Home > Articles

  • Print
  • + Share This
This chapter is from the book

OpenSOC

OpenSOC was created by Cisco to attack the “big data problem” for their Managed Threat Defense offering. Cisco has developed a fully managed service delivered by Cisco Security Solutions to help customers protect against known intrusions, zero-day attacks, and advanced persistent threats. Cisco has a global network of security operations centers (SOCs) ensuring constant awareness and on-demand analysis 24 hours a day, 7 days a week. They needed the ability to capture full packet-level data and extract protocol metadata to create a unique profile of customer’s network and monitor them against Cisco threat intelligence. As you can imagine, performing big data analytics for one organization is a challenge, Cisco has to perform big data analytics for numerous customers including very large enterprises. The goal with OpenSOC is to have a robust framework based on proven technologies to combine machine learning algorithms and predictive analytics to detect today’s security threats.

The following are some of the benefits of OpenSOC:

  • The ability to capture raw network packets, store those packets, and perform traffic reconstruction
  • Collect any network telemetry, perform enrichment, and generate real-time rules-based alerts
  • Perform real-time search and cross-telemetry matching
  • Automated reports
  • Anomaly detection and alerting
  • Integration with existing analytics tools

The primary components of OpenSOC include the following:

  • Hadoop
  • Flume
  • Kafka
  • Storm
  • Hive
  • Elasticsearch
  • HBase
  • Third-party analytic tool support (R, Python-based tools, Power Pivot, Tableau, and so on)

The sections that follow cover these components in more detail.

Hadoop

The Apache Hadoop or “Hadoop” is a project supported and maintained by the Apache Software Foundation. Hadoop is a software library designed for distributed processing of large data sets across clusters of computers. One of the advantages of Hadoop is its ability to using simple programming models to perform big data processing. Hadoop can scale from a single server instance to thousands of servers. Each Hadoop server or node performs local computation and storage. Cisco uses Hadoop clusters in OpenSOC to process large amounts of network data for their customers, as part of the Managed Threat Defense solution, and it also uses Hadoop for its internal threat intelligence ecosystem.

Hadoop includes the following modules:

  • Hadoop Common: The underlying utilities that support the other Hadoop modules.
  • Hadoop Distributed File System (HDFS): A highly scalable and distributed file system.
  • Hadoop YARN: A framework design for job scheduling and cluster resource management.
  • Hadoop MapReduce (MapR): A system designed for parallel processing of large data sets based on YARN.

Figure 5-2 illustrates a Hadoop cluster.

Figure 5-2

Figure 5-2 Hadoop Cluster Example

In Figure 5-2, a total of 16 servers are configured in a Hadoop cluster and connected to the data center access switches for big data processing.

HDFS

HDFS is a highly scalable and distributed file system that can scale to thousands of cluster nodes, millions of files, and petabytes of data. HDFS is optimized for batch processing where data locations are exposed to allow computations to take place where the data resides. HDFS provides a single namespace for the entire cluster to allow for data coherency in a write-once, read-many access model. In other words, clients can only append to existing files in the node. In HDFS, files are separated into blocks, which are typically 64 MB in size and are replicated in multiple data nodes. Clients access data directly from data nodes. Figure 5-3 shows a high-level overview of the HDFS architecture.

Figure 5-3

Figure 5-3 HDFS Architecture

In Figure 5-3, the NameNode (or Namespace Node) maps a filename to a set of blocks and the blocks to the data nodes where the block resides. There are a total of four data nodes, each with a set of data blocks. The NameNode performs cluster configuration management and controls the replication engine for blocks throughout the cluster. The NameNode metadata includes the following:

  • The list of files
  • List of blocks for each file
  • List of data nodes for each block
  • File attributes such as creation time and replication factor

The NameNode also maintains a transaction log that records file creations, deletions, and modifications.

Each DataNode includes a block server that stores data in the local file system, stores metadata of a block, and provisions data and metadata to the clients. DataNodes also periodically send a report of all existing blocks to the NameNode and forward data to other specified DataNodes as needed. DataNodes send a heartbeat message to the NameNode on a periodic basis (every 3 seconds by default), and the NameNode uses these heartbeats to detect any DataNode failures. Clients can read or write data to each data block, as shown in Figure 5-3.

Flume

OpenSOC uses Flume for collecting, aggregating, and moving large amounts of network telemetry data (like NetFlow, syslog, SNMP, and so on) from many different sources to a centralized data store. Flume is also licensed under the Apache license. Figure 5-4 shows how different network telemetry sources are sent to Flume agents for processing.

Figure 5-4

Figure 5-4 Network Telemetry Sources and Flume

Flume has the following components and concepts:

  • Event: A specific unit of data that is transferred by Flume, such as a single NetFlow record.
  • Source: The source of the data. These sources are either actively queried for new data or they can passively wait for data to be delivered to them. The source of this data can be NetFlow collectors, server logs from Splunk, or similar entities.
  • Sink: Delivers the data to a specific destination.
  • Channel: The conduit between the source and the sink.
  • Agent: A Java virtual machine running Flume that comprises a group of sources, sinks, and channels.
  • Client: Creates and transmits the event to the source operating within the agent.

Figure 5-5 illustrates Flume’s high-level architecture and its components.

Figure 5-5

Figure 5-5 Flume Architecture

Kafka

OpenSOC uses Kafka as its messaging system. Kafka is a distributed messaging system that is partitioned and replicated. Kafka uses the concept of topics. Topics are feeds of messages in specific categories. For example, Kafka can take raw packet captures and telemetry information from Flume (after processing NetFlow, syslog, SNMP, or any other telemetry data), as shown in Figure 5-6.

