3. System-wide design decisions¶
3.1. Background on Existing Core Components¶
To understand what the DIMS system is intended to provide, it is important to understand its role in the context of distributed and collaborative incident response. DIMS leverages the capabilities of several existing systems each provide key functions necessary for incident response, but are not presently designed to work together. Integrating these capabilities will result in an increase in the capacity to respond.
Figure Overview of DIMS System depicts a high-level diagram of the dataflows between DIMS and related system.
DIMS provides a user interface layer on the front end, as well as a data processing layer on the back end, that integrates with several existing systems.
- The first is the Security Information Event Management (SIEM) system at the core of the PRISEM project, and the technologies associated with it to perform behavioral detection of malicious activity from network flow data and support forensic analysis of historic data to respond and recover from attacks that evade detective mechanisms. This system collects and processes tens of millions of security related events (and network flow records, if desired) and supports a collective approach to responding and recovering from security events.
- The second system is the Ops-Trust portal system, used by a community of several hundred computer security professionals with operational and research roles in industry, government, and academia. This system is primarily designed to facilitate trust group maintenance and communication to deal with emerging threats and events of international scope. (It is now in its second incarnation, as the Trident system).
- The third are the suite of “big data” style open source unstructured data storage, log processing, log visualization, and other tools that are part of the ELK stack, MozDef, and CIF.
- Additional tools that can be used for visualization can be similarly integrated (such as Mal4s), by building them into the system deployment infrastructure like any other components used in DIMS. This type of framework model, if generalized, allows any of a number of open source security tools to be made available to the incident responder.
The DIMS software system will bring these systems together into a collaborative environment for shared analysis and shared response of shared threats, both within a regional trust community, as well as across multiple such trust communities in other regions. Through vertical sharing of indicators of compromise from US-CERT to the regional level, and lateral sharing across regional entities, the objective is to scale actionable information sharing to state, local, territorial, and tribal (SLTT) government entities across the United States, and extend the sharing to international trust groups who make up the global fabric of the internet.
Figure Data Flows Between Stakeholders depicts the data flows between a subset of the stakeholders who will be using the DIMS software system. The solid lines depict data that has the highest degree of sensitivity and trust, often being transmitted in un-redacted form (possibly tagged with TLP indicators for most restricted sharing). The dashed lines depict data flows that are at lower levels of trust, and may be transmitted only in redacted form (possibly tagged with TLP indicators for the least restricted sharing). The type of data shared may be structured IOC and Observables in STIX format, Course of Action information in either PDF or structured format, Situational Awareness Report (SITREP) documents that describe observed campaign level activity at a high level, possibly with structure data containing IOCs or Observables to assist recipients in searching for related activity, and incident reports that may similarly be a combination of human-readable PDF and machine-readable IOCs/Observables. There are two types of data that will be shared in most use cases: high-frequency, high-volume, automated data feeds of reputation data and IOCs/Observables coming from analytic and research groups; low-frequency, low-volume, manually triggered bundles of IOCs/Observables, Course of Action information, and/or high-level SITREPs for specific incident-level up to campaign-level activity.
The DIMS software, layered on top of the Trident portal system as illustrated in Figure DIMS and Trident Component Stack, will facilitate production of these reports and transmission/reception of structure data files and facilitate automated processing of the structure data files to pre-process data for an analyst to consume when ready, rather than forcing the analyst to do a lot of work manipulating files, processing their contents, and manually entering data into report generation front ends in web based portals. (See also Figure DIMS and Trident Component Interfaces.)
Figure PRISEM Initial Deployment and Flows depicts the high-level data flow relationships for the Security Information Event Management (SIEM) system and Botnets detector subsystem used in the PRISEM project as it was initially deployed in 2009. The City of Seattle (the first and to this date largest participant organization) has multiple security devices sending event logs into the system. It also generates NetFlow V5 records that are processed by real-time detectors, and archived for historical query capability. The logs are collected one site, then forwarded to the central SIEM for processing at the University of Washington.
Figure Netflow Architecture depicts a prototypical NetFlow collection and archiving model. The PRISEM system uses a slightly modified version of this model. Unlike the diagram in Figure 4, the PRISEM system processes NetFlow records as they enter the NetFlow Collector in the center of the diagram, sending copies to the Botnets system detectors. One of the processes receiving these records performs the storage task, however it converts the NetFlow V5 records to SiLK format before storing them. The SiLK tool suite is then used to process these historic logs (e.g., performing historic queries).
