A Linux desktop application that ingests bulk Bitcoin transaction files, correlates network data with blockchain wallets using machine learning, and displays suspicious activity on a dashboard.
National Technical Research Organisation (NTRO) · Software
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Decomposed from what the description asks for. Nothing added.
Data Ingestion Engine
A module that parses bulk metadata in CSV, JSON, or XML formats and integrates open-source Geo IP databases.
Graph Correlation Builder
A component that builds an entity graph linking IP addresses, network ports, and wallet transactions together.
AI Anomaly Detection Model
A working machine learning model that detects suspicious patterns and clusters entities without relying solely on static rules.
Explainable Alert Generator
A system that produces a ranked list of flagged transactions complete with confidence scores and reasoning.
Visualization Dashboard
A simple user interface that displays the flagged entities, alerts, and link-analysis connections.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
The evaluators will check if the solution runs completely offline on a Linux platform and whether it uses an actual machine learning model rather than just static rules. They will also review the technical write-up covering your approach, model choice, and how you explain the generated alerts.
A jury can still ask about these. Decide them deliberately rather than by accident.
Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.
Exact size and schema structure of the bulk input dataset?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific machine learning algorithms or anomaly types required?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Performance or processing speed benchmarks for bulk ingestion?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
Each one is quoted from a gap in the brief, not a guess about your team.
No dataset provided
The description depends on real data, and the organisers have not attached a dataset link.
No measurable target
The description asks for a working model and ranked alerts, but never specifies a required accuracy percentage, false positive rate, or processing speed target.
What the organisers attached, and what the brief assumes you can get.
Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.
Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.
Who has this problem, what already exists, and what you would have to find out.
The brief asks for data ingestion engine. How would you build that?
Decoded from SIH26146 itself — A module that parses bulk metadata in CSV, JSON, or XML formats and integrates open-source Geo IP databases. The brief asks for it by name.
The brief asks for graph correlation builder. How would you build that?
Decoded from SIH26146 itself — A component that builds an entity graph linking IP addresses, network ports, and wallet transactions together. The brief asks for it by name.
The brief asks for ai anomaly detection model. How would you build that?
Decoded from SIH26146 itself — A working machine learning model that detects suspicious patterns and clusters entities without relying solely on static rules. The brief asks for it by name.
The brief asks for explainable alert generator. How would you build that?
Decoded from SIH26146 itself — A system that produces a ranked list of flagged transactions complete with confidence scores and reasoning. The brief asks for it by name.
The brief asks for visualization dashboard. How would you build that?
Decoded from SIH26146 itself — A simple user interface that displays the flagged entities, alerts, and link-analysis connections. The brief asks for it by name.
Where does your data come from — a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the evaluators will check if the solution runs completely offline on a Linux platform and whether it uses an actual machine learning model rather than just static rules.
This is the evaluator read for your problem statement, decoded from the brief's own wording.
Show me this working: they will also review the technical write-up covering your approach, model choice, and how you explain the generated alerts.
This is the evaluator read for your problem statement, decoded from the brief's own wording.