Security From The Big Data and Analytics Perspective

All Things Open
All Things OpenAll Things Open
All Things Open
Agenda
• Intro, who I am.
• Cybersecurity
• ONI now Apache Spot (incubating)
• Apache Spot (incubating)
• Demo
• Call to Action.
• Q&A
Cybersecurity
• We have gaps… The analysis of billions of events , orchestrate our
data sources (logs in different forms), and sometimes the
documentation of our security products is not the best.
The hacker community collaborates everyday, it’s time we
do the same.
Services Products Training
Free$100
Learn to Crack
Wifi
Hack a Corporate
Email Account
Angler
Exploit Kits
$500
Asset
Inventory
Configuration
Guidance
Analysis
Vulnerability
Analysis
Threat
Analysis
Intrusion
Detection
Vulnerability
Alert
Configuration
Guidance
Asset
Definition
Threat
Alert
Incident
Report
Incident
Management
Operational Enterprise Networks
Centralized Reporting
Enterprise IT
Change Management
Development &
Sustainment
Security
Management
Processes
Assessment
of System
Development,
Integration, &
Sustainment
Activities
and
Certification &
Accreditation
System &
Software
Assurance
Guidance/
Requirements
Operations Security Management Processes
Enterprise IT Asset Management
CCE/
CCSS/
OVAL/ARF/
XCCDF/CPE
CVE/CWE/
CVSS/ARF/
CCE/CCSS/
ARF/CWSS/
OVAL/CPE/
XCCDF
CVE/CWE/
CVSS/ARF/
CCE/CCSS/
OVAL/CWSS/
XCCDF/CPE/
CAPEC/MAEC
CVE/CWE/
CVSS/ARF/.
CCE/OVAL/CCSS/
XCCDF/CPE/
CAPEC/CWSS/
MAEC/CEE
CPE/
OVAL/
ARF
CWE/CAPEC/C
WSS/MAEC/OV
AL/OCIL/XCCDF
/CCE/CPE/ARF/
SAFES/SACM
CWE/CAPEC/
SBVR/CWSS/
MAEC
OVAL/XCCDF/
CCE/CCSS/
CPE/ARF
CPE/OVAL XCCDF/OVAL/
CCE/CCSS
CVE/CWE/OVAL/
CVSS/CWSS
CAIF/IDMEF/IODEF/CVE/CWE/
OVAL/CPE/MAEC/CCSS/CWSS/
CEE/ARF
CVE/CWE/CVSS/
CPE/CWSS/
CAPEC/MAEC
SCAP
SwAAP CVE/CWE/CVSS/
CCE/CCSS/OVAL/
XCCDF/
CPE/CAPEC/MAE
C/CWSS/CEE/ARF CVE/CWE/CVSS/CCE/CCSS/OVAL/XCCDF/
CPE/CAPEC/MAEC/CWSS/CEE/ARF
Trust
Management
Identity
Management
EMAP
ERAP ECAP
ESIP ITAPTAAP
ONI -> Apache Spot (incubating)
• Apache spot (incubating) is an advanced analytic solution that will help us to
close the gaps that we are mention on the previous slides.
• Ingesting billions of records in HDFS and execute machine learning algorithms, to
detect potential threats in our environment.
Apache Spot (incubating) Core
ONIDataSources
DNS
Infrastructure Logs
Proxy
Infrastructure
Logs
Routers with
Netflow Protocol
Enabled on
Interfaces
New Data Source
New Data
Source
ONI
Visualization
Server / iPython
Server
ONIGUI
Security and
Context Use
Cases Develop in
Conjunction with
Intel Security
Assumes Cloudera Hadoop Environment
Data Integration Data Store Machine Learning
Collectors
Online
NoSQL
(HBase)
Filesystem
(HDFS)
Spot ML
Algorithms
Spark
Master Node (s) Cloudera
Manager/Navigator
Machine learning
Algoritms Output,
ONI Recommended
the Intel MPI
Libraries. Scala
Native Administration
Cloudera Manager
Cluster Authentication
LDAP/Kerberos
Authentication
Machine learning
Generates CSV Files
with the Results
Operational Analytics Adding
Context Using Reputation Services
for Public IP Address (GTI)
Defining the
Interface to Share
the Suspicious
Connections with I-
Sec Products and
Other Brands.
