This paper synthesizes the emerging field of blockchain and AI for securing intelligent networks by providing a comprehensive taxonomy, integration patterns, and an evaluation blueprint.

This paper proposes a comprehensive framework utilizing AI and machine learning to enhance cybersecurity and mitigate fraud risks in the emerging field of cardless artificial intelligence banking.

This paper proposes a unified, lifecycle-centric framework and a detailed taxonomy to survey and analyze novel, finance-specific attack surfaces and vulnerabilities in AI systems used within the finan…

The paper introduces the Lean-Agent Protocol, a formal verification platform that uses Lean 4 theorem proving to ensure agentic AI actions in finance are mathematically compliant with complex regulati…

TITAN-FedAnil+ is a trust-based, adaptive blockchain federated learning framework designed for resource-constrained intelligent enterprises, significantly improving robustness and resource efficiency.

This paper proposes and evaluates the integration of Federated Learning and blockchain technology over cloud-edge infrastructure to enhance data privacy and security for decentralized AI applications.

The paper systematizes the interaction between autonomous AI agents and blockchain platforms using a bidirectional trust framework, identifying significant gaps in current standards and proposing a ta…

This paper investigates the practical barriers preventing the trustworthy deployment of AI-driven Cyber Threat Intelligence (CTI) in the highly regulated financial sector, identifying four key socio-t…

The paper introduces MEV non-interference, a formal security notion, to ensure that composing new smart contracts in DeFi does not increase the maximal extractable value, thereby providing a formal fo…

This paper proposes ALBank, a decentralized banking application built on the Ethereum blockchain and smart contracts, demonstrating that this integration effectively enhances security, transparency, a…

This paper proposes a self-adaptive block creation process for blockchain systems that automatically optimizes configurations to reduce transaction latency by predicting performance based on workload…

The paper proposes a novel nine-dimension risk assessment framework for institutional DeFi adoption, significantly enhancing existing methodologies by incorporating novel dimensions like composability…

The paper proposes a novel decentralized framework that uses blockchain and Multi-task Peer Prediction to incentivize and manage the computationally intensive process of Federated Learning.

The paper proposes AdaBFL, a multi-layer defensive adaptive aggregation method that enhances Byzantine-robust federated learning by adaptively adjusting defense weights to counter complex poisoning at…

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 introduces LOCARD, an agentic framework that models blockchain forensics as a sequential decision-making process, demonstrating its effectiveness in complex cross-chain transaction tracing.

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 proposes a comprehensive, dual-layer architectural framework for AI identification and traceability, ensuring continuous accountability and regulatory oversight throughout the entire lifecyc…

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 a formal Risk-Cost Model (RCM) to provide an economically grounded and mathematically rigorous framework for adaptive authentication in high-stakes financial systems.