Figure Botnets System High-Level Architecture shows the high-level architecture of the Botnets network flow-based behavioral detector system. One or more NetFlow V5 feeds are combined into a single feed, which duplicates each NetFlow record and fans them out in to N different detectors. Each detector maintains its own state and sends out alerts when appropriate via SNMP, standard output to users in realtime, or to the Unix syslog service. (In Figure 5, syslog events are sent to a remote syslog server and processed by ZenOSS, an open source IT monitoring system. In the PRISEM system, all detectors alert via syslog, which are processed by the Log Matrix Threat Center application.)
Figure PRISEM Architecture shows the central system architecture of the PRISEM system. Shown in green are the Security Information Event Management (SIEM) system and event log archive in the bottom right. The box in the upper right depicts an instance of the network flow monitoring (“Botnets” detector system) and SiLK data archiving, which is typically housed on-site at participant networks due to sensitivity of network flow data. A central instance of the Collective Intelligence Framework (CIF) v0.1 database provides historic information about known malicious activity, which is used to pull watchlists that the Botnets detectors use for behavioral detection. A virtual machine server provides processing and AMQP broker functions to integrate data from multiple sources and correlate it across participating organizations, and optionally anonymize or filter any data prior to sharing. At present, a vendor-proprietary portal provides the graphical user interface front-end for participants, with the primary PRISEM systems residing behind a vendor-supported firewall, with command line utilities and AMQP access provided via an OpenVPN server for secure access. The DIMS dashboard will front-end this portal and support additional capabilities that are available on the PRISEM back-end via the AMQP broker.
Figure Ops-Trust Architecture Diagram shows the basic architecture of the Ops-Trust portal system. This system is a combination of a web-based portal, a wiki for information archiving, an email server, and DNS and LDAP services tied to OpenID authentication services to provide single-signon capability. All of these services are provided via four separate virtual machines, co-resident in a single 1U server that is backed up off-site. The instance depicted in Ops-Trust Architecture Diagram is hosted on Ops-Trust hardware. A development instance was set up at the UW for DIMS development.
The Ops-Trust portal stores attributes about each member. Figure Ops-Trust Member Information Page shows the account for the PI, which includes: user UUID; home time zone; nearest airport (to facilitate contact and meet-ups when one is on travel); how to contact via email, postal mail, SMS, IM, and phone; and current PGP encryption key. The portal lets you sign up for email lists, and switch between “trust groups”. After signing up for (and optionally being approved for membership) email lists, the user is included on list email routed through the mail server, and granted access to the appropriate section of the wiki.
The DIMS system will take advantage of the foundation of services provide by this portal in several ways. It will use it as a means of storing more information about users, the network assets they protect, the policies and mechanisms for anonymizing and filtering data based on TLP tagging, etc. It will also use it as a mechanism to distribute data to users as needed (e.g., alerts about email threads that pertain to the network assets they protect, providing a means to download OpenVPN certificates and SSH keys, as a mechanism for storing and organizing data associated with incidents and campaigns they are dealing with, etc.) The ability to manage encrypted communications and multiple email lists facilitates trusted communication and offers a basis for sending structured threat information in encrypted form, directly from one user to another, or from a user to all members of a list.
3.2. Software Development Methodology¶
As the DIMS system relies upon and integrates multiple existing open source software components, and code developed by the DIMS developers, the system is being developed using an Agile programming development methodology (as opposed to the classic waterfall development methodology with its sequential processes.) This document, therefore, is a living document that will be updated as the project proceeds and as cyclic input/feedback from users and testers is received. Sections to be addressed in future releases of this document are listed as TBA.
The DIMS project involves coordination of team members in multiple locations, multiple time zones, and multiple overlapping areas of responsibility. In order to communicate, coordinate, maintain momentum of project development, and meet deliverable requirements of the contract with the sponsor, all DIMS team members must be able to work asynchronously, independently, and be responsible for following task prioritization or asking for direction as necessary.
3.2.1. Use of Agile Development Methodology¶
Integration of existing open source tools requires research into how the existing tool is designed and how it functions, understanding how it processes inputs and outputs, and how it is configured.
The Agile methodology and Scrum methodology involve making small incremental changes based on simple user stories (short descriptions of what a user wants or needs), and making these changes on a short time frame (within a sprint, which is usually on the order of one or two weeks. (See [[agileDevelopment]] Agile development.)
Tasks are prioritized using the Jira Agile ticketing system, with the objective of completion of tasking within a 2-week sprint cycle. Weekly meetings are used to manage sprints.
Both source code, and system configuration files and installation instructions,
are maintained using the Git source code control system using git-flow
and hub, for eventual open source release on GitHub. This supports use of
the Vincent Dreisen branching workflow to allow independent and isolated
changes to be made, which are then to be tested prior to integration into more
master branches for release.
