Analytical Model - Tags as Analysis
Research and analysis consists of collecting and evaluating data and drawing conclusions based on that available data. Assuming data is initially collected and recorded (modeled) accurately within a Cortex, the underlying data (typically encoded in nodes and their properties) should not change. However, as you collect more data or different types of data, or as you re-evaluate existing data, your assessment of that data - typically encoded in tags - may change. Nodes and properties are meant to be largely stable; tags are meant to be flexible and evolving.
Every knowledge domain has its own focus and set of questions it attempts to answer. The “answers” to some of these questions can be recorded as tags applied to relevant nodes in a Cortex.
While the Synapse data model related to tags is straightforward (consisting of only the single
syn:tag form), the appropriate use of tags for data annotation is more complex. Tags can be thought of as being part of an analytical model that relies on the Synapse data model, but that:
Exists largely independently from the data model. You do not need to write code to implement new tags or design a tag structure; you simply need to create the appropriate
Is knowledge domain-specific. Tags used for cyber threat analysis will be very different from tags used for biomedical research.
Is tightly coupled with the specific analytical questions the Synapse hypergraph is intended to answer. The questions you want to answer should dictate the tags you create and apply.
In short, effective use of Synapse to conduct analysis is dependent on:
The data model: how you define types, forms, and properties to represent data within your knowledge domain.
The analytical model: how you design a set of tags to annotate data within your knowledge domain.
A well-designed tag structure should:
Represent relevant observations. Tags should annotate assessments and conclusions that are important to your analysis.
Facilitate effective analysis. Tags should be structured to allow you to ask meaningful questions of your data.
The sections below provide a few examples of the types of observations and analysis that can be represented by tags.
Note that these are meant simply to illustrate a few potential “real-world” applications for tags - there is no “right” or “wrong” tag hierarchy (although there are “better” and “worse” ways to design tag hierarchies that may impact your ability to answer analytical questions efficiently).
See Design Concepts - Analytical Model for considerations in designing a set of tags and tag hierarchies.
See Design Concepts - Forms vs. Tags for considerations on whether something should be modeled as a form, a property, or a tag.
The Synapse data model itself is meant to represent objective information related to a given knowledge domain - observables that are verifiable and not in dispute. An IP address is an IP address - there should be no disagreement or debate whether an IP “exists” or is part of a given Autonomous System (AS), for example.
Not all details about objects are relevant to all knowledge domains. An IP address is an IP address in any data model. Whether that IP address is a Tor node or a shared web host or an anonymous proxy may be irrelevant to someone using that data model to analyze Internet traffic flow and patterns, for example. However, those additional facts may be very relevant to someone using the model to analyze cyber threat data.
These additional details can be considered to be “domain-relevant facts”. That is, they are domain-relevant in that they are pertinent to certain types of analysis attempting to answer certain types of questions. They are facts in that an IP is either a Tor node or it isn’t, although your determination as to whether it is a Tor node may include some degree of assessment or evaluation. For example, you can verify directly that the IP is a Tor node if you have access to the host using the IP. Alternately, you can verify indirectly via a trusted third party (“Foo Tor Tracking Service says this is a Tor node”), or infer that the IP is a Tor node from other evidence available to you.
One option to record this information is to encode it in a set of tags that can be applied to the relevant nodes. The advantages of using tags include:
Tags eliminate the need to record an excessive number of secondary properties within the data model that may only be relevant to a subset of users.
You have the flexibility to create a tag structure appropriate to the analytical questions you need to ask.
You can easily apply, modify, or remove tags if data changes (i.e., an IP may be a Tor node for a period of time and then be reconfigured to no longer host that service).
You can apply time boundaries or additional properties to the observations if necessary (i.e., you can apply a timestamp / date range to the Tor tag to show when the IP was a Tor node).
When creating a set of domain-specific tags, it may be useful to structure them under a root tag representing that knowledge domain. For example, the root tag
cno could be used as the root for various domain-specific tags pertaining to computer network operations / cyber threat data. For example:
infra element under
cno denotes that these tags all relate to infrastructure. The
anon sub-tag specifies anonymous infrastructure (tor, anonymous proxies, etc.) and so on.
The purpose of analysis is to draw relevant conclusions from the data at hand. The specific analytical conclusions will vary based on the knowledge domain but could include assessments such as “The increase in widget manufacturing due to lower production costs has had a negative effect on the demand for gizmos” or “The threat group Vicious Wombat is working on behalf of the Derpistan government”.
