Malware Forensics

Comprehensive malware forensics skill for analyzing malicious software samples. Enables static and dynamic analysis, extraction of indicators of compromise, attribution research, and documentation of malware capabilities for incident response and threat intelligence.

Capabilities

• Static Analysis: Analyze malware without execution (strings, headers, imports)
• PE Analysis: Parse Windows executables, DLLs, and drivers
• Document Analysis: Analyze malicious Office documents and PDFs
• Script Analysis: Analyze malicious scripts (PowerShell, VBA, JavaScript)
• IOC Extraction: Extract IPs, domains, URLs, hashes, and other indicators
• YARA Scanning: Scan samples with YARA rules for identification
• String Analysis: Extract and categorize strings from samples
• Behavior Analysis: Document observed malware behavior
• Unpacking Support: Identify and document packed samples
• Attribution Analysis: Link samples to threat actors or campaigns

Quick Start

from malware_forensics import MalwareAnalyzer, PEAnalyzer, IOCExtractor

# Analyze sample
analyzer = MalwareAnalyzer("/samples/malware.exe")
report = analyzer.analyze()

# Extract IOCs
extractor = IOCExtractor("/samples/malware.exe")
iocs = extractor.extract_all()

# Scan with YARA
matches = analyzer.yara_scan("/rules/malware.yar")

Usage

Task 1: PE File Analysis

Input: Windows executable (EXE, DLL, SYS)

Process:

1. Parse PE headers
2. Analyze imports/exports
3. Check for anomalies
4. Extract resources
5. Calculate hashes

Output: PE analysis report

Example:

from malware_forensics import PEAnalyzer

# Initialize PE analyzer
analyzer = PEAnalyzer("/samples/suspicious.exe")

# Get basic info
info = analyzer.get_basic_info()
print(f"File: {info.filename}")
print(f"Size: {info.size}")
print(f"MD5: {info.md5}")
print(f"SHA256: {info.sha256}")
print(f"SSDeep: {info.ssdeep}")
print(f"Type: {info.file_type}")

# Get PE headers
headers = analyzer.get_headers()
print(f"Machine: {headers.machine}")
print(f"Timestamp: {headers.timestamp}")
print(f"Subsystem: {headers.subsystem}")
print(f"Entry point: 0x{headers.entry_point:x}")
print(f"Image base: 0x{headers.image_base:x}")

# Get sections
sections = analyzer.get_sections()
for section in sections:
    print(f"Section: {section.name}")
    print(f"  Virtual size: {section.virtual_size}")
    print(f"  Raw size: {section.raw_size}")
    print(f"  Entropy: {section.entropy}")
    print(f"  MD5: {section.md5}")

# Get imports
imports = analyzer.get_imports()
for dll, functions in imports.items():
    print(f"Import: {dll}")
    for func in functions:
        print(f"  - {func}")

# Get exports
exports = analyzer.get_exports()
for export in exports:
    print(f"Export: {export.name} @ {export.ordinal}")

# Detect anomalies
anomalies = analyzer.detect_anomalies()
for a in anomalies:
    print(f"ANOMALY: {a.type}")
    print(f"  Description: {a.description}")
    print(f"  Severity: {a.severity}")

# Get resources
resources = analyzer.get_resources()
for resource in resources:
    print(f"Resource: {resource.type}/{resource.name}")
    print(f"  Size: {resource.size}")
    print(f"  Language: {resource.language}")

# Check for packing
packing = analyzer.detect_packing()
print(f"Packed: {packing.is_packed}")
print(f"Packer: {packing.packer_name}")
print(f"Confidence: {packing.confidence}")

# Generate report
analyzer.generate_report("/evidence/pe_analysis.html")

Task 2: String Analysis

Input: Malware sample

Process:

1. Extract ASCII/Unicode strings
2. Categorize by type
3. Identify suspicious strings
4. Extract encoded strings
5. Document findings