3.2.2. Use of Continuous Integration¶
The concepts of Continuous Integration and DevOps (also known as
agile system administration or agile operations) for rapid development,
testing, and release of a functional system are employed in order to
build the overall system one component at a time, in a manner that
can support the requirements specified in Adaptation requirements
and [[continuousIntegration]] Continuous Integration & Delivery. By automating the way
systems are configured, and how DIMS developed software is installed
on them, not only are incremental changes possible with little effort,
but multiple instances can be supported. Code that reaches the
master branch is considered stable and release ready, at which
point it can be pushed to test/evaluation and/or production systems.
Development test systems would be fed by less stable branches
Documentation follows the same continuous integration and agile methodologies, using the Sphinx program, which processes ReStructured Text (reST) files (and is supported by the online documentation repository, ReadTheDocs.)
3.2.3. Use of Distributed Configuration Management¶
At the initiation of the DIMS project, the program Ansible was chosen for distributed system configuration and DIMS service deployment. Use of Ansible in DIMS is described in Section ansibleplaybooks:ansibleintro of ansibleplaybooks:ansibleplaybooks.
Figure Configuration Description Taxonomy illustrates the taxonomy of inheritence
levels, following a left-to-right order of application of variables using
host_vars files (potentially augmented by
vars files for specific services.)
Setting variables in Ansible is quite complicated and should be studied and understood well by anyone attempting to construct playbooks or configure hosts and services. The ability to gain insight into how variables are set at runtime is crucial. The ansibleplaybooks:ansibleplaybooks documentation covers this topic.
3.2.4. Use of Containerization¶
During the Base year of the DIMS project, the focus was on taking as many open source tools as possible, and code developed by the DIMS team, and installing it on virtual machines using:
- Ubuntu (versions 10.04, 12.04, and 14.04), CentOS 5 and 6, and Mac OS X as host operating systems;
- Virtualbox and KVM as hypervisors;
- Packer for turning operating system installation ISOs into Box files for Virtualbox;
- Vagrant for provisioning virtual machines on developers’ host operating systems of choice;
- Ansible for compiling code, configuring operating systems and services, installing pre-requisites libraries and tool dependencies, and other required DIMS tasks.
The team ran into a series of endlessly repeating problems that made progress painstakingly slow. These included:
- One person could get something running, only to hand it over to someone else to test (who could not run it).
- One team member could compile and install a program (because they had set up their system before hand with the requisite sofware), but another ran into missing dependencies and was blocked, not knowing what to do to get past the block.
- One team member could check in source code, only to find that another team member could not check it out because they had an out-of-date Git client.
- One team member could build a virtual machine with an open source package on it, but another did not know how to replicate the steps in the right order and could not get it to run.
- One team member would research a task, complete coding of Ansible playbooks to install the given software, but nobody else on the team could test it because they did not know the code existed or how to invoke it.
- One team member would code solutions to a problem that prevented widespread deployment of a given capability (such as component tests, status information collection, or event logging), but others on the team were not aware of the need to update their own development environments and things that formerly worked for them would “break”.
- Frequently, only one team member was expert in a particular software package or operating system, but nobody else was. This made the person who knew how to do something a blocker in the critical path. If they were not available when someone else was trying to meet a deadline, the block would halt progress.
- Even when things worked right, and complete Vagrant virtual machines could be built and run with specific services running within them, IP addresses had to be configured by hand, and no DNS service existed that knew how to serve those IP addresses from domain names. This made it difficult for the team to know how to link services together, so things only worked when all software was installed in a single virtual machine (assuming that conflicting dependencies for libraries and operating system did not prevent all the software components from running on the same virtual machine.)
The result was what seemed like an endless chain of blockers that introduced friction throughout the entire process.
Concept for a new or modified system describes the operational concept for a new system, the DIMS framework model, which requires a mechanism that avoids the problems described above. The best available solution to these problems appears to be the use of containers (also known as Operating-system-level virtualization, or Microservices architecture).
Docker is seen as the leading technology in this area, garning a tremendous amount of support and energy. Docker is, “an open source project designed to easily create lightweight, portable, self-sufficient containers from any application.” Their motto is “Build, ship, and run any application, anywhere.” One of the main benefits of the use of containers is getting away from “dependency hell” of trying to fit a least-common-denominator of:
- operating system +
- OS version +
- specific libraries +
- specific programming languages +
- specific dependant programs +
- specific service configuration settings
Docker containers are not the perfect solution, by any means. There are certain security concerns, issues with linking containers together, keeping them up and running in the face of uncaught exceptions, etc. (Many of these same problems exist with use of bare-metal or virtual machines, so certain challenges remain regardless.) Figure Run Services with Docker (from https://coreos.com/using-coreos/) illustrates a 3-tiered web application in a clustered containter deployment.