Product Architectural Overview
Apache Spot delivers…
1. Scalable Data and Analytics Platform
2. Open Data Models
3. Analytic Collaboration Across the Community
4. Growing Application Ecosystem
… to address cybersecurity use cases.
• Network Traffic Analytics
• Threat Hunting
• Incident Detection and Resolution
• Cybersecurity Data Management
• Custom Use Case
PlatformApache Spot, bringing all of the components together.
DataManagement
Apache Spot Sample Data Sets
Apache Spot Open Data Models (ODM)
Data Platform (CDH)
Ingestion (Kafka, Flume, Streamsets)
Analytics
Apache Spot OSS Analytics
Analytic Services (Jupyter, Apache Spark)
App
s
Apache Spot ODM Marketplace
Infra
Intel Hardware, On-Prem, AWS, Azure
Management,Security,Governance
(Director,Manager,Sentry,Navigator)
Public or private clouds
Scalable storage and distributed processing
Provisioning, management, and security
Batch and stream data ingestion
Logical and physical models
Data Science workbench
Network traffic analytics, Add’l OSS analytics
ODM Compliant ecosystem, both open source and
ISV
Community sourced, anonymized data sets for
model development
Demo 
Call to Action.
Contribute for the Apache Spot (incubating) project.
1. Develop connectors to ingest more data
2. Develop new algorithms that help us to increase the detection rate of the tool
3. Contribute to add Context to this results, adding threat intelligence feeds
connector to databases that will help us to present meaningful information to
the end user.
4. Develop the User Interface, propose changes, technologies, operational
summaries, reports, etc.
Call to Action.
5. Integrate Apache Spot (incubating) with other security tools, that have the
capabilities to enforce / change security postures. (Firewall consoles, IPS
consoles, Proxies, Endpoint Security Solutions, E-mail proxies)
6. Contac us
• Web page: http://spot.apache.org/
• slack: slack.apache-spot.io/
• twitter @ApacheSpot
7. Contribute to the Apache Spot (incubating) project.
With Apache Spot, you are joining a community.
Collaborate with industry leaders using a common
framework.
Apache Spot
(Incubating)
Join the community that is fighting cyber threats
today.
spot.incubator.apache.org
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Security From The Big Data and Analytics Perspective

  • 2. Agenda • Intro, who I am. • Cybersecurity • ONI now Apache Spot (incubating) • Apache Spot (incubating) • Demo • Call to Action. • Q&A
  • 3. Cybersecurity • We have gaps… The analysis of billions of events , orchestrate our data sources (logs in different forms), and sometimes the documentation of our security products is not the best.
