Background - Why Synapse?¶
Synapse is a distributed key-value hypergraph analysis framework. Synapse is designed to support analysis conducted over very large and disparate data sets. Analysis is predicated on the representation of data from a given knowledge domain in a structured data model that allows analysts to represent, annotate, and query across the collected data.
Synapse offers several advantages:
- Free and Open Source
- Highly Optimized Performance
- Flexible, Extensible Data Model
- Data Model Introspection
- Powerful Custom Query Language
- Shared Analysis Framework
- Flexible, Granular Permissions
- Custom Module Integration
- Detailed Logging
- Distributed Architecture
- Layered Data Storage
- Proven Methodology Informed by Real-World Experience
Free and Open Source¶
Highly Optimized Performance¶
Synapse was designed to address the performance limitations that constrain many large-scale analysis systems and eventually make them unworkable in practice. Examples of performance optimizations include:
- Synapse developers conducted extensive performance testing during Synapse’s initial development phase, and selected LMDB as the storage backing that provides optimal performance to meet Synapse’s goals.
- Synapse uses type-optimized indexing so that each type of data can be indexed in a manner that is optimized for how that data is typically queried and used.
- Synapse supports asynchronous processing and streaming of results. This means that even for large queries, Synapse begins returning results back to users almost immediately.
Flexible, Extensible Data Model¶
Synapse’s hypergraph framework allows for representation of widely disparate types of data as well as complex relationships. The extensible data model can be adapted to any knowledge domain.
Data Model Introspection¶
The Synapse data model is itself data that is modeled in the Synapse hypergraph. This means that an analyst can query and view the model elements from within Synapse just as they can query data represented by the model. In other words, it is not necessary for an analyst to interrupt their workflow to refer to external documentation if they have questions about the model.
Powerful Custom Query Language¶
Synapse includes a powerful native query language called Storm. Storm is designed to be both flexible and concise. Because many analysts are not programmers, Storm is designed as a “data language”, functioning in a manner that feels more like “asking questions” in a natural manner. However, Storm also includes advanced features suitable for “power analysts” and even developers to allow them to create flexible and powerful queries to support automation, orchestration, and integration with third-party services, as needed.
Storm is also optimized for Synapse and the Synapse data model. Storm includes performance enhancements specifically based on awareness of the data model and property types. In addition, the Storm query parser helps prevent “bad” queries by taking a “do what I mean” approach in some cases where the query entered by a user may be inefficient.
Finally, Storm is extensible and supports the integration of loadable modules and custom commands, allowing users to access additional functionality without leaving the Storm interface.
Flexible, Granular Permissions¶
Synapse includes a flexible role-based access control (RBAC) permissions system. Synapse allows the creation of both users and groups and supports fine-grained control over who can create, modify, and annotate data. Organizations with basic user management needs who want to be up and running quickly can set simple and broad permissions (create / modify / delete nodes, add / remove tags). Alternately, organizations with detailed needs or who support specialized types analysis can manage permissions down to the individual property or sub-sub-tag level if necessary - even to the extent of allowing a user to add or change only a single property on a single type of node.
Synapse supports automation through a number of features, including a cron scheduling system, triggers that can perform arbitrary actions upon the occurrence of a specific event (such as adding a node or applying a tag), and stored macros. Automation features leverage the Storm query language, and can carry out any action that can be expressed as a Storm query. See Storm Reference - Automation for additional detail.
Custom Module Integration¶
Synapse supports adding new or custom modules to expand functionality or integrate with third-party services.
Synapse produces a stream of potentially reversible changes called splices which can account for every change to the hypergraph. Each splice includes the source (provenance) of the change, which makes it possible to trace the origin of a given modification, regardless of whether the change was made by an analyst executing a query or an automated process.
Layered Data Storage¶
Proven Methodology Informed by Real-World Experience¶
Synapse was not developed as a mathematical abstraction. Instead, Synapse grew out of a real-world need to track a complex, diverse, and very large data set: namely, cyber threat data.
The developers and analysts who worked on early Synapse prototypes came from a variety of government and commercial backgrounds but shared a common goal: the desire to record, annotate, and track cyber threat activity (specifically, nation-state level activity) both reliably and at scale. At the time when government and industry were beginning to grasp the scope and scale of the problem, “tracking” this complex activity was largely done using long-form reports, spreadsheets, or domain knowledge residing in an analyst’s mind. There was no way to effectively store large amounts of disparate data and associated analytical findings in such a way that relationships among those data and analytical conclusions were readily apparent or easily discoverable. More importantly, critical analytical decisions such as attribution were either impossible, or being made based on loose correlation, analysts’ recollection, or generally accepted “truths” - and not based on concrete, verifiable data whose source and analysis could be traced and either verified or questioned.
In contrast, Synapse and its predecessors were designed from the beginning to support the following critical elements:
- The use of a shared analytical workspace to give all analysts access to the same data in real time, as noted above.
- The concept that the analysis captured within the system should “speak for itself”: that is, to the extent possible, data and analytical findings must be represented in such a way that relationships among data and conclusions about data should be self-evident.
These features provide the following benefits:
- 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 an analytical line of reasoning.
- Synapse allows analysts to better vet and verify each other’s findings. Conflicting analytical lines are highlighted through the structure of the data itself. Analysis can readily be questioned, reviewed, deconflicted, and ultimately improved.
The original Synapse prototype was designed to store a broad range of threat data, including:
- Network infrastructure
- Malware and malware behavior
- Host- and network-based incident response data
- Detection signatures and signature hits
- Decoded network packet captures
- Targeting of organizations, individuals, and data
- Threat groups and threat actors
- People and personas
- Newsfeeds and reference materials
Prototype systems eventually stored nearly one billion nodes, edges, and analyst annotations. Data modeled by this system was used to produce some of the most groundbreaking public reporting on nation-state (“Advanced Persistent Threat”, or APT) activity to date.
Synapse is the next generation of technology built on approximately six years of technical and analytical lessons learned:
- The new hypergraph design addresses many of the shortcomings identified with earlier directed graph and prototype hypergraph systems.
- Because the experience of working with threat data 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.
Many of the real-world examples in this User Guide reference data from the field of information technology or threat tracking, given Synapse’s history; but the structures, processes, and queries can be applied to other knowledge domains and data sets as well. The intent of Synapse is that any data that could be represented in a spreadsheet, database, or graph database can be represented in a Synapse hypergraph using an appropriate data model.