SlideShare a Scribd company logo
Phil Huggins
February 2004
 The goals of Forensic Readiness are to decrease the time and cost of
ForensicAnalysis (and ScopeAssessment) while increasing the
effectiveness.
 The main idea in Forensic Readiness is to build an infrastructure that
supports the needs (data) of an investigation
 The main areas include:
 Logging and monitoring
 Build Management & Inventory
 User Policies
 Reporting forms
 Data is critical to Forensic Analysis
 If the needed data is not being recorded, then
it can not be used in the investigation.
 Forensic Readiness assesses what network
and system information should be recorded
every day and what should be recorded
during an incident
 Goal:To create data entry forms that will contain the information that
needs to be gathered during an incident
 Every action performed during an incident should be documented
 Forms help to ensure that the proper data is recorded
 Examples:
 Chain of Custody: Records who has control of the data at a given time
 SystemAcquisition Form:When the response team takes a system from its
owner, this records the system description and owner signature
 Hard Disk Form: Records the history of each drive used during the
incident, including serial numbers and what systems it was installed in
 Investigator Log: Allows the responder to document their actions
 Form templates are included in your course handbook and will be
included on the course cd-rom.
 Log data can be crucial to the investigation
 There are two major issues with logging and
forensics:
1.Many incidents involve someone having
unauthorized privileged user access and most logs
can be modified or deleted by such a user.
2.Not all systems are logging the needed
information that is useful to an investigation
 All servers send a copy of their log data to a
dedicated log server
 Server can be on the normal network or a dedicated
network
 Server is secured to only allow log data (syslog) and
SSH access and is considered a critical asset when
patching systems
 Syslog Example:
 UNIX servers are configured to redirect syslog output
 Windows servers use 3rd party tools to send event logs to
server
 All logs can be analyzed on a periodic basis to detect
anomalies
 Makes it more difficult for attacker to modify the logs
 It is important to correlate events from multiple sources, so
we can compare the locally stored logs and the remotely
stored logs
 This server will be the target of many attacks, which may
alert one to other attacks if it is watched closely
 Windows stores logs in event files
 3rd party programs run on a scheduler and send new event
entries to the syslog server:
 Event Reporter (www.eventreporter.com)
 NT Syslog (www.ntsyslog.sourceforge.net)
 evlogsys.pl (perl script)
 Back Log (NT-Only)
 There is a slight window of opportunity with this model for
the attacker to delete the logs before the collection tool runs
 Goal:To ensure that the proper data is logged and that it is stored
in a method that can be used during forensics
 Send logs to central server to secure them during an attack
 Ensure log files have strict permissions so only a privileged user
can write to them.
 If possible, only allow the log to be appended to and deny all read
access
 Identify what OS events should be logged:
 User Logins
 System Reboots
 As much as possible, based on space requirements
 Process logging can require large amounts of storage
 Identify which application events should be logged:
 As much as possible, based on space requirements
 Log all network devices:
 Firewalls
 VPNs
 Routers
 Dialups
 Servers
 Use NetworkTime Protocol (NTP) to make log processing across
multiple machines easier
 Log by IP, do not resolve hostname
 Log Integrity
 Generate MD5 sums of log files when they are
saved and rolled over
 Use a secure (crypto-based) logging system:
 Core SDI
 syslog-ng
 IETF Secure Syslog
 Goal:To record needed network traffic to provide new evidence and
correlate activity. This is from the investigation perspective, not
detection.
 An IDS system can be used to record all events, but not generate
alerts
 A general sniffer can record all raw data
 tcpdump
 Ethereal
 Protocol analyzers can process raw output of tcpdump
 NetWitness
 Ethereal
 Available storage will be the only limitation of
how much data can be stored
 Specialized hardware or a SAN could be
worthwhile
 If monitoring is not always on, a dedicated
system should exist that can start monitoring
when an incident occurs
 Goal:To record host activity, not already being logged, which
will assist in a forensic investigation.
 This level of recording is needed for only the most sensitive
systems
 Keystroke recorders can be either:
 software: Run as services and can hide data in an encrypted file or will
email them to a remote location
 hardware: Device that the keyboard plugs into and saves the
keystrokes in hardware (does not record the window title)
 Goal:To document a system’s state
 A common task in forensics is to identify which binaries were
replaced with a trojan version
 Change management identifies which patch-level the
systems should be
 MD5 checksums can be calculated for each machine and
stored off-line (similar toTripwire)
 Configurations are recorded to identify which services are
supposed to be running and which are backdoors
 Goal:To document ownership of hardware
and addresses
 This is most useful with internal
investigations
 Allows one to identify the system with a
given MAC address (from DHCP logs)
 Allows one to identify who has a given
hostname (which is found in system logs)
 Goal:To set users expectation of privacy
appropriately
 An investigation may need access to a users
mailbox or other “private” data
 Identifying how much privacy users have should
be discussed before an incident occurs
 Data Protection Act requires users to be notified
and to accept any monitoring and for monitoring
to be a normal administration task. Suddenly
increasing monitoring is not acceptable under
the DPA.
 Goal:To build the infrastructure needed for an in-house
forensics lab (if one does not outsource it)
 The forensics lab has unique requirements from other
technology labs because of its legal requirements
 Location:
 Little traffic
 Secured by key badge or other auditable mechanism
 Camera surveillance
 Separate computer network
 A safe for long-term data storage (with sign-out sheets)
 Contents will vary depending on supported platforms
 At least one system of each supported platform
 Linux can mount most file system images and tools exist for
more advanced analysis (The Sleuth Kit)
 Windows does not have many tools native to it, but
specialized tools exist for analysis of windows systems
(EnCase etc.)
 Binary analysis capabilities
 Malicious code monitoring capabilities
 Many proactive steps can be performed to
effectively handle incidents
 Readiness forces an organization to consider
how to handle an incident before it occurs
 The amount of documentation required will
depend on the organization

