View exploit activity on the CVEs related to Pacific Rim
Threat Hunters have to spend an inordinate amount of time searching through lists of IOCs, logs, sandbox files, and more just to find a thread that could lead them to useful information. And even then, they can’t be too sure that their findings are indeed accurate. GreyNoise can help alleviate some of this pain by helping Threat Hunters save time and find the needle in the noise.
Threat hunting takes time. Threat hunters spend a significant portion of their time searching through security logs to try to find evidence that a system has been compromised. After running searches in their SIEM, threat hunters end up with lists of potential Indicators of Compromise (IoCs) that they then need to sort, filter, and narrow down. Whether they’re investigating a specific incident or searching for patterns of malicious activity, processing this data efficiently can have a tremendous impact on investigating incidents in a timely manner.
Threat hunters also have to deal with data integrity and usefulness; knowing if they can trust their external threat intelligence is crucial to any investigation. Threat hunters working with thousands of log sources in their SIEM must correctly identify IoC outliers and rule out data that is irrelevant. Filtering the noise first makes the remaining data more interesting and impactful.
We can’t promise to make all of your dreams come true, but we can promise that we will save you time with data you can trust.
GreyNoise provides visibility and deep context on network traffic by identifying the intention of observed activity on internet wide scans. This lets threat hunters focus on events that are targeting their organization and reduce time wasted on triage of harmless or irrelevant events, giving time back to dedicate to hunting adversaries.
Proactive threat hunting uses a variety of methods and data sources in order to drive a hunting campaign. Hunting for unknowns in an environment can be challenging without the right set of data. Every day hundreds of thousands of devices scan, crawl, and probe every routable IP address on the internet looking for vulnerabilities and misconfigurations. GreyNoise provides additional information about activity from a particular IP address or ASN and tracks trending or anomalous activity as threats emerge. Anomalous behavior quickly gives analysts a way to review traffic observed by GreyNoise sensors that deviates from previously observed activity. Being able to conceptualize an attacker's early-stage attack infrastructure as threats emerge provides a window of opportunity for threat hunters to start targeted and specific investigation. Why are actors looking for these devices suddenly? Are similarly vulnerable devices in my organization and exposed to the internet?
GreyNoise’s internet-wide sensor network passively collects packets from hundreds of thousands of IPs seen scanning the internet every day. Companies like Shodan and Censys, as well as researchers and universities, scan in good faith to help uncover vulnerabilities for network defense. Others scan with potentially malicious intent. GreyNoise analyzes and enriches this data to identify behavior, methods, and intent, giving analysts the context they need to take action.
Additionally, GreyNoise tracks IP’s associated with common business applications (e.g. Microsoft O365, Google Workspace, and Slack), or services like CDNs and public DNS servers. These applications communicate through unpublished or dynamic IPs making it difficult for security teams to track. Without context, this harmless behavior distracts security teams from investigating true threats.
Data collected by GreyNoise sensors can be used to enrich log events on perimeter and public, internet-facing devices in your environment in addition to helping determine if this activity is something that is happening across the internet or is something that may be targeted specifically at your organization. Data related to common business applications is often used to filter outbound traffic leaving your network and can be useful for determining traffic that is going to known services so that your focus can be on the connections going to unknown IPs.
This scan and attack data can be applied in a number of ways depending on the outcomes desired by a security team. Whether it is a large organization with robust infrastructure or MSSP’s providing threat hunting services to their customers, many teams are using different tools in order to build a robust cyber threat intelligence organization to support internal teams or customers. Collaboration is key to providing more relevant information to GreyNoise users and the security community at large. IP’s observed by the GreyNoise sensor network are enriched with additional information sources, such as, if an IP is a known Tor exit node or if the IP is used by a commercial VPN provider. GreyNoise participates in information sharing organizations and contributes data to strategic partnerships in an effort to provide and receive information on emerging threats as soon as they come into play.
Listening to the internet allows GreyNoise to uncover unique behaviors and TTPs. Sensors capture vulnerability lifecycles to show when scanners are looking for opportunities to exploit recently announced vulnerabilities. By capturing data in this way GreyNoise data generally has a low false positive rate by viewing the traffic inbound to the sensor network. Using this data allows for
From the largest organizations to the smallest security teams, typically have some method of centralizing their logs from systems and applications. Additionally as teams scale additional tools such as SOAR platforms or TIPs might be leveraged to better track and act upon indicators gathered. Collecting IOC’s is only half of teh battle; making the data actionable in an organization can be accomplished using integrations to further enrich data used for hunting and investigations.
