book

Data Engineering for Cybersecurity

Name: Data Engineering for Cybersecurity
Author: James Bonifield
ISBN: 9781718504028

by James Bonifield

August 2025

Intermediate to advanced

344 pages

8h 34m

English

No Starch Press

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Title Page
Copyright
Dedication
About the Author and the Technical Reviewers
Acknowledgments
Introduction
Who This Book Is ForWhat’s in This BookPrerequisitesOnline Resources
Part I: Foundations of Secure Data Engineering
1. Data Engineering Basics
Common Data Engineering TasksGetting Data to the SIEMManaging Throughput and LatencyEnriching and Standardizing EventsExample ArchitecturesA Basic Data PipelineProactive Data RetrievalTemporary Data CentralizationEvent CachingData Serialization FormatsJSONYAMLElastic Common SchemaVirtual Machine SetupWindows File Transfer and Shell Access ToolsOther Useful ToolsSummary
2. Network Encryption
TLS ComponentsInteractionsMutual TLSGenerating a Certificate AuthorityRoot ConfigurationRoot GenerationIntermediate ConfigurationIntermediate SigningChain FilesSetting Up TLS for LogstashFlex ConfigurationServer Keys and CertificatesDirectory PreparationLogstash ConfigurationFirewall PortsSSHEnabling SSHCreating SSH KeysStarting the AgentCreating Configuration FilesDistributing Public KeysVerifying ConnectivitySummary
3. Source and Configuration Management
The Git Version Control WorkflowInstallationSetting Up GitHub or GitLabCreating New DirectoriesInitializing LocallyCloning the Remote RepositoryUpdating README.mdCreating a New FileIgnoring FilesCreating a Development BranchAdding the BranchCreating a New FileSetting the Branch’s Remote OriginMerging the Branch into mainPerforming Optional CleanupRebasing and Resetting Your CodeCreating a New Test BranchModifying the Remote main BranchTemporarily Stashing ChangesCleanupCreating a Repository for the Book’s Project CodeSummary

