (AI) to reveal the prevalence of bot activity within the Tron blockchain DApp world. To date, this analysis is the largest scale study of bot presence within the Tron DApp ecosystem.

Key Insights

Figures 1–3 : A visualization of bot account and transaction metrics across the top 10 gambling DApps in 2019Q1
  1. The top 3 Tron DApps observed a relatively small percentage of bot activities.
  2. We observe substantially higher bot presence among the top 4 to 10 DApps. DApp 4, 7,9, and 10 in particular are dominated by bot presence: over 70% of their accounts are bot accounts.
  3. “Group Bot” is a unique bot strategy we discovered in Tron blockchain: a group of bots that, on an individual basis, exhibit a low activity level but which tightly coordinate their transactions dominate DApps 4, 9 and 10. DApp 9, for example, is comprised of over 70% of bot accounts, while accounting for less than 10% of total transactions. These bot clusters are incredibly difficult to detect, but can be caught with a sufficiently thorough methodology.

Why Does Bot Detection Matter?

Blockchain technology and its associated financial markets are still in their relative infancy, and are subject to a great deal of speculative volatility. New blockchain protocols and currencies are constantly being created within this space, and more often than not, user activity, transaction volume, daily volume, and other growth metrics are used as proxies for how well they are performing.

Figure 4: Bitwise’s report shows trading bots found in several crypto exchanges [2].

Striking New Bot Behaviors Discovered in Tron Blockchain

Per TokenInsight, there are 117 active gambling DApps on Tron that have dominated 68% of Tron DApp accounts. Furthermore, the gambling DApp category has witnessed strong and consistent growth. We believe our research has captured a representative sample of the Tron ecosystem.

Figure 5. Gambling DApps have attracted 68% of the active Tron DApps accounts in 2019Q1. Tron DApp trending in 2019Q1. Gambling DApps dominate and experience the most rapid growth, vs. categories such as exchanges, games, etc., Per TokenInsight.
Figure 6. An illustration of how a typical hyperactive bot transacts with the DApp from 3/12/2019 to 4/1/2019
Figure 7. Youtube tutorial showing how to use macros to auto-bet on Tronbet DApp.
Figure 8. This account would not ordinarily be any cause for alarm.
Figure 9. When clustered, the coordination becomes clear. They activate together, largely in the same order, and at the same time intervals.
  • Go dormant at the same time.
  • Behave almost identically when active.
Figure 10. The tight coordination in the activity, dormancy, and resurrection of these accounts is unmistakable.
Figure 11. The transaction intervals are highly regular within accounts and between accounts.

The Reason Behind the New Bot Strategy on TRON

It is highly unusual to find a completely novel bot strategy that was seemingly entirely absent from our EOS analysis, but we hypothesize that its presence on TRON can be attributed in large part to the implementation of the DPOS consensus, per this article by SVK Crypto firm in UK :

Figure 12. The key differences between EOS and TRON. Note, especially, the free accounts and transaction fees.

Bad Bots vs Good Bots in the Blockchain World

Not all bots are created equal. There are good bots and bad bots in the blockchain world, just like in the Internet world.

  • Increasing liquidity of DApp utility tokens. Most DApps are backed by tokenomics, meaning they have a token crypto asset that is actively traded across various crypto exchanges. If there is no trading activity for this token and the exchange where it is listed has an illiquid order book, the token asset will likely face sell-side pressure and decrease in value. A very common use case for bots is employing them as a tool for market making to ultimately increase liquidity of the tokens and prop up, or grow, asset values.
  • Earning profits on the payout dividends. Most DApps pay generous dividends, in coins or tokens, to incentivize players to play their DApps (mostly gambling related).
  • Sabotaging competitors by congesting the DApp, similar to a Denial-of- Service (DoS) attack on the Internet.
  • Launching BAPT (Blockchain Advanced Persistent Threat) attacks on targeted vulnerable DApps. [3]
  • Interacting with human players. For example, DApp players cannot always find sufficient human players to interact with, so a bot player will be deployed to fill the void.

What can we do as an Industry?

It is evident that the DApp, as the most relevant application of blockchain as of this writing, is currently being heavily influenced by bots; something that the industry has a responsibility to understand and address.

  • Protocol teams have all of the available data for each of the DApps within their protocol, so they ought to lead the charge with transparency and re-focus on driving organic growth which will benefit themselves and the industry in the long-term.
  • Invest in good bots that help improve product quality and increase liquidity.
  • Do not cheat by building or encouraging bad bots.
  • Defend against malicious bots, such as BAPT (Blockchain APT hackers)[3].

Deep Dive into the New Bot Detection Machine Learning Model

In this research, we leverage the existing EOS bot detection Deep Learning model, ensembling with a new model to detect the newly discovered “Group bot” army. The Machine Learning Methodology is as follows:

Figure 13. Account Distribution by Activity Level
  • Transaction level. The higher the transaction level, the heavier the transaction number.
Figure 14: A silhouette plot for the various clusters
Figure 15. A Three Dimensional Visualization of Account Clusters
Figure 16. A Close-Up of the Three Dimensional Visualization of Account Clusters
Figure 17. Minimal overlap in the detection capabilities of our two models.

Coming Next :

The AnChain.AI engineering team is working hard towards sharing more blockchain bot research with the public and the blockchain community at large. Follow us on social media for the latest updates on our research efforts.

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