The suite of tools for orchestration, shared container components used to build higher-level images, distributed configuration and service discovery, persistent storage across clustered systems, domain name services, logging, and monitoring across a vast number of systems, all put Docker in a strong position in terms of open source software as opposed to virtual machines and the equivalent tools to manage large numbers of virtual machines. (The commercial tools supporting these tasks on virtual machines are out of the price range of SLTT government entities, let alone small- and medium-sized businesses and volunteer incident response groups.)
For more information on all of these topics, see the Containerization, Virtualization, “Microservice Architectures” section of the PI’s home page and the document dimsdockerfiles:usingdockerindims.
3.3. Use of Open Source components¶
3.4. Summary of High-Level System Architecture Delineation¶
At the beginning of this section in Background on Existing Core Components we saw DIMS from the perspective of data flows and core software components. A more detailed exposition of these components is found in DIMS Operational Concept Description v 2.9.0, Section Description of current system or situation.
In this section the focus is on delineating the components that are used to build the DIMS system from those that are functional in an operations context. Further, it will clarify the difference between the boxes on the left of Figure Overview of DIMS System (which have a subset of features that would be used by a non-operations investigative entity (e.g., US-CERT, the United States Secret Service, the Federal Trade Commission, or a Fusion Center) vs. the gray box in the bottom right of Figure Overview of DIMS System that includes the full set of realtime event data collection and network flow monitoring features that are more operational in nature.
A deployment of the core components of DIMS for a user such as the a law enforcement agency, a Fusion Center, etc, is depicted as DIMS-OPS in Figure DIMS Operations.
|Trident portal and wiki||Backend Data Stores (BDS) CSCI, Design and implementation constraints|
|DIMS Web App||Dashboard Web Application (DWA) CSCI|
|LDAP Single-Signon||Data Integration and User Tools (DIUT) CSCI, [[networkAccessControls]] Network Access Controls|
|Redis, Hadoop (HDFS), Elasticsearch, etc.||Backend Data Stores (BDS) CSCI|
|OpenVPN||Data Integration and User Tools (DIUT) CSCI, [[networkAccessControls]] Network Access Controls|
|Tupelo||Data Integration and User Tools (DIUT) CSCI|
|Anonymization||Data Integration and User Tools (DIUT) CSCI|
|STIX input/output||Vertical/Lateral Information Sharing (VLIS) CSCI|
|Distributed Security Event Data Collection||Backend Data Stores (BDS) CSCI|
|Alerting||Data Integration and User Tools (DIUT) CSCI, Dashboard Web Application (DWA) CSCI|
|Cross-organizational Correlation||Data Integration and User Tools (DIUT) CSCI, Dashboard Web Application (DWA) CSCI|
|Customized User Documentation||Adaptation requirements|
|Custom Configuration and Automated Deployment||Adaptation requirements, [[automatedProvisioning]] Automated Provisioning, [[continuousIntegration]] Continuous Integration & Delivery|
Finally, the DIMS team (or anyone wishing to develop DIMS from the open source code base) requires all of the code development, configuration management, and continuous integration (or DevOps) features necessary for development. This is illustrated in Figure DIMS Operations + PISCES + DevOps.
|Trident portal and wiki||Backend Data Stores (BDS) CSCI, Design and implementation constraints|
|Git source repository management||Design and implementation constraints|
|Jenkins Continuous Integration||Design and implementation constraints|
|Ansible configuration||Design and implementation constraints|
|Distributed configuration database||Backend Data Stores (BDS) CSCI, Design and implementation constraints|
|Docker repository||Backend Data Stores (BDS) CSCI, Design and implementation constraints|
|Jira ticketing||Design and implementation constraints|
For a pilot deployment of DIMS for the U.S. Secret Service, a full DIMS-OPS + DIMS-PISCES deployment will be instantiated for a select subset of the PRISEM participants in the Puget Sound to replicate a group of “victim” sites. Using live data, an incident will be investigated and “reported” to a test “U.S. Secret Service” DIMS-OPS system. This will validate the concept of reporting machine-parsable data to a central site using the Vertical and Lateral Information Sharing CSCI components (see Vertical/Lateral Information Sharing (VLIS) CSCI and DIMS Test Plan v 2.9.1).
|||The term PISCES is the proposed replacement for PRISEM moving forward.|