Billions of dollars are lost every year in DeFi platforms by transactions exploiting business logic or accounting vulnerabilities. Existing defenses focus on static code analysis, public mempool screening, attacker contract detection, or trusted off-chain monitors, none of which prevents exploits submitted through private relays or malicious contracts that execute within the same block. We present the first decentralized, fully on-chain learning framework that: (i) performs gas-prohibitive computation on Layer-2 to reduce cost, (ii) propagates verified model updates to Layer-1, and (iii) enables gas-bounded, low-latency inference inside smart contracts. A novel Proof-of-Improvement (PoIm) protocol governs the training process and verifies each decentralized micro update as a self-verifying training transaction. Updates are accepted by PoIm only if they demonstrably improve at least one core metric (e.g., accuracy, F1-score, precision, or recall) on a public benchmark without degrading any of the other core metrics, while adversarial proposals get financially penalized through an adaptable test set for evolving threats. We develop quantization and loop-unrolling techniques that enable inference for logistic regression, SVM, MLPs, CNNs, and gated RNNs (with support for formally verified decision tree inference) within the Ethereum block gas limit, while remaining bit-exact to their off-chain counterparts, formally proven in Z3. We curate 298 unique real-world exploits (2020 - 2025) with 402 exploit transactions across eight EVM chains, collectively responsible for $3.74 B in losses. We demonstrate that on-chain ML governed by PoIm detects previously unseen attacks with over 97% attack detection accuracy and 82.0% F1. A single inference, such as one made via an external call, typically incurs zero cost. Fully on-chain inference consumes 57,603 gas (approximate to $0.18) for linear models, 143,647 gas (approximate to $0.49) for CNN(F2, K1), and 506,397 gas (approximate to $1.77) for CNN(F8, K4) on L1 (e.g., Ethereum). Our results show that practical and continually evolving DeFi defenses can be embedded directly in protocol logic without trusted guardians, and our solution achieves highly cost-effective protection while filling a critical gap between vulnerability scanners and real-time transaction screening. 2012 ACM Subject Classification Security and privacy -> Network security; Security and privacy -> Distributed systems security; Security and privacy -> Security protocols
Perpetual futures - swap contracts without expiration dates - are the most widely traded derivatives in cryptocurrency markets. Traditional perpetual trading relies on order books, which require substantial bilateral liquidity and face challenges in high-volatility environments. In this paper, we introduce pvpAMM, a peer-to-peer perpetual trading protocol based on automated market maker (AMM) principles. The protocol enables efficient settlement of long-short mismatched markets and drives positions toward equilibrium: when the minority leveraged side wins, their returns are amplified compared to conventional perpetual contracts, while the opposite occurs when the majority side prevails. We also propose arbitrage mechanisms to maintain economic equilibrium within the pvpAMM system. By incorporating liquidity providers (LPs), the protocol aligns more closely with traditional order book trading. Numerical experiments validate our theoretical findings. 2012 ACM Subject Classification Applied computing -> Digital cash; Mathematics of computing -> Stochastic processes
Recent years have seen a rising interest in cloud-hosted financial exchanges. While the public cloud platforms promise a cost-effective and more accessible option to traders, unfortunately, achieving fairness in cloud environments is challenging due to non-deterministic network latencies and execution times. This work presents Cuttlefish, a fair-by-design cloud execution environment for algorithmic trading. The idea behind Cuttlefish is the efficient and robust mapping of real operations to a novel formulation of "virtual time". With it, Cuttlefish abstracts out the variances of the underlying network communication and computation hardware. Our implementation and evaluation not only validate the practicality of Cuttlefish, but also show its operational efficiency on public cloud platforms. 2012 ACM Subject Classification Networks -> Cloud computing; Networks -> Network dynamics; Applied computing -> Online auctions
