GenDetect introduces a novel framework to rapidly generalize detection rules from single observed DeFi exploits, significantly improving resilience against subsequent, similar 'Imitative Attack Cascad…

The paper introduces Dynamic Sharded Federated Learning (DSFL), a secure aggregation framework that significantly reduces communication overhead and enhances update verification for cross-institution…

The paper proposes a novel structural invariant approach, derived from the economic constraints of fraud, that amplifies weak, low-precision signals into highly accurate fraud detections without requi…

The paper identifies a universal, statistically predictable distribution (Mandelbrot) governing LLM outputs, enabling a highly efficient, model-agnostic scoring primitive for provenance and quality as…

LibScan is an automated framework that detects eight categories of smart contract library misuse by combining LLM-based semantic reasoning with rule-based analysis, achieving 85.15% accuracy on real-w…

This paper analyzes the Solana Rug Pull ecosystem by creating a large-scale, manually verified dataset of fraudulent tokens, identifying three key behavioral patterns, and characterizing the resulting…

The paper introduces an LLM-based framework that uses vulnerability-specific prompting and a large-scale dataset to achieve high-precision, scalable detection of multiple smart contract vulnerabilitie…

This paper benchmarks LLMs for smart contract security analysis, concluding that while LLMs show potential, their reliability is limited by lexical bias and requires integration with traditional stati…

SCAFDS introduces a novel, seven-stage graph attention system that models fraud propagation using co-occurrence edge features and generates forensically traceable SAR narratives, significantly improvi…

The paper introduces Deniable Covert Asset Transfer (DCAT), a method that stages asset transfers to appear as ordinary, loss-producing DeFi activities, achieving empirical unobservability on major blo…

The paper demonstrates that large language models (LLMs) exhibit measurable, controllable biases toward specific assets like Bitcoin, identifying an internal feature that can causally shift portfolio…

The paper introduces Bitcoin Smart Accounts (BSA), a novel, trust-minimized protocol enabling native Bitcoin to participate in DeFi while maintaining self-custody of funds.

The paper introduces PhishEye, a fully dynamic self-supervised system that models Ethereum transactions as a heterogeneous temporal attributed multi-graph and uses temporal graph contrastive learning…

The paper analyzes the nascent DeFi investment agent market, finding that while token valuations are high, current deployments are heterogeneous, lack clear autonomous execution, and exhibit poor risk…

The paper empirically analyzes the nascent DeFi investment agent market, finding that while token valuations are high, current deployments lack robust autonomous execution and exhibit poor risk-adjust…

LiquiLM is a novel framework that combines Large Language Models (LLMs) with a Dynamic Co-Attention Network (DCN) to effectively bridge the semantic gap between complex smart contract code and high-le…

The paper introduces the Unicity Execution Layer, a secure, modular component that enables trustless off-chain transactions while guaranteeing double-spending prevention and enhancing user privacy.

The paper proposes a tamper-proof fraud detection system that uses blockchain smart contracts to immutably record ML predictions and workflow executions, addressing the vulnerability of controllable a…

The paper proposes FinSec, a novel four-tier security detection framework, to robustly identify complex financial risks and suspicious dialogue patterns in LLM-powered financial agents, achieving stat…

The paper empirically analyzes the impact of cross-chain interoperability on DeFi lending protocols, finding that bridge volume significantly affects performance but that increased integrations can si…