  • 4. The hacker community collaborates everyday, it’s time we do the same. Services Products Training Free$100 Learn to Crack Wifi Hack a Corporate Email Account Angler Exploit Kits $500
  • 5. Asset Inventory Configuration Guidance Analysis Vulnerability Analysis Threat Analysis Intrusion Detection Vulnerability Alert Configuration Guidance Asset Definition Threat Alert Incident Report Incident Management Operational Enterprise Networks Centralized Reporting Enterprise IT Change Management Development & Sustainment Security Management Processes Assessment of System Development, Integration, & Sustainment Activities and Certification & Accreditation System & Software Assurance Guidance/ Requirements Operations Security Management Processes Enterprise IT Asset Management CCE/ CCSS/ OVAL/ARF/ XCCDF/CPE CVE/CWE/ CVSS/ARF/ CCE/CCSS/ ARF/CWSS/ OVAL/CPE/ XCCDF CVE/CWE/ CVSS/ARF/ CCE/CCSS/ OVAL/CWSS/ XCCDF/CPE/ CAPEC/MAEC CVE/CWE/ CVSS/ARF/. CCE/OVAL/CCSS/ XCCDF/CPE/ CAPEC/CWSS/ MAEC/CEE CPE/ OVAL/ ARF CWE/CAPEC/C WSS/MAEC/OV AL/OCIL/XCCDF /CCE/CPE/ARF/ SAFES/SACM CWE/CAPEC/ SBVR/CWSS/ MAEC OVAL/XCCDF/ CCE/CCSS/ CPE/ARF CPE/OVAL XCCDF/OVAL/ CCE/CCSS CVE/CWE/OVAL/ CVSS/CWSS CAIF/IDMEF/IODEF/CVE/CWE/ OVAL/CPE/MAEC/CCSS/CWSS/ CEE/ARF CVE/CWE/CVSS/ CPE/CWSS/ CAPEC/MAEC SCAP SwAAP CVE/CWE/CVSS/ CCE/CCSS/OVAL/ XCCDF/ CPE/CAPEC/MAE C/CWSS/CEE/ARF CVE/CWE/CVSS/CCE/CCSS/OVAL/XCCDF/ CPE/CAPEC/MAEC/CWSS/CEE/ARF Trust Management Identity Management EMAP ERAP ECAP ESIP ITAPTAAP
  • 6. ONI -> Apache Spot (incubating) • Apache spot (incubating) is an advanced analytic solution that will help us to close the gaps that we are mention on the previous slides. • Ingesting billions of records in HDFS and execute machine learning algorithms, to detect potential threats in our environment.
  • 7. Apache Spot (incubating) Core ONIDataSources DNS Infrastructure Logs Proxy Infrastructure Logs Routers with Netflow Protocol Enabled on Interfaces New Data Source New Data Source ONI Visualization Server / iPython Server ONIGUI Security and Context Use Cases Develop in Conjunction with Intel Security Assumes Cloudera Hadoop Environment Data Integration Data Store Machine Learning Collectors Online NoSQL (HBase) Filesystem (HDFS) Spot ML Algorithms Spark Master Node (s) Cloudera Manager/Navigator Machine learning Algoritms Output, ONI Recommended the Intel MPI Libraries. Scala Native Administration Cloudera Manager Cluster Authentication LDAP/Kerberos Authentication Machine learning Generates CSV Files with the Results Operational Analytics Adding Context Using Reputation Services for Public IP Address (GTI) Defining the Interface to Share the Suspicious Connections with I- Sec Products and Other Brands. Product Architectural Overview
  • 8. Apache Spot delivers… 1. Scalable Data and Analytics Platform 2. Open Data Models 3. Analytic Collaboration Across the Community 4. Growing Application Ecosystem … to address cybersecurity use cases. • Network Traffic Analytics • Threat Hunting • Incident Detection and Resolution • Cybersecurity Data Management • Custom Use Case
  • 9. PlatformApache Spot, bringing all of the components together. DataManagement Apache Spot Sample Data Sets Apache Spot Open Data Models (ODM) Data Platform (CDH) Ingestion (Kafka, Flume, Streamsets) Analytics Apache Spot OSS Analytics Analytic Services (Jupyter, Apache Spark) App s Apache Spot ODM Marketplace Infra Intel Hardware, On-Prem, AWS, Azure Management,Security,Governance (Director,Manager,Sentry,Navigator) Public or private clouds Scalable storage and distributed processing Provisioning, management, and security Batch and stream data ingestion Logical and physical models Data Science workbench Network traffic analytics, Add’l OSS analytics ODM Compliant ecosystem, both open source and ISV Community sourced, anonymized data sets for model development
  • 11. Call to Action. Contribute for the Apache Spot (incubating) project. 1. Develop connectors to ingest more data 2. Develop new algorithms that help us to increase the detection rate of the tool 3. Contribute to add Context to this results, adding threat intelligence feeds connector to databases that will help us to present meaningful information to the end user. 4. Develop the User Interface, propose changes, technologies, operational summaries, reports, etc.