Output: String analysis with categorization

Example:

from malware_forensics import StringAnalyzer

# Initialize string analyzer
analyzer = StringAnalyzer("/samples/malware.exe")

# Extract all strings
strings = analyzer.extract_all(min_length=4)
print(f"Total strings: {len(strings)}")

# Get strings by category
categorized = analyzer.categorize()

print(f"URLs: {len(categorized.urls)}")
for url in categorized.urls:
    print(f"  {url}")

print(f"IPs: {len(categorized.ips)}")
for ip in categorized.ips:
    print(f"  {ip}")

print(f"Domains: {len(categorized.domains)}")
for domain in categorized.domains:
    print(f"  {domain}")

print(f"Registry keys: {len(categorized.registry)}")
for reg in categorized.registry:
    print(f"  {reg}")

print(f"File paths: {len(categorized.file_paths)}")
for path in categorized.file_paths:
    print(f"  {path}")

# Find suspicious strings
suspicious = analyzer.find_suspicious()
for s in suspicious:
    print(f"SUSPICIOUS: {s.value}")
    print(f"  Category: {s.category}")
    print(f"  Reason: {s.reason}")

# Decode encoded strings
decoded = analyzer.decode_strings()
for d in decoded:
    print(f"Encoded: {d.encoded[:50]}...")
    print(f"  Encoding: {d.encoding}")
    print(f"  Decoded: {d.decoded}")

# Find strings with XOR patterns
xor_strings = analyzer.find_xor_encoded()
for x in xor_strings:
    print(f"XOR key 0x{x.key:02x}: {x.decoded}")

# Export strings
analyzer.export("/evidence/strings.txt")
analyzer.export_json("/evidence/strings.json")

Task 3: Document Analysis

Input: Malicious document (Office, PDF)

Process:

1. Parse document structure
2. Extract macros/scripts
3. Analyze embedded objects
4. Detect exploits
5. Extract payloads

Output: Document analysis report

Example:

from malware_forensics import DocumentAnalyzer

# Analyze Office document
analyzer = DocumentAnalyzer("/samples/malicious.docx")

# Get document info
info = analyzer.get_info()
print(f"Format: {info.format}")
print(f"Created: {info.created}")
print(f"Modified: {info.modified}")
print(f"Author: {info.author}")
print(f"Has macros: {info.has_macros}")
print(f"Has embedded: {info.has_embedded}")

# Extract macros
macros = analyzer.extract_macros()
for macro in macros:
    print(f"Macro: {macro.name}")
    print(f"  Type: {macro.type}")
    print(f"  Code preview: {macro.code[:200]}...")
    print(f"  Suspicious: {macro.is_suspicious}")

# Analyze VBA code
vba_analysis = analyzer.analyze_vba()
for finding in vba_analysis.findings:
    print(f"VBA Finding: {finding.type}")
    print(f"  Description: {finding.description}")
    print(f"  Code: {finding.code_snippet}")

# Get auto-execute triggers
triggers = analyzer.get_auto_triggers()
for trigger in triggers:
    print(f"Trigger: {trigger.name}")
    print(f"  Type: {trigger.trigger_type}")

# Extract embedded objects
embedded = analyzer.extract_embedded("/evidence/embedded/")
for obj in embedded:
    print(f"Embedded: {obj.filename}")
    print(f"  Type: {obj.content_type}")
    print(f"  SHA256: {obj.sha256}")

# Detect exploits
exploits = analyzer.detect_exploits()
for exploit in exploits:
    print(f"EXPLOIT: {exploit.cve}")
    print(f"  Description: {exploit.description}")
    print(f"  Confidence: {exploit.confidence}")

# PDF-specific analysis
if info.format == "PDF":
    pdf_analysis = analyzer.analyze_pdf_structure()
    print(f"JavaScript: {pdf_analysis.has_javascript}")
    print(f"OpenAction: {pdf_analysis.has_openaction}")
    print(f"Embedded files: {pdf_analysis.embedded_files}")

# Generate report
analyzer.generate_report("/evidence/document_analysis.html")

Task 4: Script Analysis

Input: Malicious script file

Process:

1. Identify script type
2. Deobfuscate code
3. Analyze behavior
4. Extract IOCs
5. Document capabilities

Output: Script analysis report

Example:

from malware_forensics import ScriptAnalyzer

# Analyze script
analyzer = ScriptAnalyzer("/samples/malicious.ps1")

# Get script info
info = analyzer.get_info()
print(f"Type: {info.script_type}")
print(f"Size: {info.size}")
print(f"Encoding: {info.encoding}")
print(f"Obfuscated: {info.is_obfuscated}")

# Deobfuscate script
deobfuscated = analyzer.deobfuscate()
print(f"Deobfuscation stages: {deobfuscated.stages}")
print(f"Final code preview: {deobfuscated.final_code[:500]}...")

# Analyze PowerShell-specific features
if info.script_type == "PowerShell":
    ps_analysis = analyzer.analyze_powershell()
    print(f"Download cradles: {ps_analysis.download_cradles}")
    print(f"Encoded commands: {ps_analysis.encoded_commands}")
    print(f"Bypass techniques: {ps_analysis.bypass_techniques}")

# Get suspicious patterns
patterns = analyzer.find_suspicious_patterns()
for p in patterns:
    print(f"Pattern: {p.name}")
    print(f"  Description: {p.description}")
    print(f"  Code: {p.code_snippet}")
    print(f"  MITRE: {p.mitre_technique}")

# Analyze JavaScript
if info.script_type == "JavaScript":
    js_analysis = analyzer.analyze_javascript()
    print(f"Eval calls: {js_analysis.eval_calls}")
    print(f"Document.write: {js_analysis.document_writes}")
    print(f"External requests: {js_analysis.external_requests}")

# Extract IOCs
iocs = analyzer.extract_iocs()
print(f"URLs: {iocs.urls}")
print(f"Domains: {iocs.domains}")
print(f"IPs: {iocs.ips}")

# Get execution flow
flow = analyzer.analyze_execution_flow()
for step in flow:
    print(f"Step {step.order}: {step.description}")
    print(f"  Action: {step.action}")

# Generate report
analyzer.generate_report("/evidence/script_analysis.html")

Task 5: IOC Extraction

Input: Malware sample

Process:

1. Extract network IOCs
2. Extract file IOCs
3. Extract registry IOCs
4. Deduplicate and validate
5. Export in multiple formats

Output: IOC collection

Example:

from malware_forensics import IOCExtractor

# Initialize extractor
extractor = IOCExtractor("/samples/malware.exe")

# Extract all IOCs
iocs = extractor.extract_all()

# Network IOCs
print(f"URLs ({len(iocs.urls)}):")
for url in iocs.urls:
    print(f"  {url.value}")
    print(f"    Context: {url.context}")
    print(f"    Confidence: {url.confidence}")

print(f"Domains ({len(iocs.domains)}):")
for domain in iocs.domains:
    print(f"  {domain.value}")

print(f"IPs ({len(iocs.ips)}):")
for ip in iocs.ips:
    print(f"  {ip.value}")
    print(f"    Type: {ip.ip_type}")  # C2, download, etc.

# File IOCs
print(f"File paths ({len(iocs.file_paths)}):")
for path in iocs.file_paths:
    print(f"  {path.value}")

print(f"File hashes ({len(iocs.hashes)}):")
for h in iocs.hashes:
    print(f"  {h.algorithm}: {h.value}")

# Registry IOCs
print(f"Registry keys ({len(iocs.registry_keys)}):")
for reg in iocs.registry_keys:
    print(f"  {reg.value}")

# Mutexes
print(f"Mutexes ({len(iocs.mutexes)}):")
for mutex in iocs.mutexes:
    print(f"  {mutex.value}")

# Validate IOCs
validated = extractor.validate_iocs(iocs)
print(f"Valid IOCs: {validated.valid_count}")
print(f"Invalid IOCs: {validated.invalid_count}")

