CyberCode Academy

Course 6 - Network Traffic Analysis for Incident Response | Episode 7: Network Data Analysis Toolkit: Tools, Techniques and Threat Signature

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In this lesson, you’ll learn about: The complete toolkit and techniques for analyzing network traffic using Connection Analysis, Statistical Analysis, and Event-Based (signature-focused) Analysis. 1. Data Analysis Toolkit General-Purpose Tools These are foundational command-line utilities used to search, filter, and reshape data:
  • grep → pattern searching
  • awk → field extraction and manipulation
  • cut → selecting specific columns
    Used together, they form powerful pipelines for rapid, custom analysis.
Scripting Languages Python
  • Most important language for packet analysis.
  • Scapy allows:
    • Parsing PCAPs
    • Inspecting packet structure
    • Accessing fields (IP, ports)
    • Filtering traffic (e.g., HTTP GET requests)
    • Deobfuscating malware traffic
      • Example: Extracting useful strings from compressed Ghostrat C2 payloads.
R
  • Useful for statistical modeling and clustering of network data.
Specialized Tools
  • Netstat → enumerates active connections
  • Silk → large-scale flow analysis (CERT tool)
  • Yara → rule-based threat matching (binary/text patterns)
  • Snort → signature-based intrusion detection
2. The Three Core Data Analysis Techniques A. Connection Analysis Purpose: High-level visibility into which systems are connecting to which. Ideal for:
  • Detecting unauthorized servers or suspicious programs
  • Spotting lateral movement (e.g., odd SSH usage)
  • Identifying database misuse
  • Ensuring compliance across security zones
Primary Tool: Netstat
  • Shows all active connections + states
    (LISTENING, ESTABLISHED, TIME_WAIT, etc.)
Example Uses:
  • Spotting malware opening a hidden port
  • Identifying unauthorized remote access
  • Finding systems connecting to suspicious IPs
B. Statistical Analysis A macro-level technique designed to spot deviations from normal behavior. Techniques: 1. Clustering Group similar traffic together to identify families or variants.
  • Demonstrated by clustering Ghostrat variants through similarities in their C2 protocol.
2. Stack Counting Sort traffic by count of activity on:
  • Destination ports
  • Host connections
  • Packet types
Used to find anomalies:
  • Single visits to rare ports (2266, 3333)
  • Unexpected FTP traffic (port 21)
3. Wireshark Statistics Using built-in metrics:
  • Packet lengths (large packets → possible exfiltration or malware downloads)
  • Endpoints
  • Protocol hierarchy
Specialized Tool: Silk
  • Designed for massive enterprise networks
  • Supports both command line & Python (Pysilk)
  • Ideal for flow-level analysis, anomaly detection, and trend discovery.
C. Event-Based Analysis (Signature Focused) A micro-level technique used to identify known threats via rules and signatures. 1. Yara Signatures
  • Rules match known binary or text patterns.
  • Example uses:
    • Detecting Ghostrat via identifying strings like "lurk zero" or "v2010"
    • Multi-string matching to detect multi-stage malware
    • Matching malicious hostnames or indicators
Used for:
  • Malware classification
  • Reverse-engineering support
  • Deep content inspection
2. Snort Rules Snort provides concise detection logic for network traffic. Rule Structure Includes:
  • Action (alert, log)
  • Protocol (TCP/UDP)
  • Source/destination + ports
  • Options (content matches, flags, byte tests)
Examples Provided:
  • Detecting Nmap Xmas scans (FIN + PUSH + URG flags)
  • Detecting SMTP credential leakage (plaintext “authentication succeeded” over port 25)
Snort highlights:
  • Excellent for IDS/IPS
  • Simple to write and test
  • Widely used in enterprise SOCs
3. Practical Demonstrations A. Scapy + Yara Workflow shown:
  1. Use Scapy to load and parse PCAP
  2. Extract payloads
  3. Feed payloads to Yara
  4. Detect Ghostrat, multi-stage malware, or other known threats
This combination gives both:
  • PCAP-level filtering
  • Payload-level signature inspection
B. Scapy + Snort Two key demonstrations: 1. Automatic Snort Rule Generation
  • Tools like packet_to_snort.py generate draft Snort rules from suspicious packets.
2. Packet Manipulation for Rule Testing
  • Scapy is used to modify packet captures (e.g., IP address changes)
  • Allows testing Snort signatures under different conditions
  • Helps ensure rules are stable and do not create false positives
Summary: Combined Defense Strategy Effective network security requires all three techniques working together:TechniquePurposeCatchable ThreatsConnection AnalysisHigh-level visibilityUnauthorized access, lateral movementStatistical AnalysisDetect anomalies and unknown threatsData exfiltration, malware downloadsEvent-Based AnalysisDetect known, signature-based attacksRATs, worms, exploit kits

A mature SOC or network defense operation relies on all three to defend against:
  • Known threats
  • Zero-days
  • Misconfigurations
  • Insider activity
  • Advanced malware campaigns


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CyberCode AcademyBy CyberCode Academy