More Related Content

What's hot

Network forensics and investigating logs
Network forensics and investigating logsNetwork forensics and investigating logs
Network forensics and investigating logs
anilinvns
 
CNIT 152: 6 Scoping & 7 Live Data Collection
CNIT 152: 6 Scoping & 7 Live Data CollectionCNIT 152: 6 Scoping & 7 Live Data Collection
CNIT 152: 6 Scoping & 7 Live Data Collection
Sam Bowne
 
Network Miner Network forensics
Network Miner Network forensicsNetwork Miner Network forensics
Network Miner Network forensics
Sreekanth Narendran
 
Usage aspects techniques for enterprise forensics data analytics tools
Usage aspects techniques for enterprise forensics data analytics toolsUsage aspects techniques for enterprise forensics data analytics tools
Usage aspects techniques for enterprise forensics data analytics tools
Damir Delija
 
Datafoucs 2014 on line digital forensic investigations damir delija 2
Datafoucs 2014 on line digital forensic investigations damir delija 2Datafoucs 2014 on line digital forensic investigations damir delija 2
Datafoucs 2014 on line digital forensic investigations damir delija 2
Damir Delija
 
Digital Forensics and Incident Response (DFIR) Training Session - January
Digital Forensics and Incident Response (DFIR) Training Session - JanuaryDigital Forensics and Incident Response (DFIR) Training Session - January
Digital Forensics and Incident Response (DFIR) Training Session - January
Infocyte
 
Defcon 23 - Chris Sistrunk - nsm 101 for ics
Defcon 23 -  Chris Sistrunk - nsm 101 for ics Defcon 23 -  Chris Sistrunk - nsm 101 for ics
Defcon 23 - Chris Sistrunk - nsm 101 for ics
Felipe Prado
 
Network Forensic
Network ForensicNetwork Forensic
Network Forensic
Sujeet Kumar
 
Network forensic
Network forensicNetwork forensic
Network forensic
Manjushree Mashal
 
Data Mining and Intrusion Detection
Data Mining and Intrusion Detection Data Mining and Intrusion Detection
Data Mining and Intrusion Detection
amiable_indian
 
Detection of Idle Stealth Port Scan Attack in Network Intrusion Detection Sys...
Detection of Idle Stealth Port Scan Attack in Network Intrusion Detection Sys...Detection of Idle Stealth Port Scan Attack in Network Intrusion Detection Sys...
Detection of Idle Stealth Port Scan Attack in Network Intrusion Detection Sys...
skpatel91
 
NIDS ppt
NIDS pptNIDS ppt
NIDS ppt
Mahendar Reddy
 
EnCase Enterprise Basic File Collection
EnCase Enterprise Basic File Collection EnCase Enterprise Basic File Collection
EnCase Enterprise Basic File Collection
Damir Delija
 
Ecase direct servlet acess v1
Ecase direct servlet acess  v1Ecase direct servlet acess  v1
Ecase direct servlet acess v1
Damir Delija
 
Using Machine Learning in Networks Intrusion Detection Systems
Using Machine Learning in Networks Intrusion Detection SystemsUsing Machine Learning in Networks Intrusion Detection Systems
Using Machine Learning in Networks Intrusion Detection Systems
Omar Shaya
 