# Enrich IOCs
enriched = extractor.enrich_iocs(
    iocs,
    sources=["virustotal", "threatfox", "urlhaus"]
)

# Export IOCs
extractor.export_csv("/evidence/iocs.csv")
extractor.export_json("/evidence/iocs.json")
extractor.export_stix("/evidence/iocs.stix")
extractor.export_misp("/evidence/iocs.misp.json")

Task 6: YARA Scanning

Input: Malware sample(s) and YARA rules

Process:

1. Compile YARA rules
2. Scan samples
3. Collect matches
4. Document findings
5. Generate report

Output: YARA scan results

Example:

from malware_forensics import YARAScanner

# Initialize scanner
scanner = YARAScanner()

# Add rule files
scanner.add_rules("/rules/malware_families.yar")
scanner.add_rules("/rules/packers.yar")
scanner.add_rules("/rules/exploits.yar")

# Add rule directory
scanner.add_rule_directory("/rules/")

# Scan single file
matches = scanner.scan_file("/samples/malware.exe")
for match in matches:
    print(f"Rule: {match.rule}")
    print(f"  Namespace: {match.namespace}")
    print(f"  Tags: {match.tags}")
    print(f"  Meta: {match.meta}")
    print(f"  Strings matched:")
    for s in match.strings:
        print(f"    {s.identifier}: {s.data} @ 0x{s.offset:x}")

# Scan directory
dir_matches = scanner.scan_directory("/samples/")
for file_path, matches in dir_matches.items():
    if matches:
        print(f"File: {file_path}")
        for match in matches:
            print(f"  - {match.rule}")

# Scan with specific rules
specific = scanner.scan_file(
    "/samples/malware.exe",
    rules=["APT_Malware", "Ransomware"]
)

# Get statistics
stats = scanner.get_statistics()
print(f"Files scanned: {stats.files_scanned}")
print(f"Rules loaded: {stats.rules_loaded}")
print(f"Matches found: {stats.total_matches}")

# Export results
scanner.export_results("/evidence/yara_results.json")
scanner.generate_report("/evidence/yara_report.html")

Task 7: Behavior Analysis

Input: Malware execution observations

Process:

1. Document file operations
2. Track registry changes
3. Monitor network activity
4. Identify persistence
5. Map to MITRE ATT&CK

Output: Behavior analysis report

Example:

from malware_forensics import BehaviorAnalyzer

# Initialize analyzer with sandbox report
analyzer = BehaviorAnalyzer()
analyzer.load_sandbox_report("/evidence/sandbox_report.json")

# Or manually add observations
analyzer.add_file_operation("create", "C:\\Windows\\Temp\\malware.exe")
analyzer.add_registry_operation("create", "HKCU\\Software\\Microsoft\\Windows\\CurrentVersion\\Run\\Malware")
analyzer.add_network_connection("tcp", "203.0.113.50", 443)
analyzer.add_process_creation("cmd.exe", "/c whoami")

# Get file operations
file_ops = analyzer.get_file_operations()
for op in file_ops:
    print(f"File: {op.operation} - {op.path}")
    print(f"  Time: {op.timestamp}")

# Get registry operations
reg_ops = analyzer.get_registry_operations()
for op in reg_ops:
    print(f"Registry: {op.operation} - {op.key}")
    print(f"  Value: {op.value}")

# Get network activity
network = analyzer.get_network_activity()
for conn in network:
    print(f"Network: {conn.protocol} {conn.destination}:{conn.port}")
    print(f"  DNS: {conn.dns_query}")

# Get process activity
processes = analyzer.get_process_activity()
for proc in processes:
    print(f"Process: {proc.name}")
    print(f"  Command: {proc.command_line}")
    print(f"  Parent: {proc.parent}")

# Map to MITRE ATT&CK
mitre = analyzer.map_to_mitre()
for technique in mitre:
    print(f"Technique: {technique.id} - {technique.name}")
    print(f"  Tactic: {technique.tactic}")
    print(f"  Evidence: {technique.evidence}")

