Unlocking the Power of Logs: From Basic Monitoring to Security Insights

Preface

If we treat operations like a doctor diagnosing a patient, logs are the patient's own description of symptoms; operators must interpret these records to determine system health, severity of issues, and appropriate remedial actions.

Just as ancient physicians matched treatment to symptoms, logs provide essential context for troubleshooting, yet many small‑to‑mid‑size enterprises undervalue them until disk space runs low and they are forced to delete large log files.

Scope of Operations

Operations involve many focus points, any of which can trigger problems, so operators often act like firefighters, responding wherever an issue arises.

Typical monitoring dashboards look attractive and feature‑rich, but few people watch them continuously; instead they rely on alerts that point out anomalies, then investigate the underlying logs.

What is the relationship between these dashboards and logs?

Logs have no intrinsic value; they become valuable only after analysis. They contain rich information spanning operations, business, and security, and can feed unified alert platforms that automate fault detection.

What Is a Log?

Simply put, a log records information generated by computers, devices, or software under specific conditions. Content varies by source: Unix systems log login/logout events, firewalls log ACL passes/denials, storage systems generate logs on failures or predicted faults.

Logs reveal why they were generated and what the system has done—for example, a web server logs each request for resources, including usernames when authentication occurs.

Through logs, IT staff can assess system performance, security posture, and even business operations.

What Can Logs Do?

In a complete information system, logs record every action in a standardized format, enabling troubleshooting, performance optimization, and behavior adjustments.

In security, logs expose attacks such as failed logins, anomalous accesses, performance issues, fault detection, intrusion detection, and provide forensic evidence for post‑incident analysis.

Insights from a Single Log Entry

1. User Activity Analysis

111.88.155.166 - - [17/Dec/2015:13:06:05 +0800] "POST /login HTTP/1.1" 200 0 "http://secilog.abc.com/login?langType=zh" "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.86 Safari/537.36"

This typical Nginx log line reveals the visitor's IP, timestamp, request method, requested page, response status, and referrer. The request "/login" indicates a login attempt, and the 200 status confirms success.

From such entries we can derive metrics like active users (unique IPs per day), new registrations, and potential fraudulent behavior such as excessive login attempts from a single IP, which may indicate credential‑stuffing or bot activity.

2. Security Behavior Analysis

Consider a similar log line with a 302 response:

111.88.155.166 - - [17/Dec/2015:13:06:05 +0800] "POST /login HTTP/1.1" 302 0 "http://secilog.abc.com/login?langType=zh" "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.86 Safari/537.36"

A 302 indicates a redirect, often meaning the login failed. Repeated failed logins from the same IP within a short window (e.g., >20 failures in 5 minutes) suggest a password‑guessing attack.

Analyzing the referrer URL can help distinguish automated brute‑force tools from legitimate browsers. Similar patterns reveal CC attacks, webshell uploads, or SQL injection attempts, though payload details may be limited if POST parameters are not logged.

How to Analyze Logs

Collect Logs

Log analysis typically involves four stages: collection (supporting protocols like syslog, SFTP, etc.), formatting, filtering/aggregation, alerting, and finally storage. Different log formats require custom parsers; for example, firewall and IDS logs must be normalized before extracting meaningful dimensions.

Analyze Logs

Log analysis includes:

Keyword analysis : searching for specific terms.

Statistical analysis : aggregating data over time to identify trends.

Correlation analysis : linking events across heterogeneous sources to uncover hidden relationships and potential policy violations.

Correlation methods involve defining audit strategies that combine multiple heterogeneous events to infer the nature of operations and detect anomalies.

Recommended Log Analysis Tools

Building a complete solution from scratch is labor‑intensive, so many turn to existing products such as HP ArcSight or IBM QRadar SIEM, though they are costly.

Free or open‑source alternatives include

ELK

(Elasticsearch, Logstash, Kibana) and

OSSIM

. ELK requires substantial setup, while OSSIM offers a more out‑of‑the‑box experience but limited localization.

Domestic solutions like

Secilog

provide a balanced approach, supporting protocols (syslog, SNMP, JDBC, FTP/SFTP), log formatting, alert generation, full‑text indexing, horizontal scaling, and a suite of built‑in alerts for password‑guessing, off‑hours logins, account enumeration, malicious file access, webshell detection, CC attacks, and more.

By configuring custom alert rules, organizations can also monitor business‑level anomalies such as abnormal API request patterns, large‑scale fraudulent orders, or other operational warnings.

Hope you find this guide useful.