Scoring System

BlockGuard uses a sophisticated scoring system to evaluate the risk level of blockchain addresses, tokens, and wallets. Our scoring methodology combines multiple factors to provide a comprehensive risk assessment that helps users make informed decisions.

Token & Address Risk Score Scale

For tokens and addresses, BlockGuard uses a 0.0-1.0 scoring system where higher scores indicate higher risk:

0.0-0.5 Low Risk - Generally safe with minimal suspicious activity

0.51-0.79 Medium Risk - Exercise caution, some suspicious transactions detected

0.8-1.0 High Risk - Significant suspicious activity detected, proceed with extreme caution

Wallet Health Score Scale

BlockGuard uses a 0-100 scoring system where higher scores indicate lower risk:

80-100 Low Risk - Generally safe with minimal suspicious activity

50-79 Medium Risk - Exercise caution, some suspicious transactions detected

0-49 High Risk - Significant suspicious activity detected, proceed with extreme caution

Scoring Factors

Our risk assessment algorithm considers multiple factors when calculating scores:

For Addresses

Interaction with known malicious addresses
Reported incidents (scams, phishing, hacks)
Transaction patterns and anomalies
Age and activity history
Connection to sanctioned entities
Community reports and feedback

For Tokens

Suspicious Name / Symbol detection
Imposture Detection
Burn Address Transfer Analysis
Whitelist System

For Wallets

Number of interactions with flagged addresses
Percentage of suspicious transactions
Presence of known spam tokens
Transaction volume and patterns
Wallet age and activity consistency

Scoring Methodology

Our scoring system employs a weighted approach where different risk factors contribute differently to the final score:

Machine Learning Analysis

We use machine learning models trained on millions of blockchain transactions to identify patterns associated with malicious activities. These models continuously improve as new data becomes available.

Network Analysis

Our system analyzes the network of transactions to identify clusters of related addresses and their collective risk profile. This helps identify sophisticated scams that operate across multiple addresses.

Temporal Analysis

We consider how patterns evolve over time, as many malicious activities have distinct temporal signatures. Recent suspicious activities generally have a higher impact on the risk score than older ones.

Community Intelligence

We incorporate verified reports from users and trusted partners to enhance our risk detection capabilities. This crowdsourced intelligence helps identify new threats faster.

Interpreting Scores

While our scoring system provides valuable guidance, it's important to understand its limitations and proper use:

Not Absolute: Risk scores provide guidance but should not be the sole factor in decision-making. Always conduct your own research.

Context Matters: A low score doesn't always indicate malicious intent. New or low-activity addresses may have lower scores due to limited history.

Evolving Threats: Scammers continuously adapt their techniques. A high score today doesn't guarantee safety tomorrow.

False Positives: Legitimate addresses may occasionally receive lower scores due to unusual but legitimate transaction patterns.