Aurora
DeFi Surveillance System to Detect Market Manipulation
Most firms can detect illicit transactions.
Very few can detect market manipulation before settlement.
AURORA is a live institutional DeFi surveillance platform built to identify, classify and report manipulation activity across Ethereum and Solana in real time.
What makes it different is not the dashboard.
It's the operational intelligence behind it:
• 700,000+ data points processed since activation
• Hundreds of thousands of labelled behavioural observations
• Proprietary PULSE-MEV13 detection framework published on SSRN
• MiCA and UK MAR regulatory classification built directly into reporting workflows
• Live production infrastructure and cross-chain surveillance architecture already deployed
An institution can hire engineers.
What it cannot buy back is time.
Replicating a surveillance platform requires years of production calibration, labelled behavioural data, protocol-specific baselines, regulatory mapping, and continuous monitoring infrastructure.
That process has already been completed.
AURORA is not raising capital.
We are exploring selling discussions with exchanges, compliance providers, surveillance vendors, digital asset infrastructure firms, and institutional risk platforms seeking to accelerate their market surveillance capabilities.
Full IP transfer available. FULLY FUNCTIONAL ENGINE WITH DATAS UP FOR SELL.
If this belongs in your stack, my inbox is open.
#DeFi #BlockchainCompliance #Web3Security #MarketSurveillance #CryptoRegulation
The DeFi market is running on steroids, but the Saviour has just arrived.
Since activating the AURORA engine 4 days ago, we’ve moved past the "research" phase and into high-velocity reality. Our PULSE-MEV13 algorithm isn't just a theory anymore—it’s a stress-tested machine.
80,281 Total Requests processed and counting.
Latency: 97% of requests cleared in <49ms.
Intelligence: Live mempool integration to detect market abuse before it settles.
Foundation: Built on the legal intersection of MiCA and UK MARC compliance.
80,281 Total Requests processed and counting.
Latency: 97% of requests cleared in <49ms.
Intelligence: Live mempool integration to detect market abuse before it settles.
Foundation: Built on the legal intersection of MiCA and UK MARC compliance.
I’m currently bridging the gap between an LLM from UWE Bristol and high-frequency technical infrastructure.
The Big Question:
To the builders, the VCs, and the skeptics—looking at these performance marks and this level of traction at the prototype stage:
Is this ready to pass a Pre-Seed round with golden marks?
Drop your thoughts below. Is the market ready for automated, sub-50ms regulatory enforcement?
#RegTech #DeFi #MiCA #BlockchainSurveillance #QantX #UWEBristol #FinTech #Web3Compliance #FounderJourney
While the market is bracing for the July 1st MiCA deadline, we’ve been quietly stress-testing our ingestion engine at the @SETsquared Bristol lounge in the Engine Shed.
In just over three days of live telemetry, AURORA has processed 20,761 high-velocity data points with 100% uptime.
We aren't just tracking price—we are mapping the "Logic Layer" of the market. Our causal inference engine is now identifying risk signals and network alerts at a rate that legacy systems simply weren't built to handle.
The reality: On July 1st, the regulatory for crypto in the EU ends. Firms will either have the surveillance infrastructure to survive, or they won't.
We are moving fast toward our institutional pilot phase and our official full-scale launch on 07/07/26.
Research & Technical Documentation on SSRN:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6319998#
Live Environment: Aurora
https://research-builder--alimulghani5.replit.app
If you are a founder, compliance officer, or investor in the RegTech space and want to see what 'Day Zero' readiness looks like, my DMs are open.
#RegTech #MiCA #DeFi #Web3Compliance #BristolTech #Aurora SETsquared Bristol QantX
Just shipped a major upgrade to Aurora — our real-time DeFi market integrity / manipulation detection platform.
New in this update:
• Deep Investigate Mode
Every flagged anomaly now opens into a forensic breakdown with root-cause analysis, transaction path tracing, wallet/contract attribution, confidence scoring, and severity classification.
• Live Exploit / Manipulation Replays
Aurora now saves replay snapshots of suspicious events in real time, so users can review exactly how an exploit or manipulation unfolded block-by-block.
