EDCCS, a team of four Chinese university students, qualified for the SCAN 2026 blockchain tracking competition finals scheduled for September 28 in Seoul. The team from three different universities—Qinhuangdao Northeastern University, Henan University of Economics and Law, and Chengdu University of Information Technology—advanced from preliminaries that included 416 teams and 754 participants. Their qualification marks their first participation in a blockchain investigation competition, achieved by adapting methodologies from traditional cybersecurity CTF (Capture The Flag) competitions. SCAN 2026, organized by digital asset information company Diasset with global blockchain data platform Chainalysis as CTF partner, aims to enhance digital asset cybersecurity and investigation capabilities.
EDCCS Forms Team from Three Chinese Universities
EDCCS united students from separate institutions through shared interests in cybersecurity and security competitions. Two members attend Qinhuangdao Northeastern University, one attends Henan University of Economics and Law, and one attends Chengdu University of Information Technology. Team members possess overlapping technical backgrounds rather than distinct role divisions. Their experience derives from CTF competitions, vulnerability research, tool development, and engineering implementation. The team approaches unfamiliar technical problems by rapidly identifying required information and capabilities, then creating or modifying tools to streamline problem-solving processes.
Team Qualifies for Finals in First Blockchain Competition
EDCCS expressed surprise at reaching the finals given their first-time participation in blockchain investigation competitions. The qualification validated their ability to apply CTF-developed capabilities—rapid learning in unfamiliar environments, information extraction, task automation, hypothesis formation and verification—to on-chain investigation. The team identified no single overwhelmingly difficult problem during preliminaries. Instead, unfamiliarity with the blockchain investigation field itself presented the primary challenge. Data sources, transaction structures, terminology, and investigation methods initially appeared unfamiliar.
EDCCS Applies CTF Methodology to Blockchain Investigation
The team responded to unfamiliarity using approaches identical to encountering new CTF categories. They first determined what each problem specifically asked, organized information needed to derive answers, located appropriate data sources and tools, then automated investigation and verification processes. When one approach failed to provide sufficient evidence, they changed search directions, utilized alternative sources, or verified conclusions through different pathways. This approach prioritized rapid adaptation and iterative verification over reliance on prior experience with specific blockchain investigation platforms.
Team Deploys AI-Based Automation for Data Analysis
AI-based automation occupied significant portions of the team's workflow. EDCCS utilized AI tools not merely for answering individual questions but as components of broader investigation and analysis workflows. The team configured AI tools to search relevant information, access available data sources, query APIs, analyze returned results, derive possible explanations, and cross-verify candidate answers when presented with problem situations. Before preliminaries, the team secured advance access to multiple APIs and data sources anticipated as useful during competition. This preparation expanded capabilities available to AI-based workflows when information unavailable through simple web searches became necessary.
Team roles focused primarily on constructing and adjusting workflows—deciding which tools and information to utilize, improving processes when results lacked sufficient reliability, and directly verifying critical conclusions when required. The team automated repetitive search, query, parsing, and verification tasks rather than performing identical operations manually. They treated AI as an automation layer combining search, tool usage, data analysis, and verification into unified problem-solving processes rather than simple chatbots.
EDCCS Identifies Rapid Adaptation as Core Strength
EDCCS considers their greatest strength the ability to rapidly adapt to unfamiliar technical problems and transform problem-solving processes into engineering workflows. CTF and vulnerability research experience taught the team to operate in situations where technology, intended solution methods, and even ultimately useful tools remain initially unknown. They learned to acquire necessary knowledge during problem-solving processes and quickly pivot directions when needed. The team asks not only "how to solve this problem manually" but also "how much of this process can tools solve instead." This approach includes information gathering, API queries, data processing, hypothesis generation, and answer verification. Reducing repetitive manual work allows greater attention to overall solution strategy correctness.
Team Refines AI Workflow for Finals Preparation
EDCCS plans no complete overhaul of approaches proven effective during preliminaries. Instead, preparation focuses on refining methodology for greater reliability and finals suitability. One direction involves improving AI-based workflows—how to connect different tools, APIs, and data sources, and how to cross-verify results from multiple sources. The team also prepares for situations where specific APIs or information sources become unavailable, incomplete, or inconsistent. Establishing alternative methods for obtaining and verifying information strengthens workflow robustness under time constraints. Simultaneously, members familiarize themselves further with blockchain transaction structures, common fund flow patterns, and investigation methods. This preparation aims to reduce basic domain knowledge requiring acquisition during competition.
Regarding potential prize winnings, the team stated victory itself holds greater significance than prize money. As first-time participants in this competition type, strong performance against global teams would provide valuable experience and motivation for continued exploration of the field. If victorious, the team intends to allocate portions of prize money toward future security research, CTF participation, technical infrastructure, and tool development for upcoming projects.
FAQ
What methodology did EDCCS use to qualify for SCAN 2026 finals?
EDCCS applied traditional CTF (Capture The Flag) competition methodologies to blockchain investigation despite first-time participation in this field. The team identified problem requirements, organized necessary information, located appropriate data sources and tools, then automated investigation and verification processes. When one approach proved insufficient, they changed search directions or utilized alternative sources for verification.
How did EDCCS utilize AI tools during SCAN 2026 preliminaries?
The team deployed AI-based automation as components of broader investigation workflows rather than simple question-answering tools. AI tools searched relevant information, accessed data sources, queried APIs, analyzed results, derived explanations, and cross-verified candidate answers. EDCCS secured advance access to multiple APIs and data sources before preliminaries, expanding capabilities available to AI-based workflows beyond simple web searches.
What preparations is EDCCS making for the September 28 finals?
EDCCS focuses on refining AI-based workflows by improving connections between different tools, APIs, and data sources, and enhancing cross-verification of results from multiple sources. The team prepares alternative methods for obtaining and verifying information when specific sources become unavailable or inconsistent. Members also familiarize themselves further with blockchain transaction structures, fund flow patterns, and investigation methods to reduce basic domain knowledge requiring acquisition during competition.