Network forensics
Network forensicsNetwork forensics
Network forensics
ArthyR3
 
INTRUSION DETECTION TECHNIQUES
INTRUSION DETECTION TECHNIQUESINTRUSION DETECTION TECHNIQUES
INTRUSION DETECTION TECHNIQUES
Trinity Dwarka
 
SureLog SIEM
SureLog SIEMSureLog SIEM
SureLog SIEM
Ertugrul Akbas
 

What's hot (20)

Network forensics and investigating logs
Network forensics and investigating logsNetwork forensics and investigating logs
Network forensics and investigating logs
 
CNIT 152: 6 Scoping & 7 Live Data Collection
CNIT 152: 6 Scoping & 7 Live Data CollectionCNIT 152: 6 Scoping & 7 Live Data Collection
CNIT 152: 6 Scoping & 7 Live Data Collection
 
Network Miner Network forensics
Network Miner Network forensicsNetwork Miner Network forensics
Network Miner Network forensics
 
Ids
IdsIds
Ids
 
Usage aspects techniques for enterprise forensics data analytics tools
Usage aspects techniques for enterprise forensics data analytics toolsUsage aspects techniques for enterprise forensics data analytics tools
Usage aspects techniques for enterprise forensics data analytics tools
 
Datafoucs 2014 on line digital forensic investigations damir delija 2
Datafoucs 2014 on line digital forensic investigations damir delija 2Datafoucs 2014 on line digital forensic investigations damir delija 2
Datafoucs 2014 on line digital forensic investigations damir delija 2
 
Digital Forensics and Incident Response (DFIR) Training Session - January
Digital Forensics and Incident Response (DFIR) Training Session - JanuaryDigital Forensics and Incident Response (DFIR) Training Session - January
Digital Forensics and Incident Response (DFIR) Training Session - January
 
Defcon 23 - Chris Sistrunk - nsm 101 for ics
Defcon 23 -  Chris Sistrunk - nsm 101 for ics Defcon 23 -  Chris Sistrunk - nsm 101 for ics
Defcon 23 - Chris Sistrunk - nsm 101 for ics
 
Network Forensic
Network ForensicNetwork Forensic
Network Forensic
 
Network forensic
Network forensicNetwork forensic
Network forensic
 
Data Mining and Intrusion Detection
Data Mining and Intrusion Detection Data Mining and Intrusion Detection
Data Mining and Intrusion Detection
 
Detection of Idle Stealth Port Scan Attack in Network Intrusion Detection Sys...
Detection of Idle Stealth Port Scan Attack in Network Intrusion Detection Sys...Detection of Idle Stealth Port Scan Attack in Network Intrusion Detection Sys...
Detection of Idle Stealth Port Scan Attack in Network Intrusion Detection Sys...
 
Intrusion Detection
Intrusion DetectionIntrusion Detection
Intrusion Detection
 
NIDS ppt
NIDS pptNIDS ppt
NIDS ppt
 
EnCase Enterprise Basic File Collection
EnCase Enterprise Basic File Collection EnCase Enterprise Basic File Collection
EnCase Enterprise Basic File Collection
 
Ecase direct servlet acess v1
Ecase direct servlet acess  v1Ecase direct servlet acess  v1
Ecase direct servlet acess v1
 
Using Machine Learning in Networks Intrusion Detection Systems
Using Machine Learning in Networks Intrusion Detection SystemsUsing Machine Learning in Networks Intrusion Detection Systems
Using Machine Learning in Networks Intrusion Detection Systems
 
Network forensics
Network forensicsNetwork forensics
Network forensics
 
INTRUSION DETECTION TECHNIQUES
INTRUSION DETECTION TECHNIQUESINTRUSION DETECTION TECHNIQUES
INTRUSION DETECTION TECHNIQUES
 
SureLog SIEM
SureLog SIEMSureLog SIEM
SureLog SIEM
 

Viewers also liked

First Response - Session 11 - Incident Response [2004]
First Response - Session 11 - Incident Response [2004]First Response - Session 11 - Incident Response [2004]
First Response - Session 11 - Incident Response [2004]
Phil Huggins FBCS CITP
 
Countering Cyber Threats
Countering Cyber ThreatsCountering Cyber Threats
Countering Cyber Threats
Phil Huggins FBCS CITP
 
http://Chiropractor.forbostonmassachusetts.com
http://Chiropractor.forbostonmassachusetts.comhttp://Chiropractor.forbostonmassachusetts.com
http://Chiropractor.forbostonmassachusetts.com
Adalab
 
First Responders Course - Session 7 - Incident Scope Assessment [2004]
First Responders Course - Session 7 - Incident Scope Assessment [2004]First Responders Course - Session 7 - Incident Scope Assessment [2004]
First Responders Course - Session 7 - Incident Scope Assessment [2004]
Phil Huggins FBCS CITP
 
