Most organizations collect logs. Few actually analyze them effectively. The difference between compliance-driven log collection and genuine threat hunting isn't the volume of data you store—it's the analytical techniques you apply to that data.

Security teams drowning in alerts often miss active intrusions hiding in plain sight. The problem isn't insufficient logging. It's that searching for known-bad signatures only catches attackers using yesterday's techniques. Sophisticated adversaries blend into normal operations, making traditional detection approaches ineffective.

The techniques that actually find attackers share a common philosophy: they focus on behavior rather than indicators. By understanding what normal looks like, establishing statistical baselines, and correlating events across time, analysts can surface the subtle anomalies that reveal human adversaries operating within networks. These methods require more effort than signature matching, but they find threats that automated tools miss entirely.

Signature-based detection asks a simple question: does this event match a known threat? Behavioral analysis asks something more powerful: does this event fit the pattern of normal activity? The second approach catches attackers regardless of their specific techniques, because adversary behavior inherently differs from legitimate operations.

Building effective baselines requires understanding your environment at a granular level. Start with authentication patterns—when do users typically log in, from which systems, using which protocols? A domain administrator authenticating at 3 AM from a workstation they've never touched before might be legitimate, but it warrants investigation. The same authentication during business hours from their usual system is noise.

Process execution baselines prove particularly valuable. Most servers run predictable software. A web server executing PowerShell encoded commands stands out against a baseline of Apache and MySQL processes. Workstations show more variation, but even there, patterns emerge. Finance users don't typically run network reconnaissance tools.

The critical insight is that baselines must be contextual. Global baselines generate excessive false positives because they ignore legitimate variation between user roles, system functions, and business units. Build separate baselines for server types, user populations, and network segments. This contextual approach dramatically improves signal-to-noise ratio while maintaining detection capability for genuinely anomalous activity.

Takeaway

Attackers can evade signatures, but they cannot perfectly mimic the behavioral patterns of legitimate users and systems they've compromised. Build context-specific baselines, then hunt for deviations that signatures would never catch.

When you're examining millions of log entries, the mathematically rare events deserve attention first. Stack counting—grouping identical events and counting their frequency—transforms overwhelming data volumes into prioritized investigation queues. Events occurring once or twice among millions of similar entries often represent attacker activity or misconfigurations worth examining.

Apply stack counting to process execution logs first. Export all process names, parent-child relationships, or command-line arguments from your environment over a week. Sort by frequency. The processes running on thousands of systems are almost certainly legitimate. The process appearing on exactly one system, launched by an unusual parent, executed with encoded arguments? That's your investigation starting point.

Network connections respond well to similar analysis. Stack destination IPs, ports, or user-agent strings. Legitimate business operations generate consistent, high-frequency connections to known resources. Attacker command-and-control infrastructure typically appears as low-frequency outliers—connections to unusual destinations that don't match normal business patterns.

The technique scales across log types. Stack authentication source IPs to find unusual login locations. Stack file access patterns to identify reconnaissance activity. Stack DNS query destinations to surface potential data exfiltration or C2 channels. Each application of the method transforms thousands of events into ranked lists where statistical outliers bubble to the top, directing analyst attention to the events most likely to represent malicious activity.

Takeaway

When facing millions of log entries, count frequencies and investigate the rare events first. Attackers generate statistically unusual activity that stack counting surfaces naturally, even when you don't know what specific malicious behavior to search for.

Individual security alerts rarely tell complete stories. A single failed authentication attempt means nothing. That same failed attempt, followed by successful authentication, followed by privilege escalation, followed by lateral movement—that's an attack narrative. Temporal correlation links discrete events into sequences that reveal adversary operations.

Start with your highest-fidelity alerts and work outward in time. When you identify a confirmed malicious event, query for all activity from that user account, source IP, or compromised system across the preceding days. Attackers rarely compromise systems and immediately achieve their objectives. The dwell time between initial access and objective completion creates a trail of connected events.

Effective temporal correlation requires common fields across log sources. Ensure your authentication logs, process execution logs, network flow data, and security tool alerts share normalized timestamps, user identifiers, and host names. Without these connective fields, events remain isolated islands rather than linked components of a coherent timeline.

Build correlation queries that answer operational questions. After detecting credential theft, ask: what systems did this account authenticate to in the following 72 hours? After identifying malware execution, ask: what processes did this malware spawn, and what network connections did those processes establish? Each query extends the investigation timeline, often revealing lateral movement, persistence mechanisms, and data access that individual alerts never surfaced.

Takeaway

Single alerts miss context. Build queries that extend investigations backward and forward in time, linking events through shared users, systems, and timestamps to reconstruct the complete attack narrative from scattered log evidence.

Log analysis that finds attackers requires moving beyond the search for known-bad indicators. Behavioral baselines expose activity that doesn't fit normal patterns. Stack counting surfaces statistical outliers hiding among millions of routine events. Temporal correlation links isolated alerts into coherent attack narratives.

These techniques share a common requirement: they demand analyst investment. You must understand your environment to build meaningful baselines. You must process and sort data to apply stack counting. You must write queries that extend investigations across time.

The investment pays returns that signature-based detection cannot match. Sophisticated attackers evade known signatures, but they cannot perfectly replicate legitimate behavior, eliminate their statistical footprint, or prevent their activities from appearing in chronological sequence. Master these analytical techniques, and your logs become windows into adversary operations rather than compliance checkboxes.