Demo: Blockchain Shield - Advanced Threat Detection & Forensic Analysis Platform
Bibliographic record
Abstract
In the rapidly evolving landscape of blockchain technology, security emerges as a paramount concern. This paper introduces an innovative blockchain security threat awareness platform, designed to comprehensively address the multifaceted security challenges within blockchain networks, particularly focusing on Ethereum contracts. Central to the platform is a dual-database architecture, blending a NoSQL database with a graph database, enhancing data management, and enabling intricate transaction network visualizations. The platform's Threat Detection module, utilizing Large Language Models (LLMs) in conjunction with traditional methods, offers a novel approach to identifying and categorizing vulnerabilities in Ethereum smart contracts. Complementing this, the Threat Evidence Collection module provides detailed post-attack analysis, tracing transactions to their sources and evaluating address risks. This module's capabilities extend to producing statistical reports, including the transactional history and risk evaluation of individual addresses. Demonstrated on the Ethereum blockchain, the platform showcases its proficiency in handling complex data, rapid threat detection, and extensive forensic analysis, presenting a robust solution to fortifying blockchain security and offering a proactive defense mechanism for users and developers in the blockchain environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".