• Expanded Systemic Risk Engine
Added contagion exposure mapping, liquidity dependency graphs, oracle concentration risk, TVL-at-risk estimates, cascade liquidation probability, and protocol fragility scoring.
• Upgraded Whale Intelligence
Whale tracker now goes beyond large transfers:
– Wallet/entity clustering
– Smart money classification
– Behavioral pattern detection
– Intent inference
– Market impact correlation
– Threat scoring
The goal is to move beyond dashboards that merely show on-chain data and toward infrastructure that explains whysomething is happening and what it likely means.
Still early, but the prototype is beginning to resemble the system outlined in our SSRN framework.
Would love feedback from builders, traders, protocol teams, or anyone working in DeFi/data infrastructure.
#buildinpublic #indiehackers #web3 #defi #startups #fintech
Decentralised finance has recreated many of the functions of traditional capital markets—trading, lending, leverage, liquidity provision—but without the intermediaries, oversight structures, or surveillance infrastructure those markets normally rely on.
As DeFi has grown more sophisticated, so too have the forms of market abuse emerging within it. Manipulation no longer resembles only simple pump-and-dump schemes; it increasingly manifests through transaction ordering exploitation, oracle distortion, engineered liquidation events, flash-loan–enabled attacks, and cross-protocol contagion mechanisms operating at machine speed.
My SSRN research examined how these structural dynamics create a surveillance gap: blockchain data may be public, but raw transparency is not the same as actionable oversight.
That gap is why I built AURORA.
AURORA is an institutional-grade DeFi market surveillance and systemic risk intelligence framework designed to move beyond simple anomaly detection.
Its objective is to:
• Reconstruct protocol state in real time
• Detect coordinated behavioural patterns across wallets/protocols
• Apply causal verification to distinguish genuine manipulation from normal volatility
• Model liquidity stress and systemic fragility
• Forecast contagion and market instability before escalation
• Translate technical detections into regulatory/compliance-relevant outputs
In simple terms:
AURORA is built to answer not just what happened on-chain, but whether it was manipulative, why it mattered, and what systemic risk it creates.
If decentralised finance is to mature into credible financial infrastructure, it will require surveillance systems capable of matching the complexity of the markets themselves.
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It is a significant risk for the DeFi ecosystem when raw transparency is mistaken for actual security because even though every transaction is public the speed and complexity of cross-protocol attacks make it nearly impossible for humans to spot a manipulation event until the liquidity has already been drained.
The real shift in thinking here is the "causal verification" because in a decentralized environment a massive price swing could be a legitimate large trade or a malicious oracle distortion and the ability to mathematically distinguish between the two is what separates basic monitoring from true institutional-grade surveillance.
Since you are focusing on translating technical detections into regulatory-relevant outputs are you finding that the biggest challenge is the "attribution" problem—connecting coordinated wallet behaviors to a single entity in a way that meets the evidentiary standards required by traditional oversight bodies?
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Absolutely—and attribution is one of the hardest layers of the stack. Detecting anomalous on-chain behavior is only the first step; translating that into regulator- or institution-grade intelligence requires probabilistic attribution models that can infer coordinated control across wallets without relying on assumptions that would fail evidentiary scrutiny. In practice, the challenge is bridging pseudonymous behavioral clustering with defensible causal analysis—because identifying manipulation is valuable, but identifying who likely orchestrated it is what turns detection into actionable oversight.
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Bridging pseudonymous behavioral clustering with causal analysis is the only way to turn "on-chain noise" into a legal or institutional argument because simple clustering is rarely enough to prove malicious intent.
Probabilistic attribution models allow for a more realistic approach to DeFi oversight by acknowledging that while you may never have a physical ID, you can demonstrate a level of coordinated control that is statistically impossible to ignore.
I use this same principle of deep attribution in my high-tier PR and media placement work where we use verified data patterns to build undeniable authority for brands on major news outlets.
Do you plan to integrate "off-chain" data points—like social media sentiment or developer activity—into these models to strengthen the probability scores for your attribution reports?
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A lot of people assume DeFi is “fair” because everything is on-chain.
In reality, manipulation hasn’t disappeared — it has evolved.
