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THREAT-Recon-AutomatedVulnScannerSweep IBM QRadar · QRadar

Detect Automated Vulnerability Scanner Sweep Against External Attack Surface in IBM QRadar

Before exploitation, most intrusions begin with active scanning of the target's external attack surface using automated vulnerability scanning tools such as sqlmap, Nikto, Nuclei, Acunetix, Nessus, OpenVAS, or WPScan. Attackers run these tools against internet-facing web applications, APIs, and edge devices to fingerprint software versions, enumerate misconfigurations, and identify exploitable CVEs before committing to an intrusion attempt. This scanning is typically noisy at the HTTP layer: high-volume requests for known vulnerable paths (e.g. /.env, /.git/config, /wp-login.php, /actuator/health, /phpmyadmin), distinctive scanner user-agent strings, and rapid sequential probing of many distinct URIs from a single source in a short window. Nation-state actors such as Volt Typhoon and APT41 routinely scan edge devices and web infrastructure ahead of exploitation attempts, while eCrime groups like FIN7 and Scattered Spider use commodity scanning frameworks to identify soft targets at scale. Detecting the scanning phase gives defenders a window to harden or patch before the attacker moves to Initial Access.

MITRE ATT&CK

Tactic
Reconnaissance

QRadar Detection Query

IBM QRadar (QRadar)
sql
SELECT
  DATEFORMAT(starttime, 'yyyy-MM-dd HH:mm') AS TimeWindow,
  sourceip AS SourceIP,
  destinationip AS DestinationIP,
  url AS RequestURL,
  LOGSOURCETYPENAME(devicetype) AS VendorType,
  COUNT(*) AS TotalRequests,
  COUNT(DISTINCT url) AS DistinctURIs
FROM events
WHERE
  LOGSOURCETYPENAME(devicetype) ILIKE ANY ('%F5%', '%Imperva%', '%FortiWeb%', '%Akamai%', '%Cloudflare%', '%NetScaler%')
  AND (
    LOWER(useragent) ILIKE ANY ('%sqlmap%', '%nikto%', '%nuclei%', '%acunetix%', '%nessus%', '%openvas%', '%wpscan%', '%dirbuster%', '%gobuster%')
    OR LOWER(url) ILIKE ANY ('%/.env%', '%/.git/config%', '%/wp-login.php%', '%/actuator/env%', '%/phpmyadmin%', '%/.aws/credentials%')
  )
  AND starttime >= NOW() - 86400000
GROUP BY
  DATEFORMAT(starttime, 'yyyy-MM-dd HH:mm'),
  sourceip,
  destinationip,
  url,
  LOGSOURCETYPENAME(devicetype)
ORDER BY TotalRequests DESC
medium severity medium confidence

Detects known vulnerability scanner user-agent strings or requests for well-known sensitive/vulnerable paths against WAF-protected web infrastructure (F5, Imperva, FortiWeb, Akamai, Cloudflare, NetScaler) within the last 24 hours.

Data Sources

F5 ASMImperva WAFFortiWebAkamai Kona Site DefenderCloudflare WAFCitrix NetScaler AppFirewall

Required Tables

events

False Positives & Tuning

  • Authorised internal or contracted vulnerability scanning not yet excluded via source IP allowlist
  • Synthetic monitoring services probing multiple endpoints for uptime checks
  • Security researchers or bug-bounty participants operating under an authorised program
  • Web crawlers indexing legitimately deprecated paths that happen to overlap sensitive-path patterns

Other platforms for THREAT-Recon-AutomatedVulnScannerSweep


Testing Methodology

Validate this detection against 3 adversary techniques from Atomic Red Team. Each test below lists the behaviour to exercise and the telemetry you should expect to see. Executable commands and cleanup steps are available with Pro.

  1. Test 1Vulnerability Scanner User-Agent Simulation via curl

    Expected signal: WAF/reverse-proxy logs record inbound requests with the scanner user-agent strings in the Message or http_user_agent field.

  2. Test 2Sensitive Path Probing Simulation

    Expected signal: WAF/reverse-proxy logs show requests to each sensitive path, mostly resulting in 404 responses.

  3. Test 3Behavioral URI Enumeration Simulation via ffuf-style Loop

    Expected signal: WAF/reverse-proxy logs record 40 distinct URIs requested from the test source IP within a single 5-minute window, nearly all returning 404.

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