Network Log Analysis

Device-level network log analysis using raw syslog data without SIEM platforms. Guides forensic timeline construction from rsyslog/syslog-ng collectors, device console logs, and SNMP trap data. Covers syslog pattern recognition across Cisco IOS-XE, Juniper JunOS, and Arista EOS message formats, multi-device event correlation using grep/awk/sort, anomaly detection via baseline deviation, and chronological timeline reconstruction with NTP-aware timestamp normalization.

Device-level syslog analysis and forensic timeline construction without SIEM platforms. This skill covers raw log data from rsyslog/syslog-ng collectors, device console output, and SNMP trap receivers — all analysis uses standard Unix tools (grep, awk, sort, sed).

For environments with a SIEM platform, use the companion skill siem-log-analysis which provides the same forensic reasoning with platform-specific query syntax.

Reference references/cli-reference.md for rsyslog/syslog-ng configuration, device syslog commands, and log parsing one-liners. Reference references/syslog-patterns.md for vendor-specific message formats, the RFC 5424 facility/severity matrix, and common event pattern catalogs.

When to Use

• No SIEM available — investigate network events using only raw syslog files on a centralized collector or individual device logs
• Syslog infrastructure audit — verify that rsyslog or syslog-ng is correctly receiving, routing, and retaining logs from all network devices in scope
• Multi-device event correlation — construct a unified timeline from separate per-device or per-facility log files using timestamp sorting and pattern matching
• Anomaly investigation — identify deviations from normal log volume, new message types, or authentication failure clusters without statistical query engines
• Post-incident timeline reconstruction — assemble a chronological evidence chain from raw logs after a network outage or security event
• Log retention compliance — verify that log rotation policies and retention periods meet organizational or regulatory requirements

Prerequisites

• Syslog collector access — SSH or console access to the rsyslog/syslog-ng server with read permissions on log directories (typically /var/log/ or custom paths defined in collector config)
• Device CLI access — Read-only credentials for network devices to verify syslog forwarding configuration and NTP synchronization status
• Unix tool availability — grep, awk, sort, sed, wc, and date available on the syslog collector (standard on any Linux/BSD system)
• NTP verification — Confirm time synchronization across all network devices and the syslog collector before multi-device correlation; skewed clocks corrupt timeline accuracy
• Log file identification — Know the log file paths, naming conventions, and rotation schedule on the collector; rsyslog and syslog-ng route logs differently based on facility, severity, and source address

Procedure

Follow these six steps sequentially. The procedure builds a forensic timeline from raw syslog evidence through pattern recognition, correlation, anomaly detection, and chronological reconstruction. Each step produces artifacts that feed subsequent steps.

Step 1: Log Collection Assessment

Verify that the syslog infrastructure is complete and healthy before analyzing log content. Missing or misconfigured sources create blind spots that invalidate investigation conclusions.

Syslog server configuration — Examine the collector configuration to understand how logs are routed:

• [rsyslog] — Review /etc/rsyslog.conf and /etc/rsyslog.d/*.conf for input modules (imudp, imtcp, imrelp), facility/severity routing rules, and output file templates. Confirm that $ActionFileDefaultTemplate includes the source hostname for multi-device disambiguation.
• [syslog-ng] — Review /etc/syslog-ng/syslog-ng.conf for source definitions (network listeners), filter chains (facility, severity, host match), and destination paths. Verify that keep-hostname(yes) preserves the originating device hostname.

Device syslog verification — On each in-scope network device, confirm syslog forwarding is active and targeting the correct collector:

• [Cisco] — show logging confirms logging host, trap level, and facility; show logging history shows recent buffered severity counts
• [JunOS] — show system syslog shows configured host targets, facility filters, and structured-data enablement
• [EOS] — show logging shows syslog server address, logging level, and protocol (UDP/TCP)

NTP synchronization check — Verify on each device:

• [Cisco] — show ntp status (stratum, offset)
• [JunOS] — show system ntp (peer status, offset)
• [EOS] — show ntp status (clock state, stratum)

Devices with NTP offset exceeding 1 second require timestamp correction before correlation.

Log retention and rotation — Check logrotate configuration for retention period, compression, and file size limits. Confirm the retention window covers the investigation period. Missing rotated files indicate evidence gaps.

Step 2: Syslog Pattern Recognition

Parse raw syslog messages into structured fields using vendor-specific format knowledge. Pattern recognition transforms unstructured text into correlatable evidence.

