DeepShield-HFT
Last updated
Last updated
DeepShield-HFT is our specialized security model designed to identify high-frequency trading (HFT) addresses on Ethereum. With an accuracy of over 95%, it helps dapp developers and users alike distinguish between organic trading activity and machine-controlled bots that can disrupt market integrity. Although DeepShield-HFT currently focuses on historical detection, its architecture can be extended to support real-time use cases in the future.
Built on the same foundational architecture as our HODL-C1 model, DeepShield-HFT benefits from the rigorous development and testing methods that have proven successful in our other solutions. This ensures a robust and scalable approach to detecting rapid trading patterns that might otherwise go unnoticed.
DeepShield-HFT excels at flagging addresses that consistently perform multiple trades per minute, indicating the potential presence of bots or automated systems. Its core feature set enables dapp developers to proactively enforce security measures—such as banning or restricting suspicious addresses—while also providing a clearer view of on-chain trading dynamics.
Beyond security, DeepShield-HFT can serve as a valuable on-chain intelligence tool. By pinpointing which tokens are heavily traded by bots, the model helps users and developers gauge how much of a token’s volume is truly organic. This insight supports more transparent market analyses and informed decision-making.
Like HODL-C1, DeepShield-HFT is trained exclusively on Ethereum on-chain data. Although it can be extended to other networks, the current model focuses on analyzing wallet addresses and their transaction histories within the Ethereum ecosystem. All insights are derived from publicly available blockchain records, ensuring transparency and verifiability.
DeepShield-HFT adopts the same classification framework used in HODL-C1, benefiting from advanced feature engineering and robust model training. For labeling, addresses are considered high-frequency traders if their last 3 out of 50 trades each had a holding period of one minute or less. This strict criterion ensures the model accurately captures addresses engaging in rapid, bot-like trading behaviors.
By leveraging proven machine learning techniques and extensive training data, DeepShield-HFT offers a high degree of reliability in flagging suspicious activity. Its flexible architecture allows for future enhancements, including adaptation to other blockchains and potential real-time detection capabilities.
DeepShield-HFT achieves over 95% accuracy in identifying high-frequency trading addresses, a performance level consistent with our broader suite of security and analytics models. This accuracy metric stems from rigorous training and validation, where out-of-sample testing confirms the model’s ability to generalize effectively to new data.
Because the model borrows directly from the HODL-C1 architecture, it also inherits the proven methodologies that minimize overfitting and ensure stable performance. This gives developers confidence in deploying DeepShield-HFT for mission-critical security applications.
DeepShield-HFT is primarily intended to help dapp developers detect and mitigate bot-driven trading activity. By identifying high-frequency trading addresses, developers can implement tailored security measures such as outright bans, restricted permissions, or clear flagging within user interfaces. This proactive approach preserves the integrity of on-chain markets and protects human users from unfair or manipulative trading practices.
In addition, DeepShield-HFT provides valuable market intelligence for anyone interested in understanding the nature of token trading volume. By highlighting the presence of automated activity, it enables more transparent analyses of market liquidity and trends. As the Web3 ecosystem continues to evolve, DeepShield-HFT stands ready to adapt, offering a reliable foundation for both current security needs and emerging real-time detection scenarios.