Those big-picture assessments are made based on numerous smaller assessments (tags) which are themselves based on the observables (nodes) encoded in the hypergraph. However, to build up to those larger assessments, you must start by recording those smaller domain-specific assessments within the Synapse hypergraph.
The folowing examples from the knowledge domain of cyber threat data illustrate the types of assessments that can be recorded using tags:
A common practice in threat tracking and cyber security involves determining not only whether an indicator (e.g., a file, domain, IP address, or email address) is malicious, but whether it is part of a threat cluster. That is, whether the indicator can be linked to other indicators (e.g., from the same indcident or intrusion) to create a known set of related activity. “Threat clusters” of related indicators may be built up over time to represent a broader set of activity presumed to be carried out by some (generally unknown) set of malicious actors (a “threat group”).
An analyst researching an unknown indicator - such as a newly-identified domain - will evaluate a variety of data to determine whether the domain can be linked to a known threat cluster. This may include:
whether any malware is associated with the domain
current and historical domain registration (whois) data
current and historical domain resolution / DNS data
If the analyst determines that there is sufficient evidence to link the domain to an existing threat cluster, it is helpful to record that assessment. Not only does this make the assessment available to other analysts, it also means that other analysts do not need to spend time evaluating the same or similar data to come to the same conclusion (barring any new data that prompts a re-evaluation of the assessment).
A set of tags can be used to denote that nodes are part of or associated with a given threat cluster, such as:
The value of
<cluster> may vary depending on an organization’s method to distinguish different clusters (i.e., naming convention, numbering system, etc.)
Tactics, Techniques, and Procedures (TTPs)
The methodologies (sometimes known as tactics, techniques, and procedures or TTPs) that a threat group uses to conduct its activity can provide insight into the group and its operations. Knowledge of past TTPs may help predict future actions or operations. Sets of TTPs observed together may provide a “fingerprint” of a group’s activity. General knowledge of TTPs in current use can help organizations more effectively protect and defend their assets.
“TTP” can cover a broad range of observed activity, from whether a group uses zero-day exploits to the specific packer used to obfuscate a piece of malware. A simple example of a TTP is whether a group uses “masquerading” - imitating a legitimate resource such as a valid domain name or a trusted email sender - to facilitate an attack. A masquerade is a social engineering technique intended to gain the potential victim’s trust, making them more likely to visit a web site or open an email attachment.
An analyst evaluating whether a domain imitates the name of a legitimate company or service for malicious purposes may first note the domain’s similarity with that of a known company, and then evaluate additional information such as:
whether the similar domain is actually registered to the legitimate company (as a less well-known site, or a domain registered for purposes of brand protection).
whether the similar domain is associated with known malicious activity.
whether any malicious activity appeared targeted at individuals who would have a personal or professional interest in the legitimate site that the similar domain imitates.
If the analyst determines that the similar domain is not associated with the legitimate site or company, and that the domain appears to have been crafted for malicious use, a tag can be used to note this assessment. For example:
A node (such as a domain) is meant to imitate a legitimate resource associated with Google:
Some third-party data sources provide both data and tags or labels associated with that data. For example, Shodan may provide both data on an IPv4 address (such as which ports were open on the IPv4 as of the last Shodan scan) as well as tags such as
vpn. Similarly, VirusTotal may provide metadata and multiscanner data for files along with tags such as
Similarly, many commercial organizations conduct their own threat tracking and analysis and publish their research on cyber threats. From blogs to white papers, this type of research commonly includes “indicators of compromise” (hashes, domains, IP addresses, etc.). These indicators are (at minimum) purportedly malicious and may also be associated with named malware families, ‘campaigns’, or threat groups.
Both Shodan’s assertion (via the explicit use of a tag) that an IPv4 address hosted a VPN, or ESET’s assertion that a SHA1 hash is associated with the X-Agent malware family (via a summarized list of categorized indicators) are assertions made by others - by third-parties.
Third-party assertions can themselves act as valuable data (the fact that “Shodan says this IPv4 hosts a VPN” can be a useful point in your analysis). However, it is important to consider that you may not have direct access to sufficient data to verify the third-party assertion. This means that whether or not to accept the assertion at face value may be a matter of how much you trust the third-party in question. So where should tags for third-party assertions live?