  • 12. Call to Action. 5. Integrate Apache Spot (incubating) with other security tools, that have the capabilities to enforce / change security postures. (Firewall consoles, IPS consoles, Proxies, Endpoint Security Solutions, E-mail proxies) 6. Contac us • Web page: http://spot.apache.org/ • slack: slack.apache-spot.io/ • twitter @ApacheSpot 7. Contribute to the Apache Spot (incubating) project.
  • 13. With Apache Spot, you are joining a community. Collaborate with industry leaders using a common framework.
  • 14. Apache Spot (Incubating) Join the community that is fighting cyber threats today. spot.incubator.apache.org

Notas del editor

  1. Rules and patterns most of the time on the cyber side.. DDoS, The internet apocalypse map hides the major vulnerability that created it… China stuff 
  2. https://cloudera.my.salesforce.com/06934000001jGcw Hire a hacker - Hack corporate email account without them knowing or needing to change the password. Hacker can then forgot password and reset password to critical applications. Buy a product that helps you hack - Angler exploit kits help infect users with malware. The malware is delivered to the user when they visit a site that has the kit deployed on it. Get trained by the best hackers on Youtube – Anyone can know learn how to hack a corporation.
  3. Enterprise System Information Protocol (ESIP) For reporting of asset inventory information. Common Platform Enumeration (CPE), etc. Threat Analysis Automation Protocol (TAAP) For reporting and sharing structured threat information. Malware Attribute Enumeration & Characterization (MAEC), Common Attack Pattern Enumeration & Classification (CAPEC), Common Platform Enumeration (CPE), Common Weakness Enumeration (CWE), Open Vulnerability and Assessment Language (OVAL), Common Configuration Enumeration (CCE), and Common Vulnerabilities and Exposures (CVE). Event Management Automation Protocol (EMAP) For reporting of security events. Common Event Expression (CEE), Malware Attribute Enumeration & Characterization (MAEC), and Common Attack Pattern Enumeration & Classification (CAPEC). Incident Tracking and Assessment Protocol (ITAP) For tracking, reporting, managing and sharing incident information. Open Vulnerability and Assessment Language (OVAL), Common Platform Enumeration (CPE), Common Configuration Enumeration (CCE), Common Vulnerabilities and Exposures (CVE), Common Vulnerability Scoring System (CVSS), Malware Attribute Enumeration & Characterization (MAEC), Common Attack Pattern Enumeration & Classification (CAPEC), Common Weakness Enumeration (CWE), Common Event Expression (CEE), Incident Object Description Exchange Format (IODEF), National Information Exchange Model (NIEM), and Cybersecurity Information Exchange Format (CYBEX). Enterprise Remediation Automation Protocol (ERAP) For automated remediation of mis-configuration & missing patches. Common Remediation Enumeration (CRE), Extended Remediation Information (ERI), Open Vulnerability and Assessment Language (OVAL), Common Platform Enumeration (CPE), and Common Configuration Enumeration (CCE). Enterprise Compliance Automation Protocol (ECAP) For reporting configuration compliance. Asset Reporting Format (ARF), Open Checklist Reporting Language (OCRL), etc.
  4. In more detail, LDA represents documents as mixtures of topics that spit out words with certain probabilities. It assumes that documents are produced in the following fashion: when writing each document, you decide on the number of words N the document will have (say, according to a Poisson distribution). Choose a topic mixture for the document (according to a Dirichlet distribution over a fixed set of K topics). For example, assuming that we have the two food and cute animal topics above, you might choose the document to consist of 1/3 food and 2/3 cute animals. Generate each word w_i in the document by: First picking a topic (according to the multinomial distribution that you sampled above; for example, you might pick the food topic with 1/3 probability and the cute animals topic with 2/3 probability). Using the topic to generate the word itself (according to the topic’s multinomial distribution). For example, if we selected the food topic, we might generate the word “broccoli” with 30% probability, “bananas” with 15% probability, and so on. Assuming this generative model for a collection of documents, LDA then tries to backtrack from the documents to find a set of topics that are likely to have generated the collection.