Synapse is a versatile central intelligence and analysis system created to support analyst teams in every stage of the intelligence life cycle. We designed Synapse to answer complex questions which require the fusion of large data sets from a broad range of sources that span multiple disciplines. Analysis is based on representing all data in a structured model that allows analysts or algorithms to query, annotate, navigate, and reason over the collected data.
See Synapse’s Key Features for an overview of Synapse’s advantages!
Synapse is based on a proven methodology informed by real-world experience. Synapse grew out of the need to track a complex, diverse, and very large data set: namely, cyber threat data. Synapse is the successor to the proprietary, directed graph-based analysis platform (Nucleus) used within Mandiant to produce the APT1 Report.
Synapse and its predecessors were designed from the beginning to support the following critical elements:
The use of a shared analytical workspace to give analysts access to the same data and assessments in real time.
The principle that relationships among and conclusions about data should be self-evident. That is, to the extent possible, data and analytical findings must be represented so that analysis captured within the system should “speak for itself”.
These features give Synapse the following advantages:
Synapse allows (and requires) analysts to “show their work” in a reasonably concise manner. Analysts should not have to refer to long-form reporting (or rely on the unquestioned word of a subject matter expert) to trace a line of analytical reasoning.
Synapse allows analysts to better review and validate their findings. Conflicting analysis is highlighted through the structure of the data itself. Analysis can readily be questioned, reviewed, deconflicted, and ultimately improved.
Because Synapse’s knowledge store is continually expanded, updated, and revised, it always represents the current, combined understanding of its data and analysis. Unlike prose reports or tickets, Synapse is never stale or outdated.
Synapse’s hypergraph design addresses many of the shortcomings we identified with earlier directed graph and prototype hypergraph systems. In addition, because our experience taught us the power of a flexible analysis platform over any large and disparate data set, Synapse has been designed to be flexible, modular, and adaptable to any knowledge domain - not just cyber threat data.
Many of the real-world examples in this User Guide reference data from the fields of information technology or cyber threat intelligence, given Synapse’s history. But Synapse’s structures, processes, and queries can be applied to other knowledge domains and data sets. The intent of Synapse is that any data that could be represented in a spreadsheet, database, or graph database can be represented in Synapse using an appropriate data model.
Graphs and Hypergraphs
To understand the power of Synapse, it helps to have some additional background. Without delving into mathematical definitions, this section introduces key concepts related to a hypergraph, and contrasts them with those of a graph or a directed graph. Most people should be familiar with the concept of a graph – even if not in the strict mathematical sense – or with data that can be visually represented in graph form.
A graph is a mathematical structure used to model pairwise relations between objects. Graphs consist of:
vertices (or nodes) that represent objects, and
edges that connect two vertices in some type of relationship.
Edges connect exactly two nodes; they are “pairwise” or “two-dimensional”. Both nodes and edges may have properties that describe their relevant features. In this sense both nodes and edges can be thought of as representational objects within the graph: nodes typically represent things (“nouns”) and edges typically represent relationships (“verbs”).
Cities and Roads. A simple example of data that can be represented by a graph are cities connected by roads. If abstracted into graph format, each city would be a vertex or node and a road connecting two cities would be an edge. Since you can travel from City A to City B or from City B to City A on the same road, the graph is directionless or undirected.
Social Networks. Another example is social networks based on “connections”, such as LinkedIn. In this case, each person would be a node and the connection between two people would be an edge. In most cases, LinkedIn requires a mutual connection (you must request a connection and the other party must accept); in this sense it can be considered a directionless graph. (This is a simplification, but serves our purpose as an example.)
A directed graph is a graph where the edges have a direction associated with them. In other words, the relationship represented by the edge is one-way. Where an edge in an undirected graph is often represented by a straight line, an edge in a directed graph is represented by an arrow.
Cities and Roads. In our cities-and-roads example, the graph would be a directed graph if the roads were all one-way streets: in this case you can use a particular road to go from City A to City B, but not from City B to City A.