Part II: Log Extraction and Management
4. Endpoint and Network Data
Collecting Logs with FilebeatInstallationEnabling TLSCreating a Configuration FileGenerating Certificate Signing RequestsConfigurationInput SourcesReading Local FilesApplying Parsers and New FieldsListening to the NetworkConnecting to External SystemsProcessorsControlling Processors with ConditionalsScreening Out DataModulesSending OutputsPublishing to KafkaPublishing to RedisWriting Output Data to a FileSending Data to LogstashPruning and Privatizing DataFilebeat for WindowsSummary
5. Windows Logs
Collecting Logs with WinlogbeatEvent Log TypesApplication and System LogsThe Security Log and SysmonOther Log SourcesInstallationEnabling TLSCreating a Configuration FileGenerating a Key Pair and Client CertificateConfigurationStarting the ServiceCollecting Logs from Sysmon and PowerShellSysmonPowerShell Script BlocksPowerShell ModulesExploring Available Event LogsWorking with ProcessorsSummary
6. Integrating and Storing Data
An Elastic Agent ArchitectureSetting Up the EnvironmentEnabling TLSConfiguring DNS and Firewall RulesInstalling Tools on Multiple ServersViewing Events in KibanaConfiguring Different Technology IntegrationsNetwork MetadataIptables Firewall LogsIntegration GuidelinesStand-Alone AgentsPreparing the Virtual MachineReceiving a Logstash API KeyConfiguring a Stand-Alone Agent PolicyConfiguring LogstashElasticsearch Ingest PipelinesCreating a Custom Ingest PipelineTesting the PipelineSummary
7. Working with Syslog Data
Logging PrioritiesCollecting Data with RsyslogInstallationEnabling TLSCustom Rsyslog ConfigurationsBasic vs. Advanced FormatsAdding and Testing New ConfigurationsAdvanced Format SyntaxGlobal PropertiesInput ModulesOutput ModulesTemplatesRulesetsExample ConfigurationsA ClientA Server RelaySummary
Part III: Data Transformation and Standardization
8. Data Manipulation Pipelines
Logstash Pipeline ComponentsLogstash Installation and SetupEnabling TLSConfiguring Java Virtual Machine OptionsInstalling CodecsInputs and OutputsHTTP POST RequestsAPI RequestsReading and Writing FilesKafkaRedisAmazon S3 and MinIOLogstash to LogstashNull OutputVirtual Addressing in PipelinesSummary
9. Transformation Filters
Logstash Filters for Data TransformationExtracting Data from Messy InputsExtracting Unstructured Data with grokExtracting Reliably Ordered Data with dissectHandling Inaccurate Syslog TimestampsEnriching DataNetwork Identification with the CIDR FilterKey-Value Lookups with translateAdding Directional TagsField ComparisonEquality and InequalityField ExistenceMembershipRegular ExpressionsParsing Key-Value Pairs from Structured Syslog DataSplitting Fields and Avoiding BackslashesInputMutateOutputWriting Filters with Ruby ScriptingInit and CodeArray ComparisonsDropping Data to Save CPUSummary
Part IV: Data Centralization, Automation, and Enrichment
10. Centralizing Security Data
Kafka FundamentalsProducers, Consumers, and Consumer GroupsBrokers and ControllersTopicsPartitionsData ReplicationListeners and MetadataStreams and ConnectorsCreating a Kafka ClusterSetting Up the NetworkCreating Users and DirectoriesInstalling Kafka and a Java Development KitEnabling TLSConfigurationsConfiguring Server PropertiesDefining the Connection SettingsCreating Cluster IDsUsing Systemd for Automatic StartupRunning KafkaStarting BrokersCreating TopicsPublishing MessagesSubscribing to TopicsCreating Tool-Specific TopicsConnecting to External ToolsRsyslogFilebeatLogstashKafka Pipeline Design ConsiderationsSummary
11. Automating Tool Configurations
Working with AnsibleInventoriesCommands, Playbooks, and RolesGathering System FactsTemplatesTemporary FilesSSH RequirementsInstallationCreating InventoriesConfiguration FilesRunning Ad Hoc CommandsEnabling Check ModeDistributing FilesExecuting CommandsUpdating PackagesManaging ServicesRebooting ServersPlaybooks for Task ExecutionReusable TasksDelegating TasksPerforming Tasks LocallyWriting Dynamic Data to FilesModifying hosts FilesAutomatically Generating Encryption KeysAppending TextSummary
12. Ansible Tasks and Playbooks
Allowing Firewall TrafficAutomatic File TransfersDistributing the Kafka PackageDownloading Files from the InternetCloning Git RepositoriesCreating Kafka Configuration TemplatesInstalling GPG KeysResponding to PromptsAccessing APIs and Web ServicesTLS AutomationSetting Up the DirectoryCreating the Root CACreating the Intermediate CAGenerating the CA ChainCreating a Flex CertificateAdding Container FilesCreating the TLS PlaybookTroubleshooting with debugSummary
13. Caching Threat Intelligence Data
Speeding Up Lookups with CachingInstalling Redis and MemcachedDividing the Project Servers into NodesEnabling TLSConfiguring RedisConfiguring MemcachedWorking with RedisWorking with MemcachedPopulating and Replicating Redis for Threat LookupsCreating the Leader CacheReceiving Threat Indicators with LogstashDefining InputsProcessing IndicatorsHandling Custom UploadsPopulating Redis with Key-Value PairsCreating Custom API KeysOutputting Data to ElasticsearchReading Threat IntelligenceCreating Redis FollowersConfiguring a Logstash GetterDeduplicating and Enriching DataDistributing Threat Intelligence with MemcachedConfiguring the Cyber Threat Intelligence NodeConfiguring the Data NodePreventing DriftHandling Analyst-Submitted Indicator DataGoing BeyondSummary
Index

Content preview from Data Engineering for Cybersecurity

INTRODUCTION

A robot pours a sack of jumbled pieces from various graphs into a funnel.

Data engineering is the practice of creating infrastructure that can receive data from multiple sources, format or consolidate that data, then store it in databases for analysis. Many organizations require data engineers to track information like customer engagement metrics, financial transactions, and stock market movements. But when it comes to cybersecurity, data engineers are critical to connecting sensors to the analysts who can detect incidents.

Although generally invisible to all but those who manage them, the tools that ship data from one place to another are foundational to cybersecurity programs. Before an analyst can ...

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Publisher Resources

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