# Identify capabilities
capabilities = analyzer.identify_capabilities()
for cap in capabilities:
    print(f"Capability: {cap.name}")
    print(f"  Description: {cap.description}")
    print(f"  Confidence: {cap.confidence}")

# Generate behavior report
analyzer.generate_report("/evidence/behavior_analysis.html")

Task 8: Sample Comparison

Input: Multiple malware samples

Process:

1. Calculate similarity hashes
2. Compare code sections
3. Identify shared IOCs
4. Find common patterns
5. Cluster related samples

Output: Sample comparison results

Example:

from malware_forensics import SampleComparator

# Initialize comparator
comparator = SampleComparator()

# Add samples
comparator.add_sample("/samples/sample1.exe")
comparator.add_sample("/samples/sample2.exe")
comparator.add_sample("/samples/sample3.exe")

# Or add directory
comparator.add_directory("/samples/")

# Compare all samples
comparison = comparator.compare_all()

# Get similarity matrix
matrix = comparison.similarity_matrix
for sample1, similarities in matrix.items():
    for sample2, score in similarities.items():
        if score > 0.8:
            print(f"{sample1} <-> {sample2}: {score:.2f}")

# Find clusters
clusters = comparator.cluster_samples(threshold=0.7)
for i, cluster in enumerate(clusters):
    print(f"Cluster {i + 1}:")
    for sample in cluster:
        print(f"  - {sample}")

# Compare specific samples
detail = comparator.compare_pair("/samples/a.exe", "/samples/b.exe")
print(f"Overall similarity: {detail.overall_score}")
print(f"Section similarity: {detail.section_scores}")
print(f"Import similarity: {detail.import_score}")
print(f"String similarity: {detail.string_score}")
print(f"Shared IOCs: {detail.shared_iocs}")

# Find shared code
shared_code = comparator.find_shared_code()
for code in shared_code:
    print(f"Shared code block at offset {code.offset}")
    print(f"  Size: {code.size}")
    print(f"  Samples: {code.samples}")

# Generate comparison report
comparator.generate_report("/evidence/comparison_report.html")

Task 9: Attribution Analysis

Input: Malware sample with IOCs

Process:

1. Match against known families
2. Compare with threat intel
3. Identify TTPs
4. Find related campaigns
5. Document attribution

Output: Attribution analysis

Example:

from malware_forensics import AttributionAnalyzer

# Initialize analyzer
analyzer = AttributionAnalyzer("/samples/malware.exe")

# Match against malware families
families = analyzer.match_malware_families()
for family in families:
    print(f"Family: {family.name}")
    print(f"  Confidence: {family.confidence}")
    print(f"  Matching indicators: {family.indicators}")

# Check against threat intel
threat_intel = analyzer.check_threat_intel(
    feeds=["malwarebazaar", "virustotal", "threatfox"]
)
for intel in threat_intel:
    print(f"Intel: {intel.source}")
    print(f"  Family: {intel.family}")
    print(f"  Tags: {intel.tags}")
    print(f"  First seen: {intel.first_seen}")

# Match TTPs to threat actors
actors = analyzer.match_threat_actors()
for actor in actors:
    print(f"Threat Actor: {actor.name}")
    print(f"  Confidence: {actor.confidence}")
    print(f"  Matching TTPs: {actor.matching_ttps}")
    print(f"  Known aliases: {actor.aliases}")

# Find related campaigns
campaigns = analyzer.find_related_campaigns()
for campaign in campaigns:
    print(f"Campaign: {campaign.name}")
    print(f"  Time range: {campaign.start_date} - {campaign.end_date}")
    print(f"  Targets: {campaign.targets}")

# Get attribution summary
summary = analyzer.get_attribution_summary()
print(f"Most likely family: {summary.primary_family}")
print(f"Most likely actor: {summary.primary_actor}")
print(f"Confidence: {summary.overall_confidence}")

# Generate attribution report
analyzer.generate_report("/evidence/attribution.html")

Task 10: Malware Triage

Input: Collection of suspicious files

Process:

