This paper analyzes Bitcoin's new V2 P2P transport protocol, demonstrating that while it fixes known vulnerabilities, attackers can still execute conceptual attacks like message identification via pay…

This paper proposes a two-stage machine learning system that accurately detects I2P traffic and subsequently classifies it as data exfiltration or legitimate activity, achieving high accuracy in both…

UniDetect is a novel LLM-driven method that detects cross-chain cryptocurrency fraud by generating generalized transaction summaries, significantly outperforming existing detection techniques across m…

The paper proposes BitSov, an eight-layer, Bitcoin-native architectural framework designed to build sovereign internet infrastructure by composing existing decentralized technologies.

This paper critically re-evaluates the use of Graph Neural Networks (GNNs) for Bitcoin fraud detection, demonstrating that under strict, leakage-free temporal evaluation, simple feature-only models si…

The paper investigates using Convolutional Neural Networks (CNNs) for deanonymizing I2P traffic patterns, but concludes that the proposed methods do not compromise the network's anonymity guarantees.

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 robust semi-supervised temporal learning framework for cloud intrusion detection that explicitly handles adversarial contamination and temporal drift in unlabeled network traffic,…

EdgeDetect is a communication-efficient and privacy-preserving federated intrusion detection system that uses gradient binarization and homomorphic encryption to significantly reduce bandwidth usage w…

The paper introduces Ark, a novel Bitcoin-compatible commit-chain that enables offchain transaction batching of virtual UTXOs (VTXOs) with a constant onchain footprint, solving scalability issues with…

VeriX-Anon is a multi-layered framework that provides mathematically verifiable assurance that outsourced data anonymization (k-anonymization) was executed correctly, achieving high detection rates ag…

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 proposes a dual-regime architecture combining Bernoulli CUSUM and asymmetric scoring to significantly improve trust fraud detection in sparse rating networks, achieving superior performance…

MambaNetBurst introduces a compact, tokenizer-free byte-level classifier using a Mamba-2 backbone to achieve strong network traffic classification without requiring pre-training or complex data prepro…

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 introduces the quotient semivalue mechanism to provide fair data attribution that is resistant to contributors manipulating their reported identities by splitting or duplicating data.

Pepper is a novel, high-bandwidth anonymous broadcast protocol that achieves cryptographic sender anonymity and significantly improves messaging throughput compared to existing state-of-the-art system…

This study demonstrates that the publicly distributed firmware of ASIC cryptocurrency miners constitutes a primary and sufficient attack surface, allowing attackers to reconstruct internal architectur…

The paper proposes n-VM, a novel Layer-1 architecture that unifies multiple heterogeneous virtual machines (VMs) onto a shared consensus and state layer, solving cross-chain fragmentation issues.

This paper introduces an active traffic analysis method (NATA) and a deep learning framework (BM-Net) to demonstrate that bandwidth perturbations can be used by an adversary to correlate and de-anonym…