Social Networks. Social networks that support a “follows” relationship (such as Twitter) can be represented as directed graphs. Each person is still a node, but the “follows” relationship is one way – I can “follow” you, but you don’t have to follow me. If you choose to follow me, that would be a second, independent one-way edge in the opposite direction. (This is also a simplification but works for a basic illustration.)
Other Examples. Many other types of data can be represented with nodes and directed edges. For example, in information security you can represent data and relationships such as:
malware_file --(performed DNS lookup for)--> domain
domain --(resolves to)--> ip_address
In these examples, files, domains, and IP addresses are nodes and “performed DNS lookup for” and “resolves to” are edges (relationships). The edges are directed because a malware binary can contain programming to resolve a domain name, but a domain can’t “perform a lookup” for a malware binary; the relationship (edge) is one-way.
In addition to nodes and edges, some directed graph implementations may allow labeling or tagging of nodes and edges with additional information. These tags can act as metadata for various purposes, such as to create analytically relevant groups of objects.
Many tools exist to visually represent various types of data in a directed graph format.
Analysis with Graphs
When working with graphs and directed graphs, analysts typically select (or lift) objects (nodes) and navigate the graph by traversing the edges (relationships) that connect those nodes. A key limitation to this approach is that all relationships (edges) between objects must be explicitly defined. You must know all of the relationships that you want to represent in advance, which makes the discovery of novel relationships among the data extremely difficult.
A hypergraph is a generalization of a graph in which an edge can join any number of nodes. Because an edge is no longer limited to joining exactly two nodes, edges in a hypergraph are often called hyperedges.
Looked at another way, the key features of a hypergraph are:
Everything is a node. In a hypergraph, objects (“nouns”) are still nodes, similar to a directed graph. However, relationships (“verbs”, commonly represented as edges in a directed graph) may also be represented as nodes. An edge in a directed graph consists of three objects (two nodes and the edge connecting them), but in a hypergraph the same data may be represented as a single multi-dimensional node.
Hyperedges connect arbitrary sets of nodes. An edge in a directed graph connects exactly two nodes. A hyperedge can connect an arbitrary number of nodes; this makes hypergraphs more challenging to visualize in a “flat” form. As in the image above, hyperedges are commonly represented as a set of disconnected nodes encircled by a boundary; the boundary represents the hyperedge “joining” the nodes into a related group. Just as there is no limit to the number of edges to or from a node in a directed graph, a node in a hypergraph can be joined by any number of hyperedges (i.e., be part of any number of “groups”).
Analysis with a Synapse Hypergraph
Synapse is a specific implementation of a hypergraph model. Synapse’s data store is called a Cortex. A Cortex is a scalable hypergraph implementation which includes key/value-based node properties and a data model that facilitates normalization.
In Synapse, all objects and most relationships are nodes (though Synapse uses what we call “lightweight” or “light” edges, similar to directed edges, in some cases). This means that most relationships in Synapse are based on nodes sharing a common property value. Instead of an FQDN being related to an IPv4 using a “resolves to” edge:
the FQDN node is related to a DNS A record because the FQDN is a property of the DNS A node;
the DNS A node is related to an IPv4 because the IPv4 is a property of the DNS A node.
So, in Synapse to understand the relationship between an FQDN and the IPv4 it resolves to, you navigate (pivot) from the FQDN to the DNS A node to the IPv4 node using those nodes’ shared property values.
This means that in Synapse, you are not limited to navigating the data using explicitly defined edges; you primarily navigate (pivot) among nodes with shared property values. Synapse can readily identify these shared values, which both simplifies navigation (Synapse can “show you” the relationships; you don’t need to know them in advance) and help users discover novel relationships that you may not know existed.
Synapse uses mechanisms such as type enforcement to ensure that properties conform to their expected values (e.g., Synapse does its best to prevent you from entering an email address where you need a URL, and that any URL you enter looks reasonably like a URL) and property normalization to ensure property values are represented consistently (e.g., in many cases Synapse converts string-based values to all lowercase for consistency). These methods make the data as consistent and “clean” as possible to facilitate navigation and discovery.