One option would be to record your own assertions and other organizations’ assertions using the same set of tags. In other words, if Shodan says an IPv4 hosts a VPN, and you analyze a second IPv4 and conclude that it hosts a VPN, you could use the same tag for both assertions.
This might seem like a good idea - it gives you fewer tags too work with. However, you lose the ability to distinguish between assertions you made based on your own data and analysis (which are presumably higher confidence) and assertions someone else made based on unknown data. In addition, there is overhead involved in trying to map multiple sets of “other people’s tags” to your tags - assuming a 1:1 mapping even exists (and some third-party use tags without documenting their specific meanings!).
For these reasons, it may be preferable to record your own assertions in one (potentially domain-specific) tag tree, and assertions made by others in one or more separate tag trees. This still gives you the context of what “other people” say about something, but you can distinguish what “other people” say from what you assess independently. It also allows you to more easily see where contradictory assertions exist - perhaps you assess that an IPv4 address is a sinkhole, while another organization claims it is malicious infrastructure. Knowing that these discrepancies exist gives you the opportunity to deconflict them.
A set of tags can be used to annotate “other people’s analysis”, under a root tag such as
rep (for “reported by”):
FireEye says this MD5 hash is associated with APT1:
Shodan says this IPv4 hosts a VPN::
VirusTotal says this file is a PE executable:
Tags as Hypotheses
Another way to look at tags is as hypotheses. If a tag represents the outcome of an assessment, then every tag can be seen as having an underlying question or hypothesis it is attempting to answer. Making the decision to apply the tag equates to assessing the tag’s underlying hypothesis to be true. Making these assessments often involves the judgment of a human analyst; hence evaluating and tagging data within the hypergraph is one of the primary analyst tasks.
Hypotheses may be simple or complex; most often individual tags represent relatively simple concepts that are then used collectively to support (or refute) more complex theories. Because the concept of encoding assessments, judgments, or analytical conclusions within a graph or hypergraph may be unfamiliar to some, a few examples may be helpful.
The broad cyber threat question “can this newly identified domain be associated with any known threat cluster?” can be thought of as comprised of n number of individual hypotheses based on the number of known threat clusters:
Hypothesis: This domain is associated with Threat Cluster 1.
Hypothesis: This domain is associated with Threat Cluster 2.
Hypothesis: This domain is associated with Threat Cluster n.
If an analyst determines that the domain is associated with Threat Cluster 46, placing a Threat Cluster 46 tag (e.g.,
cno.threat.t46) on the node for that domain effectively means that the hypothesis “This domain is associated with Threat Cluster 46” has been assessed to be true.
The criteria used to evaluate whether an indicator is part of a threat cluster may be complex. Tags (and their underlying hypotheses) can also represent concepts that are simpler (easier to evaluate). The use of “masquerading” as a TTP is one example.
Let’s say an analyst comes across the domain
g00gle.com, which bears a resemblance to the legitimate
google.com domain. The mere similarity is not enough to determine whether the similar domain is malicious or used for malicious purposes. However, the analyst may have access to additional data (such as a phishing email with a link to a
g00gle.com web site that prompts the user to enter their password). The analyst determines that the domain is malicious and likely intended for credential theft. Applying the tag
cno.ttp.se.masq.google effectively means that the hypothesis “A threat actor created this domain to imitate Google for malicious purposes” has been assessed to be true.
Individual Hypotheses to Broader Reasoning
More complex hypotheses may not be explicitly annotated within the graph (that is, as tags applied to individual nodes), but may be supported (or refuted) by the presence of individual tags or combinations of tags on sets of nodes.
For example, an analyst tracking Threat Cluster 12 believes (has a hypothesis) that they frequently register domains that imitate technology companies. In the absence of a detailed data modeling and tracking system (such as a Synapse hypergraph), such an assessment is often made based on an analyst’s “impression” of historical Threat Cluster 12 data / domains.
A better way to make this determination based on tracked data and assessments would be to:
review all of the domains associated with Threat Cluster 12 (i.e., tagged
determine how many of those domains have
determine the types of organizations represented by the
masqtags (technology, media, government, etc.)
This allows you to determine the number or percentage of known Threat Cluster 12 domains that represent masquerades, and what types of masquerades they represent, providing a much more concrete basis to evaluate your hypothesis than the recollection of a “subject matter expert” or an impression gleaned from looking at a list of domains.