Lazarus High Risk Bybit Hacking Investigation [CLADIOUS-[BYBIT_HACKER_LAZARUS_ITER]-2025-001] - Wallet Analysis Report - Very High Risk - 0x323c...d58a
Published
14 Jul 2025
23 views
Wallet Name
Analysis Target Wallet (CLADIOUS-[BYBIT_HACKER_LAZARUS_ITER]-2025-001) - 0x323c...d58a
Overview
Project Scope
Analysis of wallet 0x323ccaf2d7622f3efdb3377a9c76670eb217d58a - Lazarus High Risk Bybit Hacking Investigation
Suspicious Wallet Hash
0x323ccaf2d7622f3efdb3377a9c76670eb217d58a
This is the primary wallet address being investigated in this report.
Methodology
Research Methodology
Automated Analysis Methodology for Wallet 0x323ccaf2d7622f3efdb3377a9c76670eb217d58a
1. Data Collection
- Automated transaction retrieval from blockchain
- Historical transaction pattern analysis
- Network connection mapping
2. Analysis Algorithms
- Multi-algorithm approach using 1 detection methods
- Statistical anomaly detection
- Behavioral pattern analysis
- Network-based risk assessment
3. Risk Scoring
- Weighted risk factor calculation
- Multi-dimensional analysis
- Historical comparison baseline
- Real-time pattern detection
4. Report Generation
- Automated findings compilation
- Risk level determination
- Recommendation synthesis
- Compliance-ready documentation
Data Collection
Data Collection Process for 0x323ccaf2d7622f3efdb3377a9c76670eb217d58a
1. Blockchain Data Retrieval
- Retrieved 1 analysis data points
- Collected complete transaction history
- Gathered network connection data
2. Analysis Processing
- Applied multiple detection algorithms
- Performed statistical analysis
- Generated risk indicators
- Created behavioral profiles
3. Quality Assurance
- Data validation checks
- Algorithm consistency verification
- Result accuracy confirmation
Data Preprocessing
Data Preprocessing Steps:
1. Data Cleaning
- Removed duplicate transactions
- Standardized timestamp formats
- Validated transaction data integrity
2. Feature Engineering
- Created time-based features
- Calculated statistical metrics
- Generated network features
3. Normalization
- Applied consistent scaling
- Handled missing values
- Optimized for analysis algorithms
Design Pattern
No design pattern information is available for this report.
Analysis
General Analysis Summary for 0x323ccaf2d7622f3efdb3377a9c76670eb217d58a
Risk Level: Very High Risk Score: 100/100 Total Issues Identified: 4 Suspicious Transactions: 1
Key Findings: - Automated analysis detected 1 suspicious transactions - Risk assessment indicates very high risk level - 4 total suspicious patterns identified across all algorithms - Standardized risk score: 100/100
Analysis Confidence: High (automated multi-algorithm approach) Recommendation: Immediate investigation required
No suspicious patterns detected.
0x891fdbac4dcbc877291b8544146bbba22d5f749e7f50e3e450f2b4b02444a56e: High frequency transactions (less than 1 minute interval)
Summary
Total Suspicious Transactions
2
Average Risk Score
89.5
Top Tags
No tags
Suspicious Transactions
| Transaction Hash | Network | Risk Score | Risk Factors | Tags |
|---|---|---|---|---|
0x891fdba…
|
— |
100
High
|
Transaction involves DeFi exploit address: Bybit Exploiter 27
Large transaction amount
Receives funds from exploit address: 0x52207e...
High frequency transactions (less than 1 minute interval)
Low transaction fee
Part of suspicious wallet community
Related to 21 high-risk transactions (highest score: 100)
Very short time between transactions
|
No tags
|
Showing 1 to 10 of 0 transactions
Advanced Analysis Findings
No Local Outlier Factor analysis data is available for this report.
No wallet community detection data is available for this report.
No transaction layering pattern data is available for this report.
No address clustering data is available for this report.
No sanctioned address connection data is available for this report.
Suspicious Activities
Suspicious Activities Summary:
High Risk Activities: 1
Medium Risk Activities: 0
Total Flagged Transactions: 1
Pattern Categories:
- Network-based anomalies
- Behavioral inconsistencies
- Statistical outliers
- Temporal irregularities
Automated Detection Results:
- Algorithm coverage: Comprehensive
- Detection confidence: High
- Risk classification: Validated
Conclusions & Recommendations
Conclusions
Analysis Conclusions for 0x323ccaf2d7622f3efdb3377a9c76670eb217d58a:
1. Risk Assessment
- Overall Risk Level: Very High
- Standardized Risk Score: 100/100
- Average Transaction Risk Score: 100.00
- Total Suspicious Patterns: 1
2. Key Findings
- Automated analysis completed successfully
- Multiple detection algorithms applied
- Comprehensive risk evaluation performed
- Standardized scoring methodology applied (score: 100/100)
3. Confidence Level
- Analysis Quality: High
- Data Coverage: Complete
- Algorithm Performance: Validated
4. Summary
The automated analysis has identified significant concerns.
Immediate action recommended.
Recommendations
Immediate Action Recommendations:
1. Priority Actions
- Escalate to compliance team immediately
- Implement enhanced monitoring
- Consider transaction restrictions
- Document all findings
2. Investigation Requirements
- Detailed transaction review required
- Source of funds investigation
- Enhanced due diligence protocols
- Regular monitoring updates
3. Compliance Measures
- File suspicious activity reports if required
- Implement know-your-customer procedures
- Apply enhanced monitoring protocols
- Document risk mitigation measures
Severity Assessment
Very High
Appendices & References
Appendices
Appendix A: Automated Analysis Results
Appendix B: Algorithm Details and Methodology
Appendix C: Risk Assessment Matrix
Appendix D: Transaction Pattern Analysis
Appendix E: Network Connection Analysis
Appendix F: Case Reference Documentation - CLADIOUS-[BYBIT_HACKER_LAZARUS_ITER]-2025-001
Appendix G: Investigation Team Notes - Cladious Forensics Team
References
1. Blockchain Analysis Framework - Cladious Platform
2. Risk Assessment Guidelines - Financial Action Task Force (FATF)
3. Automated Analysis Documentation - Internal Methodology
Report Information
Author
Cladious Auto
Published Date
July 14, 2025
Views
23
Likes
0