Threat Hunting

Needle in the haystack. Found

Quickly identify anomalous behavior and catch targeted threats

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Overview

Effective threat hunting isn't just about finding more data, it’s about finding the right data. GreyNoise empowers your hunt team to adopt the PEAK Framework by correlating your internal traffic against our real-time map of internet-wide mass scanning.

By using GreyNoise to filter out opportunistic probes, benign scanners, and botnet noise, you reveal the statistically significant anomalies that represent targeted attacks. Stop chasing false positives and focus on the signals that actually threaten your perimeter.

How GreyNoise
Helps You Hunt Smarter

Focus effort on highest risks

Eliminate time-consuming research of benign and opportunistic scanning, allowing hunters to focus on infrastructure actually used by threat actors.

Supports threat research and hypothesis development

Hunters can use GreyNoise to conduct threat research, validate assumptions, and explore attack vectors in order to develop hypotheses.

Correlate isolated incidents

GreyNoise helps threat hunters link isolated incidents to larger campaigns by mapping attacker infrastructure and patterns, connecting logged IPs to those exploiting relevant vulnerabilities.

How GreyNoise Maps to the PEAK Hunting Framework

Explore Available Fields

Filter by category & search available IP fields and their uses with GreyNoise.
Categories
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NAME
Description & Use
ASN
Autonomous System Number routing the IP. Helps group malicious infrastructure.
ASN Count
Count of IPs grouped by ASN. Supports ASN-level threat analysis.
Actor
Known or attributed owner/operator of the IP (e.g., research org, ISP, hosting provider). Useful for attribution.
Actor Count
Aggregated count of IPs per actor. Supports statistical analysis of actors.
Bot
Flags whether the IP is part of known botnet activity. Helps detect automated scanning or malware distribution.
CVE
List of CVEs the IP has scanned for or attempted to exploit. Critical for vulnerability triage.
CVEs
CVEs tied to the tag behavior. Critical for identifying exploitation of known vulnerabilities.
Category
High-level network type (e.g., hosting, ISP, enterprise).
Category Count
Aggregated count of IPs per category (hosting, ISP, etc.). Highlights infrastructure trends.
City
Registered city of the IP. Useful for geolocation context and pivoting.
Classification
GreyNoise’s judgment of the IP’s intent: benign, malicious, suspicious, or unknown. Most useful filter for triage.
Classification Count
Aggregated count of IPs per classification. Useful for threat landscape analysis.
Created At
Date the tag was first added. Indicates when this behavior was first observed.
Description
Human-readable explanation of what the tag represents. Adds analyst context.
Destination ASNs
List of ASNs targeted by the IP. Helps show which networks are being scanned.
Destination Cities
List of cities where scanning traffic was observed. Useful for geo-targeting analysis.
HTTP Method
HTTP methods used (e.g., GET, POST). Provides context on attacker request.
HTTP Path
Web paths targeted during scanning (e.g., /robots.txt, /admin). Reveals reconnaissance goals.
HTTP Request Authorization
Authorization headers observed in HTTP requests. May reveal brute-force attempts.
HTTP Request Cookies
Cookies included in HTTP requests. Adds context on reconnaissance or exploit attempts.
HTTP Request Headers
Headers used in HTTP requests. Useful for tool fingerprinting.
HTTP Request Origin
Origin IPs or addresses set in HTTP headers. May indicate spoofing.
HTTP Useragent
User-Agent strings observed. Useful for identifying attacker tools or crawlers.
Hassh Fingerprint
Fingerprint hash of SSH client behavior. Helps identify SSH attack tools.
Hassh Port
Port associated with observed SSH behavior. Adds protocol context.
ID
Unique tag identifier.
ID
Unique identifier for the record. Used to track and reference the vulnerability consistently across systems and reports.
IP
The observed IP address itself. Primary entity to investigate or correlate across alerts.
Intention
Tag’s intent classification: benign, malicious, suspicious, or unknown. Adds risk context.
JA3 Fingerprint
JA3 TLS fingerprint of client behavior. Useful for identifying attack tools, actors, botnets, and campaigns.
JA3 Port
Port associated with observed JA3 TLS activity.
Last Seen
Last date the IP was observed by GreyNoise sensors. Indicates recency of activity.

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