Vendor message formats:

• [Cisco] — %FACILITY-SEVERITY-MNEMONIC: message. Facility identifies the subsystem (LINEPROTO, OSPF, SEC), severity is 0–7 (RFC 5424), mnemonic is the event identifier.
• [JunOS] — hostname process[pid]: EVENT_ID: message. When structured-data is enabled, adds [tag value] pairs.
• [EOS] — hostname AgentName: %FACILITY-SEVERITY-message. Agent name identifies the subsystem (Ebra, Bgp, Ospf).

Severity classification — Extract the severity digit and map to RFC 5424 levels (0=Emergency through 7=Debug). Filter scope to severity 0–4 (Emergency through Warning) for operationally significant events. Use severity 5–7 only when lower severities lack context.

Message frequency baseline — Count messages per facility per hour to establish normal volume:

text

awk '{print $1, $2, $3}' /var/log/network.log | sort | uniq -c | sort -rn

This produces a timestamp-grouped frequency table. Significant deviations from the hourly mean indicate events worth investigating.

Facility-to-subsystem mapping — Map syslog facility codes to network subsystems using references/syslog-patterns.md. Facility local0–local7 assignments vary per organization — check the rsyslog routing rules from Step 1 to decode local facility meanings.

Step 3: Event Correlation

Join events from multiple devices and log files by shared attributes to build investigation threads from isolated messages.

Multi-device timeline via grep/awk/sort — Merge per-device log files into a single chronologically sorted stream:

text

cat /var/log/rtr*.log /var/log/sw*.log | sort -k1,3 > /tmp/merged-timeline.log

If log files use different timestamp formats, normalize first with awk before merging (see Step 5 for timestamp normalization details).

Temporal clustering — Identify events within a configurable time window of a known trigger event. For precision, convert timestamps to epoch seconds and select events within the desired window using awk numeric comparison (see references/cli-reference.md for correlation helpers).

Causal chain detection — Network failures propagate through protocol dependencies in predictable patterns: interface flap (LINK-3-UPDOWN) → OSPF neighbor down (OSPF-5-ADJCHG) → BGP route withdrawal (BGP-5-ADJCHANGE) → traffic reroute on alternate paths. Search for cascading patterns by extracting events matching each stage and verifying temporal sequence.

SNMP trap correlation — If the collector receives SNMP traps (via snmptrapd), correlate trap OIDs with syslog events. Interface linkDown traps (OID 1.3.6.1.6.3.1.1.5.3) should pair with LINK-UPDOWN syslog messages from the same device. Mismatches indicate logging gaps.

Step 4: Anomaly Detection

Compare observed log patterns against baselines to surface deviations that warrant investigation. All detection uses grep, awk, and sort against raw log files.

Baseline deviation — Compare current-day event counts per device against the rolling 7-day average. A current-day count exceeding twice the average signals a volume anomaly. Calculate per-facility counts to pinpoint which subsystem generates excess messages.

New or unseen message types — Extract unique mnemonics from the investigation window and compare against a baseline file:

text

grep -oP '%\S+-\d-\S+' /var/log/cisco.log | sort -u > /tmp/current.txt
comm -23 /tmp/current.txt /tmp/baseline-mnemonics.txt

Mnemonics present in current but absent from baseline are first-seen events requiring classification.

Authentication failure clustering — Extract auth messages and group by source IP using grep/awk/sort (see references/cli-reference.md for one-liners). Source IPs with failure counts exceeding 10 per hour warrant investigation as potential brute-force attempts.

Config changes outside maintenance windows — Filter for config change messages (SYS-5-CONFIG_I, UI_COMMIT) and check timestamps against the approved schedule. Changes outside the window require attribution — who changed what, and was it authorized.

Login source IP anomalies — Extract management session source IPs and compare against the authorized management subnet list. IPs outside known ranges indicate unauthorized access attempts.

Step 5: Timeline Reconstruction

Assemble a definitive chronological event sequence from all evidence gathered in Steps 1–4. The timeline is the primary deliverable of forensic log analysis.

Chronological assembly — Merge relevant events from multiple log sources into a single sorted output:

text

grep -h "PATTERN1\|PATTERN2\|PATTERN3" /var/log/*.log | sort -k1,3

Use sort -s -k1,3 (stable sort) to preserve original order of events with identical timestamps.