Through my research (published on SSRN), I found that manipulation in decentralised markets is often harder to detect not because data is hidden, but because it’s fragmented, fast-moving, and technically complex to interpret.
Here are some of the most common patterns:
1. Wash Trading
Artificial volume created by the same entity trading with itself across wallets to simulate liquidity or demand.
2. Pump-and-Dump Schemes
Coordinated buying (often off-chain organised) followed by rapid selling, leaving late participants exposed.
3. Liquidity Manipulation
Actors add/remove liquidity strategically to distort price signals or exploit slippage in AMMs.
4. Front-Running / MEV (Maximal Extractable Value)
Bots or validators reorder transactions to profit from pending trades — effectively exploiting users at the execution layer.
5. Spoofing-like Behaviour (Emerging in DeFi)
While traditional order books are limited in DeFi, similar effects can be created through liquidity positioning and rapid transaction patterns.
The challenge isn’t that these behaviours are invisible.
It’s that:
Data exists across multiple protocols
Wallet identities are pseudonymous
Patterns only emerge across time and interactions
There is no unified surveillance layer
This is the gap AURORA is built to explore.
How AURORA Approaches This Problem
AURORA is an early-stage market surveillance prototype designed to:
• Aggregate on-chain activity across relevant data points
• Identify behavioural patterns rather than isolated transactions
• Flag anomalies that may indicate manipulative activity
• Translate complex blockchain data into interpretable signals
Instead of asking:
“What happened in this transaction?”
AURORA tries to ask:
“What pattern of behaviour does this represent?”
The broader goal is not just detection.
It’s to test whether institutional-style surveillance principles — the kind used in traditional finance — can be adapted to decentralised systems where:
There is no central authority
Participants are pseudonymous
Data is open but unstructured
If DeFi is going to scale into credible financial infrastructure, it will need systems that go beyond transparency and toward interpretable, actionable market intelligence.
DeFi is often described as “transparent” because every transaction is visible on-chain.
But transparency alone does not equal market integrity.
Raw blockchain data may be public, yet identifying manipulative behaviour within that data remains technically complex, fragmented, and inaccessible to most participants.
That creates a major gap:
Markets can be transparent in theory while remaining opaque in practice.
AURORA is my attempt to address that gap.
It is an early-stage DeFi market surveillance engine designed to help surface suspicious on-chain activity and explore how institutional-style surveillance principles might be adapted for decentralised financial systems.
Why build it?
Because if decentralised markets are to mature into credible financial infrastructure, they will need more than transparency—
they will need usable systems for monitoring, accountability, and market integrity.
AURORA began as research into DeFi market manipulation, later published on SSRN, and is now being developed into a live prototype to test whether that research can become practical infrastructure
I started with a question during my research:
If DeFi is “transparent,” why is meaningful market surveillance still so difficult?
That question led me into researching market manipulation in decentralised finance, which I later published on SSRN.
But I didn’t want the work to stay theoretical.
So I began building AURORA — a live prototype that turns that research into practical tooling for DeFi market surveillance.
The idea behind AURORA is to explore whether institutional-style surveillance principles can be adapted to decentralised markets and used to surface suspicious on-chain behaviour in a usable way.
Right now it’s still early-stage and very much in development, but the prototype is live and I’m using it to validate both the technical assumptions and whether the problem is commercially meaningful.
After publishing my research on DeFi market manipulation, I’ve now launched the first live prototype of AURORA — a market surveillance engine designed to detect manipulative behaviour across decentralised finance ecosystems.
What began as academic research is now evolving into applied infrastructure.
AURORA is being built to bridge the gap between:
• fragmented on-chain transparency
• institutional surveillance standards
• the growing need for risk monitoring in digital asset markets
This prototype represents the first step toward translating research into practical surveillance tooling for the next generation of financial infrastructure.
Live Prototype: https://research-builder--alimulghani5.replit.app
Research Paper (SSRN): https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6319998
Currently seeking feedback from:
• DeFi founders
• Quant / trading professionals
• Compliance / regulatory specialists
• Investors interested in market infrastructure
Turning vision into reality.
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