Detection Engineering

A honeypot is a security resource whose value lies entirely in being attacked. It looks like a legitimate target — a database, an admin account, a file share, a misconfigured cloud key — but in reality it has no legitimate users, no real data, and one job: when someone touches it, raise an alarm. The first interaction is the alarm, and that’s why honeypots routinely deliver detection in minutes for techniques that signature-based EDR misses entirely. This guide answers what is a computer honeypot in 2026, walks through the practical taxonomy (low- vs high-interaction, production vs research), and shows the deployment patterns and SIEM integration that actually catch attackers rather than wasting blue-team time. ...

Most SIEMs fail not because the technology can’t keep up but because the detection content is bad. This guide walks through how a detection engineer actually thinks about a rule, from data onboarding to deployment. The Lifecycle Threat → Hypothesis → Data → Query → Tuning → Deploy → Measure → Retire Skip any step and you produce noise. ...

YARA is the pattern-matching language of malware analysis. Writing rules is easy; writing rules that survive the next sample variant is hard. This guide focuses on the second. The Rule Structure rule Example_Detection { meta: description = "Detects Example loader v3" author = "CyberSecurity Elite" date = "2026-04-14" hash = "f3b1...c7e9" reference = "https://example.com/report" tlp = "white" strings: $s1 = "C2_BEACON_HEADER" ascii wide $s2 = { 48 8b ?? ?? 48 89 ?? E8 [4] 85 C0 } $s3 = /api\/v[0-9]\/(checkin|exec)/ ascii nocase condition: uint16(0) == 0x5A4D and 2 of them } Three string types — text, hex with wildcards/jumps, and regex. The condition is what makes the rule precise. ...