Key GN Features or Capabilities:
Analysis of similarity / differences in IPs through the GreyNoise IP Dest and Timeline features + other existing features (bulk search / analysis, IP similarity, IP comparison)
Threat Hunters have to spend an inordinate amount of time searching through lists of IOCs, logs, sandbox files, and more just to find a thread that could lead them to useful information. And even then, they can’t be too sure that their findings are indeed accurate. GreyNoise can help alleviate some of this pain by helping Threat Hunters save time and find the needle in the noise.
Threat hunting takes time. Threat hunters spend a significant portion of their time searching through security logs to try to find evidence that a system has been compromised. After running searches in their SIEM, threat hunters end up with lists of potential Indicators of Compromise (IoCs) that they then need to sort, filter, and narrow down. Whether they’re investigating a specific incident or searching for patterns of malicious activity, processing this data efficiently can have a tremendous impact on investigating incidents in a timely manner.
Threat hunters also have to deal with data integrity and usefulness; knowing if they can trust their external threat intelligence is crucial to any investigation. Threat hunters working with thousands of log sources in their SIEM must correctly identify IoC outliers and rule out data that is irrelevant. Filtering the noise first makes the remaining data more interesting and impactful.
We can’t promise to make all of your dreams come true, but we can promise that we will save you time with data you can trust.
GreyNoise provides visibility and deep context on network traffic by identifying the intention of observed activity on internet wide scans. This lets threat hunters focus on events that are targeting their organization and reduce time wasted on triage of harmless or irrelevant events, giving time back to dedicate to hunting adversaries.
Proactive threat hunting uses a variety of methods and data sources in order to drive a hunting campaign. Hunting for unknowns in an environment can be challenging without the right set of data. Every day hundreds of thousands of devices scan, crawl, and probe every routable IP address on the internet looking for vulnerabilities and misconfigurations. GreyNoise provides additional information about activity from a particular IP address or ASN and tracks trending or anomalous activity as threats emerge. Anomalous behavior quickly gives analysts a way to review traffic observed by GreyNoise sensors that deviates from previously observed activity. Being able to conceptualize an attacker's early-stage attack infrastructure as threats emerge provides a window of opportunity for threat hunters to start targeted and specific investigation. Why are actors looking for these devices suddenly? Are similarly vulnerable devices in my organization and exposed to the internet?
GreyNoise’s internet-wide sensor network passively collects packets from hundreds of thousands of IPs seen scanning the internet every day. Companies like Shodan and Censys, as well as researchers and universities, scan in good faith to help uncover vulnerabilities for network defense. Others scan with potentially malicious intent. GreyNoise analyzes and enriches this data to identify behavior, methods, and intent, giving analysts the context they need to take action.
Additionally, GreyNoise tracks IP’s associated with common business applications (e.g. Microsoft O365, Google Workspace, and Slack), or services like CDNs and public DNS servers. These applications communicate through unpublished or dynamic IPs making it difficult for security teams to track. Without context, this harmless behavior distracts security teams from investigating true threats.
Data collected by GreyNoise sensors can be used to enrich log events on perimeter and public, internet-facing devices in your environment in addition to helping determine if this activity is something that is happening across the internet or is something that may be targeted specifically at your organization. Data related to common business applications is often used to filter outbound traffic leaving your network and can be useful for determining traffic that is going to known services so that your focus can be on the connections going to unknown IPs.
This scan and attack data can be applied in a number of ways depending on the outcomes desired by a security team. Whether it is a large organization with robust infrastructure or MSSP’s providing threat hunting services to their customers, many teams are using different tools in order to build a robust cyber threat intelligence organization to support internal teams or customers. Collaboration is key to providing more relevant information to GreyNoise users and the security community at large. IP’s observed by the GreyNoise sensor network are enriched with additional information sources, such as, if an IP is a known Tor exit node or if the IP is used by a commercial VPN provider. GreyNoise participates in information sharing organizations and contributes data to strategic partnerships in an effort to provide and receive information on emerging threats as soon as they come into play.
Listening to the internet allows GreyNoise to uncover unique behaviors and TTPs. Sensors capture vulnerability lifecycles to show when scanners are looking for opportunities to exploit recently announced vulnerabilities. By capturing data in this way GreyNoise data generally has a low false positive rate by viewing the traffic inbound to the sensor network. Using this data allows for
From the largest organizations to the smallest security teams, typically have some method of centralizing their logs from systems and applications. Additionally as teams scale additional tools such as SOAR platforms or TIPs might be leveraged to better track and act upon indicators gathered. Collecting IOC’s is only half of teh battle; making the data actionable in an organization can be accomplished using integrations to further enrich data used for hunting and investigations.
Key GN Features or Capabilities:
Analysis of similarity / differences in IPs through the GreyNoise IP Dest and Timeline features + other existing features (bulk search / analysis, IP similarity, IP comparison)
