Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
654 detections
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This rule detects potential vulnerability scanning activity by identifying multiple network connection attempts to common vulnerable ports (135, 139, 445) where the remote URL contains keywords like 'vuln' or 'scan'. It aggregates these activities by device and time, flagging devices with 5 or more such connections within an hour.
This rule identifies potential network scanning or high-volume communication by detecting a large number of connections (50 or more) between a single source IP and a single destination IP within a 5-minute time window. It aggregates connection details such as total connections, sent bytes, and received bytes from the CommonSecurityLog.
This rule detects potential port scanning activity from a single host. It identifies scenarios where a device attempts to connect to a large number of unique remote ports (20 or more) or a large number of unique remote IP addresses (25 or more) within a 10-minute window. This behavior is indicative of an adversary attempting to discover open ports or active hosts on a network.
This rule detects unusual network connection patterns characterized by a high volume of connections to a remote IP address within a 5-minute window, combined with anomalous connection durations. Specifically, it flags scenarios where there are more than 1000 connections and either the 95th percentile of connection durations is very short (less than 50ms) or the 5th percentile of connection durations is very long (greater than 30000ms). These patterns can indicate activities such as network scanning, denial-of-service attempts, or command and control communication.
Detects a device making an unusually high number of unique outbound network connections to different remote IP addresses within a short time frame (5 minutes). This behavior can indicate various malicious activities such as network scanning, botnet activity, or data exfiltration to multiple external hosts.
Suspicious PAN-OS Captive Portal Access
Cortex XDR
Detects anomalous or repetitive access to PAN-OS Captive Portal HTTP endpoints which may indicate active scanning for CVE-2026-0300 vulnerability exposure.
CVE-2026-0300 Scanner Tool User-Agent
Cortex XDR
Detects the use of the CVE-2026-0300 scanner tool by its distinct User-Agent string attempting to identify vulnerable PAN-OS Captive Portals.
This rule detects potential network reconnaissance activity by looking for keywords such as 'scan', 'sweep', 'probe', 'recon', 'nmap', and 'nessus' within the 'Message' field of CommonSecurityLog entries. This could indicate an adversary actively gathering information about the network infrastructure.
This rule identifies potential network scanning or high-volume communication by detecting a large number of connections (50 or more) between a single source IP and a single destination IP within a 5-minute time window. It aggregates connection details such as total connections, sent bytes, and received bytes from the CommonSecurityLog.
This rule detects potential internal network scanning activity by identifying a single source IP address that communicates with 15 or more distinct internal destination IP addresses within a 5-minute time window. This behavior is often indicative of reconnaissance efforts by an attacker attempting to map out the internal network.
This rule detects potential network port scanning activity by identifying a single source IP address attempting to connect to 10 or more distinct destination ports on a single destination IP address within a 5-minute window. This behavior is indicative of an attacker attempting to discover open services on a target host.
Requires Defender DeviceNetworkEvents table
Group together attempted connections from single remote IPs and bin them into time intervals. For each remote IP series find a best fit line and extend the slope measure. Using the slope attribute display the outliers.
Varying the timespan does change the results, long timespans will only display very noisy port scans whereas short timespans tend to produce more false positives.
Group together attempted connections from single remote IPs and bin them into time intervals. For each remote IP series find a best fit line and extend the slope measure. Using the slope attribute display the outliers.
Varying the timespan does change the results, long timespans will only display very noisy port scans whereas short timespans tend to produce more false positives.
Detects User-Agent rotation behavior from a single Source IP (c-ip) associated with more than 3 distinct
User-Agent (c-useragent) values within a 5-minute window. This behavior is characteristic of the Nuclei
'-random-agent' flag, which allows the scanner to bypass WAF (Web Application Firewall) detections by
using a different, valid browser User-Agent string for each request.
User-Agent (c-useragent) values within a 5-minute window. This behavior is characteristic of the Nuclei
'-random-agent' flag, which allows the scanner to bypass WAF (Web Application Firewall) detections by
using a different, valid browser User-Agent string for each request.
This rule detects brute force activity based on 'BruteForceActivityDetected' events from DeviceEvents. It identifies the attacking IP, the device being attacked, the credential being targeted, the workstation name, domain name, and the type of attack. It then summarizes the count of attacks per unique combination of these fields, ordered by the attack count.
This rule detects potential port scanning activity from a single host. It identifies scenarios where a device attempts to connect to a large number of unique remote ports (20 or more) or a large number of unique remote IP addresses (25 or more) within a 10-minute window. This behavior is indicative of an adversary attempting to discover open ports or active hosts on a network.
This rule detects the execution of multiple common network discovery commands within a short time frame. This behavior can indicate reconnaissance activities aimed at understanding network topology, identifying active hosts, and discovering network services.
Detects a high frequency of ICMP Echo Request (Type 8) packets, which could indicate a Denial of Service (DoS) attack.
Detects exploitation attempts and worm-like scanning behavior targeting Next.js applications, characteristic of the EtherRAT malware. The rule identifies web requests with the 'x-middleware-subrequest' header (CVE-2025-29927) or scanning of sensitive paths like '/_next/data/' from internal sources.
Detects high-volume vulnerability and spyware threat activity originating from external (UNTRUSTED) sources targeting public-facing IP addresses.
This detection identifies potential exploitation attempts, scanning activity, and malicious payload delivery by monitoring PAN-OS threat logs where severity is not informational and the subtype indicates vulnerability or spyware.
The rule triggers when:
A source IP generates ≥100 reset-both actions within 1 hour (indicative of blocked exploit or aggressive scanning), or
A source IP generates ≥10 non-reset actions within 1 hour (indicative of potentially successful or partially successful attempts)
Enrichment includes ASN, country, organization, and city to support attribution, threat hunting, and blocking decisions.
This detection identifies potential exploitation attempts, scanning activity, and malicious payload delivery by monitoring PAN-OS threat logs where severity is not informational and the subtype indicates vulnerability or spyware.
The rule triggers when:
A source IP generates ≥100 reset-both actions within 1 hour (indicative of blocked exploit or aggressive scanning), or
A source IP generates ≥10 non-reset actions within 1 hour (indicative of potentially successful or partially successful attempts)
Enrichment includes ASN, country, organization, and city to support attribution, threat hunting, and blocking decisions.
Detects enumeration of local or remote network services.
Detects API reconnaissance and endpoint scanning activity against Ollama servers by identifying sources probing multiple API endpoints within short timeframes, particularly when using HEAD requests or accessing diverse endpoint paths, which indicates systematic enumeration to map the API surface, discover hidden endpoints, or identify vulnerabilities before launching targeted attacks.
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