Figure 5-6

Figure 5-6 Kafka Example in OpenSOC

In Figure 5-6, a topic is a category or feed name to which log messages and telemetry information are exchanged (published). Each topic is an ordered, immutable sequence of messages that is continually appended to a commit log.

Kafka provides a single “consumer” abstraction layer, as illustrated in Figure 5-7.

Figure 5-7

Figure 5-7 Kafka Cluster and Consumers

Consumers are organized in consumer groups, and each message published to a topic is sent to one consumer instance within each subscribing consumer group.

All consumer instances that belong to the same consumer group are processed in a traditional queue load balancing. Consumers in different groups process messages in a publish-subscribe mode, where all the messages are broadcast to all consumers.

In Figure 5-7, the Kafka cluster contains two servers (Server 1 and Server 2), each with two different partitions. Server 1 contains partition 0 (P0) and partition 1 (P1). Server 2 contains partition 2 (P2) and partition 3 (P3). Two consumer groups are illustrated. Consumer Group 1 contains consumers A, B, and C. Consumer Group 2 contains consumers: D and E.

Kafka provides parallelism to provide ordering guarantees and load balancing over a pool of consumer processes. However, there cannot be more consumer instances than partitions.

Storm

Storm is an open source, distributed, real-time computation system under the Apache license. It provides real-time processing and can be used with any programming language.

Hadoop consists of two major components: HDFS and MapReduce. The early implementations of Hadoop and MapReduce were designed on batch analytics, which does not provide any real-time processing. In SOCs, you often cannot process data in batches, and so it can take several hours to complete the analysis.

OpenSOC uses Storm because it provides real-time streaming and because of its amazing ability to process big data, at scale, in real time. Storm can process data at over a million tuples processed per second per node. Figure 5-8 shows how Kafka topics feed information to Storm to provide real-time processing.

Figure 5-8

Figure 5-8 Storm in OpenSOC

Hive

Hive is a data warehouse infrastructure that provides data summarization and ad hoc querying. Hive is also a project under the Apache license. OpenSOC uses Hive because of its querying capabilities. Hive provides a mechanism to query data using a SQL-like language that is called HiveQL. In the case of batch processing, Hive allows MapR programmers use their own custom mappers.

Figure 5-9 shows how Storm feeds into Hive to provide data summarization and querying.

Figure 5-9

Figure 5-9 Hive in OpenSOC

Storm can also feed into HBase and Elasticsearch. These are covered in the following sections.

Elasticsearch

Elasticsearch is a scalable and real-time search and analytics engine that is also used by OpenSOC. Elasticsearch has a very strong set of application programming interfaces (APIs) and query domain-specific languages (DSLs). It provides full query DSL based on JSON to define such queries. Figure 5-10 shows how Storm feeds into Elasticsearch to provide real-time indexing and querying.

Figure 5-10

Figure 5-10 Elasticsearch in OpenSOC

HBase

HBase is scalable and distributed database that supports structured data storage for large tables. You guessed right: HBase is also under the Apache license! OpenSOC uses HBase because it provides random and real-time read/write access large data sets.

HBase provides linear and modular scalability with consistent database reads and writes.

It also provides automatic and configurable high-availability (failover) support between Region Servers. HBase is a type of “NoSQL” database that can be scaled by adding Region Servers that are hosted on separate servers.

Figure 5-11 shows how Storm feeds into HBase to provide real-time indexing and querying.

Figure 5-11

Figure 5-11 HBase in OpenSOC

Third-Party Analytic Tools

OpenSOC supports several third-party analytic tools such as:

  • R-based and Python-based tools
  • Power Pivot
  • Tableau

Figure 5-12 shows the complete OpenSOC architecture, including analytics tools and web services for additional search, visualizations, and packet capture (PCAP) reconstruction.

Figure 5-12

Figure 5-12 OpenSOC Architecture

Other Big Data Projects in the Industry

There are other Hadoop-related projects used in the industry for processing and visualizing big data. The following are a few examples:

  • Ambari: A web-based tool and dashboard for provisioning, managing, and monitoring Apache Hadoop clusters.
  • Avro: A data serialization system.
  • Cassandra: A scalable multimaster database with no single points of failure.
  • Chukwa: A data collection system for managing large distributed systems.
  • Mahout: A scalable machine learning and data mining library.
  • Pig: A high-level data-flow language and execution framework for parallel computation.
  • Spark: A fast and general compute engine for Hadoop data.
  • Tez: A generalized data-flow programming framework, built on Hadoop YARN.
  • ZooKeeper: A high-performance coordination service for distributed applications.
  • Berkeley Data Analytics Stack (BDAS): A framework created by Berkeley’s AMPLabs. BDAS has a three-dimensional approach: algorithms, machines, and people. The following are the primary components of BDAS:

    • Akaros: An operating system for many-core architectures and large-scale SMP systems
    • GraphX: A large-scale graph analytics
    • Mesos: Dynamic resource sharing for clusters
    • MLbase: Distributed machine learning made easy
    • PIQL: Scale independent query processing
    • Shark: Scalable rich analytics SQL engine for Hadoop
    • Spark: Cluster computing framework
    • Sparrow: Low-latency scheduling for interactive cluster services
    • Tachyon: Reliable file sharing at memory speed across cluster frameworks

You can find detailed information about BDAS and Berkeley’s AMPLabs at https://amplab.cs.berkeley.edu

  • + Share This
  • 🔖 Save To Your Account

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.

Overview


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information


To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information


Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children


This site is not directed to children under the age of 13.

Marketing


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information


If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information


Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents


California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure


Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact


Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice


We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020