False Negative Rate

The False Negative Rate is the proportion of actual threats (e.g., hacks, scams, exploits) that a security system fails to detect. A high FNR means dangerous transactions pass through undetected, leading to direct financial loss. It is calculated as:

• FNR = False Negatives / (False Negatives + True Positives) In practice, this is often the most costly error for protocols and users, as it represents a complete security failure.

Reducing the False Negative Rate typically increases the False Positive Rate (FPR), where legitimate transactions are incorrectly flagged as malicious. This creates a fundamental security-operations trade-off:

• Aggressive detection: Lower FNR but higher FPR, causing user friction with blocked legitimate tx.
• Permissive detection: Lower FPR but higher FNR, increasing exposure to real attacks. System designers must balance this based on the cost of each error type for their specific application.

A high FNR in wallet security scanners or smart contract auditors has immediate consequences:

• User Asset Loss: Undetected malicious contracts drain wallets.
• Protocol Insolvency: Uncaught exploit vectors lead to protocol hacks (e.g., flash loan attacks, reentrancy).
• Reputational Damage: Persistent failures erode trust in the security provider or underlying platform. Real-world audits prioritize minimizing FNR, sometimes accepting a higher FPR to ensure critical vulnerabilities are never missed.

Security systems use multiple techniques to minimize False Negatives without overwhelming FPR:

• Layered Defense (Defense in Depth): Combine static analysis, runtime monitoring, and human audits.
• Ensemble Methods: Use multiple, diverse detection models and require consensus.
• Continuous Learning: Update threat models and signatures based on new attack vectors post-incident.
• High-Fidelity Oracles: Integrate real-time threat intelligence feeds for known malicious addresses.

FNR is best understood alongside Recall and Precision, key metrics for evaluating detection systems:

• Recall (Sensitivity): 1 - FNR. The ability to find all relevant threats. High recall is critical for security.
• Precision: The proportion of flagged items that are actually threats. High precision minimizes false alarms. A perfect system has high recall (low FNR) and high precision (low FPR), but in practice, improving one often degrades the other.

Consider a system designed to detect predatory MEV bots front-running user transactions:

• False Negative: A harmful sandwich attack is not flagged, allowing the bot to extract value from the user.
• Trade-off: To catch this, the system might also flag complex but legitimate arbitrage transactions as malicious (False Positive), blocking profitable, permissible activity. This illustrates the constant calibration required between protecting users and not stifling legitimate network activity.

The False Negative Rate (FNR) is the probability that a test or monitoring system fails to detect a true positive event. It is calculated as FNR = False Negatives / (False Negatives + True Positives). In blockchain, this often refers to a security oracle or smart contract audit tool missing a genuine hack, exploit, or fraudulent transaction.

FNR exists in a direct trade-off with the False Positive Rate (FPR). Increasing a system's sensitivity to catch more threats (lowering FNR) typically increases the number of false alarms (raising FPR). Optimizing this balance is key for practical security systems to avoid alert fatigue while maintaining protection.

• High FNR, Low FPR: Misses many real attacks but has few false alarms.
• Low FNR, High FPR: Catches most attacks but generates many incorrect alerts.

A high FNR in blockchain monitoring has severe financial consequences:

• Slashing Risk: In Proof-of-Stake networks, a validator monitoring service with a high FNR might fail to detect an upcoming slashing condition, leading to loss of staked funds.
• Oracle Failure: A price feed oracle with a high FNR on anomalous data could provide stale prices during a market crash, causing massive liquidations.
• Smart Contract Wallets: Security modules that fail to detect malicious transaction patterns (high FNR) can lead to irreversible fund theft.

Systems lower their FNR through several technical approaches:

• Ensemble Methods: Combining multiple, diverse detection models (e.g., heuristic rules + machine learning) to cover blind spots.
• Continuous Retraining: Updating ML models with new attack signatures and patterns as they emerge on-chain.
• Multi-Source Data: Correlating on-chain data with off-chain intelligence (social sentiment, dark web monitoring) to identify threats a single source might miss.

In machine learning and data science, Recall (or Sensitivity) is the direct inverse of the False Negative Rate. It is defined as Recall = True Positives / (True Positives + False Negatives). Therefore, Recall = 1 - FNR. A system with 95% Recall has a 5% False Negative Rate. This metric is prioritized in applications where missing a positive case (like a hack) is costlier than raising a false alarm.

Consider a system designed to detect Maximal Extractable Value (MEV) exploitation like sandwich attacks:

• True Positive: Correctly flags a transaction bundle as a malicious sandwich attack.
• False Negative: Fails to identify a sophisticated, novel sandwich attack, allowing it to proceed. This FNR represents a direct loss for the victim trader. The system's effectiveness is measured by its ability to minimize this FNR against evolving MEV strategies.