Network Traffic Analysis
Analyze network traffic patterns to detect anomalies, optimize performance, and enhance security. Covers flow analysis, packet inspection, traffic classification, bandwidth planning, and the patterns that turn raw network data into actionable intelligence.
Network traffic analysis transforms raw packets and flow records into intelligence. It answers critical questions: Is someone exfiltrating data? Why did latency spike at 3 AM? Which application is consuming 80% of bandwidth? Without traffic analysis, your network is a black box where problems are discovered by users, not engineers.
Traffic Analysis Layers
Metadata Analysis (Flow Records):
What: Source, destination, ports, bytes, duration
Tools: NetFlow, IPFIX, VPC Flow Logs, sFlow
Use: Capacity planning, anomaly detection, billing
Volume: Low (summarized per flow)
Example flow record:
src: 10.1.2.3:45678
dst: 203.0.113.50:443
protocol: TCP
bytes: 1,234,567
packets: 890
start: 2026-03-04T14:23:00Z
duration: 45s
action: ACCEPT
Header Analysis (Packet Headers):
What: IP, TCP/UDP headers without payload
Tools: tcpdump (headers only), Zeek, Suricata
Use: Protocol analysis, connection behavior
Volume: Medium (every packet header)
Deep Packet Inspection (Full Capture):
What: Complete packet including payload
Tools: Wireshark, tcpdump (full capture), Moloch/Arkime
Use: Forensics, debugging, compliance
Volume: Very high (storage intensive)
⚠️ May capture sensitive data — handle with careAnomaly Detection
class TrafficAnomalyDetector:
"""Detect anomalies in network flow data."""
def analyze_flows(self, flows: list) -> list:
"""Identify suspicious traffic patterns."""
anomalies = []
# 1. Data exfiltration: Large outbound transfers
for flow in flows:
if (flow.direction == "outbound" and
flow.bytes > self.baseline_outbound * 10 and
flow.dst_ip not in self.known_destinations):
anomalies.append({
"type": "potential_exfiltration",
"severity": "high",
"flow": flow,
"reason": f"Outbound transfer {flow.bytes} bytes "
f"to unknown destination {flow.dst_ip}",
})
# 2. Port scanning: Many connections to different ports
src_port_counts = self.group_by_src_and_count_dst_ports(flows)
for src, port_count in src_port_counts.items():
if port_count > 100:
anomalies.append({
"type": "port_scan",
"severity": "medium",
"source": src,
"ports_scanned": port_count,
})
# 3. DNS tunneling: High volume of DNS queries
dns_flows = [f for f in flows if f.dst_port == 53]
dns_volume = sum(f.bytes for f in dns_flows)
if dns_volume > self.baseline_dns * 5:
anomalies.append({
"type": "dns_tunneling_suspect",
"severity": "high",
"dns_volume_bytes": dns_volume,
"baseline": self.baseline_dns,
})
return anomaliesAnti-Patterns
| Anti-Pattern | Consequence | Fix |
|---|---|---|
| Capture everything, analyze nothing | Storage costs with no value | Define analysis goals, capture selectively |
| No traffic baseline | Cannot detect anomalies | Establish normal patterns first, then alert on deviation |
| Full packet capture in production | Performance impact, privacy risk | Flow records for baselining, full capture for forensics only |
| Ignore encrypted traffic metadata | Miss anomalies in HTTPS/TLS | Analyze flow metadata (size, timing, destination) not content |
| No retention policy | Unbounded storage growth | 7-day full capture, 90-day flows, 1-year summaries |
Network traffic analysis is surveillance done right — monitoring your own infrastructure to protect it. The key is knowing what to look for, establishing what normal looks like, and alerting when reality diverges from that baseline.