1. Calculate hashes
2. Check against known malware
3. Quick static analysis
4. Prioritize for analysis
5. Generate triage report

Output: Triage results with priorities

Example:

from malware_forensics import MalwareTriage

# Initialize triage
triage = MalwareTriage()

# Add samples
triage.add_directory("/quarantine/")

# Run triage
results = triage.run()

print(f"Total samples: {results.total}")
print(f"Known malware: {results.known_malware}")
print(f"Suspicious: {results.suspicious}")
print(f"Clean: {results.clean}")
print(f"Unknown: {results.unknown}")

# Get prioritized list
prioritized = triage.get_prioritized()
for sample in prioritized:
    print(f"Priority {sample.priority}: {sample.filename}")
    print(f"  Status: {sample.status}")
    print(f"  Reason: {sample.reason}")
    print(f"  Risk score: {sample.risk_score}")

# Get known malware
known = triage.get_known_malware()
for m in known:
    print(f"Known: {m.filename}")
    print(f"  Family: {m.family}")
    print(f"  Detection: {m.detection_name}")

# Get suspicious files
suspicious = triage.get_suspicious()
for s in suspicious:
    print(f"Suspicious: {s.filename}")
    print(f"  Indicators: {s.indicators}")

# Export triage results
triage.export_csv("/evidence/triage_results.csv")
triage.generate_report("/evidence/triage_report.html")

Configuration

Environment Variables

YARA_RULES_PATH

VT_API_KEY

MALWARE_BAZAAR_KEY

SANDBOX_API

Options

auto_deobfuscate

extract_resources

deep_string_analysis

check_threat_intel

parallel

Examples

Example 1: Incident Response Analysis

Scenario: Analyzing malware from compromised system

from malware_forensics import MalwareAnalyzer, IOCExtractor

# Analyze malware sample
analyzer = MalwareAnalyzer("/evidence/malware.exe")
analysis = analyzer.full_analysis()

# Extract IOCs for blocking
extractor = IOCExtractor("/evidence/malware.exe")
iocs = extractor.extract_all()

# Export for SIEM
extractor.export_stix("/evidence/block_iocs.stix")

# Generate IR report
analyzer.generate_ir_report("/evidence/malware_ir_report.html")

Example 2: Threat Intelligence

Scenario: Analyzing new malware for threat intel

from malware_forensics import MalwareAnalyzer, AttributionAnalyzer

# Full analysis
analyzer = MalwareAnalyzer("/samples/new_sample.exe")
analysis = analyzer.full_analysis()

# Attribution
attribution = AttributionAnalyzer("/samples/new_sample.exe")
actor = attribution.match_threat_actors()
campaigns = attribution.find_related_campaigns()

# Generate threat intel report
analyzer.generate_threat_intel_report("/evidence/threat_intel.html")

Limitations

• Static analysis cannot detect runtime behavior
• Packed samples may require manual unpacking
• Obfuscated code may resist analysis
• Attribution has inherent uncertainty
• Requires safe environment for handling samples
• Some formats may have limited support
• Threat intel depends on available data

Troubleshooting

Common Issue 1: PE Parsing Failure

Problem: Cannot parse PE file Solution:

• Check file integrity
• May be packed or corrupted
• Try different parsing options

Common Issue 2: Deobfuscation Failure

Problem: Script remains obfuscated Solution:

• Try manual deobfuscation
• Use dynamic analysis
• Check for custom obfuscation

Common Issue 3: YARA Rule Errors

Problem: YARA rules fail to compile Solution:

• Check rule syntax
• Verify string escape sequences
• Update YARA version

Related Skills

• memory-forensics: Memory-based malware analysis
• disk-forensics: Find malware artifacts on disk
• network-forensics: Analyze malware traffic
• timeline-forensics: Malware timeline integration
• artifact-collection: Sample collection procedures

References

• Malware Analysis Reference
• YARA Rule Writing Guide
• PE Format Specification