NTP-aware timestamp normalization — If devices log in different timezones or formats, normalize all timestamps to UTC epoch seconds before sorting. Convert RFC 3164 timestamps with awk piped to date (see references/cli-reference.md for conversion one-liners). Apply the NTP offset correction from Step 1 to events from devices with known clock drift. For BSD/macOS, use date -jf instead of date -d.

Event-to-impact mapping — For each significant event in the timeline, annotate the user-visible impact:

1. Identify the event (e.g., OSPF-5-ADJCHG neighbor down)
2. Determine the network impact (loss of redundant path)
3. Map to the user symptom (degraded connectivity or failover latency)

Root cause ordering — Walk the timeline backward from the user-reported symptom to the earliest causal event. The root cause is the first event that, if prevented, would have prevented all downstream effects. Document the causal chain with event references for each link.

Step 6: Report

Compile all findings into a structured deliverable. Present the event timeline from Step 5 as the central artifact — annotate each entry with its classification (root cause, contributing factor, symptom, or informational). Summarize anomaly findings from Step 4 with counts and severity assessments. Document correlation chains from Step 3 with supporting evidence. State the root cause assessment with confidence level and the supporting evidence chain. Include an integrity section listing evidence gaps that limit conclusions.

Threshold Tables

Log Volume Anomaly Thresholds

Metric	Normal	Warning (>1.5×)	Alert (>2×)	Critical (>3×)
Messages per hour (per device)	Baseline ± 50%	1.5–2× baseline	2–3× baseline	>3× baseline
Unique mnemonics per day	Baseline count	1–3 new mnemonics	4–10 new mnemonics	>10 new mnemonics
Auth failure events (per source IP)	≤3/hour	4–10/hour	11–50/hour	>50/hour
Config change events (per device)	≤2/day during windows	Any outside window	3+ outside window	>5 outside window
SNMP trap rate (per device)	≤5/hour	6–20/hour	21–100/hour	>100/hour

Syslog Severity Response Matrix

Severity	RFC 5424 Level	Investigation Action
0 — Emergency	System unusable	Immediate investigation, all-hands
1 — Alert	Immediate action needed	Priority investigation within 15 minutes
2 — Critical	Critical conditions	Investigation within 1 hour
3 — Error	Error conditions	Investigation within 4 hours
4 — Warning	Warning conditions	Review within 24 hours
5 — Notice	Normal but significant	Log for trending, review weekly
6 — Info	Informational	Baseline data, no action
7 — Debug	Debug-level	Exclude from standard analysis

Correlation Confidence Levels

Confidence	Criteria	Action
Confirmed	3+ events across 2+ devices with matching attributes and <60s window	Treat as established fact in report
Probable	2 correlated events or single-device chain with supporting evidence	Include in report with qualification
Possible	Single event or loose time correlation (>5 min window)	Note as hypothesis, do not assert as finding

Decision Trees

Investigation Entry Point

text

Investigation trigger received
├── Reported outage with known time window?
│   ├── Yes → Start at Step 3 (Correlation) scoped to window
│   │   └── Expand to Step 2 (Pattern Recognition) if correlation
│   │       yields insufficient events
│   └── No → Start at Step 1 (Log Collection Assessment)
│
├── Anomaly detected in monitoring (no log detail)?
│   ├── Time of anomaly known? → Start at Step 4 (Anomaly Detection)
│   │   └── Confirm with Step 2 pattern analysis
│   └── Time unknown? → Full procedure Steps 1–6
│
├── Post-incident review (incident already resolved)?
│   └── Start at Step 1 → Full procedure for completeness
│       └── Focus Step 5 (Timeline) for the deliverable
│
└── Compliance audit of syslog infrastructure?
    └── Steps 1 and 2 only (Collection + Pattern verification)

Anomaly Classification

text

Anomaly identified in Step 4
├── Volume anomaly (message count deviation)?
│   ├── Single device affected?
│   │   ├── Device reload or maintenance → Expected, document
│   │   └── No maintenance → Investigate device health
│   └── Multiple devices affected?
│       ├── Shared upstream link event? → Correlate in Step 3
│       └── Independent spikes? → Investigate each separately
│
├── New mnemonic or event type?
│   ├── Matches known firmware upgrade pattern? → Expected
│   ├── Security-related facility (SEC, AUTH)? → Priority review
│   └── Informational facility? → Add to baseline if benign
│
├── Authentication anomaly?
│   ├── Single source IP, many targets? → Brute-force scanning
│   ├── Many source IPs, single target? → Distributed attack
│   └── Single source, single target? → Credential issue
│
└── Configuration change outside window?
    ├── Attributed to known admin? → Verify authorization
    ├── Attributed to unknown user? → Security incident
    └── No attribution available? → Escalate immediately

Report Template

text

NETWORK LOG ANALYSIS REPORT
==============================
Report ID: [unique identifier]
Investigation Trigger: [outage report / anomaly alert / compliance audit]
Investigation Window: [start timestamp] — [end timestamp] (UTC)
Analyst: [name/identifier]
Log Sources: [collector hostname, device count, log file paths]

SUMMARY:
- Investigation type: [outage / security / compliance / anomaly]
- Root cause confidence: [Confirmed / Probable / Possible]
- Devices involved: [count and hostnames]
- Evidence gaps: [list any missing log sources or time gaps]

LOG COLLECTION STATUS (Step 1):
- Syslog collector: [rsyslog / syslog-ng, config path]
- Devices forwarding: [count] / [total expected]
- NTP synchronized: [count] / [total], max offset: [ms]
- Retention coverage: [days available] vs [days needed]

EVENT TIMELINE (Step 5):
| # | Timestamp (UTC) | Device | Facility-Sev | Mnemonic/Event | Message Summary | Classification |
|---|-----------------|--------|-------------|----------------|-----------------|----------------|
| 1 | [time] | [host] | [fac-sev] | [mnemonic] | [summary] | [root cause / contributing / symptom] |

ANOMALIES DETECTED (Step 4):
| # | Type | Device(s) | Description | Severity |
|---|------|-----------|-------------|----------|

CORRELATION CHAINS (Step 3):
- Chain 1: [event A] → [event B] → [event C]
  Confidence: [Confirmed / Probable / Possible]

ROOT CAUSE ASSESSMENT:
- Root cause: [description]
- Confidence: [level with justification]
- Causal chain: [first event → ... → user impact]

EVIDENCE INTEGRITY:
- Gaps: [missing devices, time periods, rotated files]
- NTP corrections applied: [list any offset adjustments]

RECOMMENDATIONS:
1. [immediate — e.g., fix syslog forwarding gap, address root cause]
2. [short-term — e.g., add missing devices to collector, tune rotation]
3. [long-term — e.g., improve NTP architecture, add log redundancy]

Troubleshooting

Missing Device Logs on Collector

Symptom: Expected device logs are absent from syslog collector files despite the device being configured to forward syslog.

Diagnosis: Verify syslog configuration on the device (see Step 1 commands). Check network path — firewall rules may block UDP 514 or TCP 514 between the device and collector. On the collector, check rsyslog/syslog-ng for dropped messages: rsyslogd logs input errors to its own syslog facility. Verify the collector is listening on the expected port with ss -ulnp | grep 514.

Resolution: Fix the forwarding path (device config, network ACLs, collector listener). Generate a test message from the device and confirm receipt. Document the gap period in the investigation report.

Timestamp Format Inconsistencies

Symptom: Merged log files produce an unsortable timeline because timestamp formats differ (RFC 3164 vs RFC 5424 vs device-specific).

Diagnosis: Inspect the first 10 lines of each source. RFC 3164 uses Mmm dd HH:MM:SS (no year); RFC 5424 uses ISO 8601 with timezone.

Resolution: Write an awk normalizer for each format (see Step 5 and references/cli-reference.md). Add the year to RFC 3164 timestamps based on file modification date or logrotate naming convention.

Log Rotation Destroyed Evidence

Symptom: Investigation period extends beyond the oldest available log file.

Diagnosis: Check /etc/logrotate.d/ for retention and compression settings. Look for compressed archives (.gz, .bz2, .xz).

Resolution: Search within compressed files using zgrep or zcat | grep. If data is permanently lost, document the gap and state which conclusions are limited by missing evidence.

High Log Volume Makes grep Impractical

Symptom: Multi-GB log files make interactive grep analysis impractically slow.

Resolution: Narrow to the investigation window with a date-based grep first, redirect to a working file, then apply detailed analysis to the smaller extract. Use LC_ALL=C grep for faster processing. Consider GNU parallel for multi-file analysis.