Diet Solution Reviews
Diet Solution ReviewsDiet Solution Reviews
Diet Solution Reviews
Adalab
 
Intelligence-led Cybersecurity
Intelligence-led Cybersecurity Intelligence-led Cybersecurity
Intelligence-led Cybersecurity
Phil Huggins FBCS CITP
 
Practical Security Architecture Analysis
Practical Security Architecture AnalysisPractical Security Architecture Analysis
Practical Security Architecture Analysis
Phil Huggins FBCS CITP
 
Security Metrics [2008]
Security Metrics [2008]Security Metrics [2008]
Security Metrics [2008]
Phil Huggins FBCS CITP
 
Cyber Resilience
Cyber ResilienceCyber Resilience
Cyber Resilience
Phil Huggins FBCS CITP
 
First Responders Course - Session 6 - Detection Systems [2004]
First Responders Course - Session 6 - Detection Systems [2004]First Responders Course - Session 6 - Detection Systems [2004]
First Responders Course - Session 6 - Detection Systems [2004]
Phil Huggins FBCS CITP
 
UK Legal Framework (2003)
UK Legal Framework (2003)UK Legal Framework (2003)
UK Legal Framework (2003)
Phil Huggins FBCS CITP
 
First Responder Course - Session 10 - Static Evidence Collection [2004]
First Responder Course - Session 10 - Static Evidence Collection [2004]First Responder Course - Session 10 - Static Evidence Collection [2004]
First Responder Course - Session 10 - Static Evidence Collection [2004]
Phil Huggins FBCS CITP
 
First Responders Course - Session 5 - First Response [2004]
First Responders Course - Session 5 - First Response [2004]First Responders Course - Session 5 - First Response [2004]
First Responders Course - Session 5 - First Response [2004]
Phil Huggins FBCS CITP
 
Cyber Resilience: Managing Cyber Shocks
Cyber Resilience: Managing Cyber ShocksCyber Resilience: Managing Cyber Shocks
Cyber Resilience: Managing Cyber Shocks
Phil Huggins FBCS CITP
 
Managing Insider Risk
Managing Insider RiskManaging Insider Risk
Managing Insider Risk
Phil Huggins FBCS CITP
 
Resilience is the new cyber security
Resilience is the new cyber securityResilience is the new cyber security
Resilience is the new cyber security
Phil Huggins FBCS CITP
 
Security Analytics Beyond Cyber
Security Analytics Beyond CyberSecurity Analytics Beyond Cyber
Security Analytics Beyond Cyber
Phil Huggins FBCS CITP
 
Shri krishna
Shri krishnaShri krishna
Shri krishna
kksharma786
 

Viewers also liked (18)

First Response - Session 11 - Incident Response [2004]
First Response - Session 11 - Incident Response [2004]First Response - Session 11 - Incident Response [2004]
First Response - Session 11 - Incident Response [2004]
 
Countering Cyber Threats
Countering Cyber ThreatsCountering Cyber Threats
Countering Cyber Threats
 
http://Chiropractor.forbostonmassachusetts.com
http://Chiropractor.forbostonmassachusetts.comhttp://Chiropractor.forbostonmassachusetts.com
http://Chiropractor.forbostonmassachusetts.com
 
First Responders Course - Session 7 - Incident Scope Assessment [2004]
First Responders Course - Session 7 - Incident Scope Assessment [2004]First Responders Course - Session 7 - Incident Scope Assessment [2004]
First Responders Course - Session 7 - Incident Scope Assessment [2004]
 
Diet Solution Reviews
Diet Solution ReviewsDiet Solution Reviews
Diet Solution Reviews
 
Intelligence-led Cybersecurity
Intelligence-led Cybersecurity Intelligence-led Cybersecurity
Intelligence-led Cybersecurity
 
Practical Security Architecture Analysis
Practical Security Architecture AnalysisPractical Security Architecture Analysis
Practical Security Architecture Analysis
 
Security Metrics [2008]
Security Metrics [2008]Security Metrics [2008]
Security Metrics [2008]
 
Cyber Resilience
Cyber ResilienceCyber Resilience
Cyber Resilience
 
First Responders Course - Session 6 - Detection Systems [2004]
First Responders Course - Session 6 - Detection Systems [2004]First Responders Course - Session 6 - Detection Systems [2004]
First Responders Course - Session 6 - Detection Systems [2004]
 
UK Legal Framework (2003)
UK Legal Framework (2003)UK Legal Framework (2003)
UK Legal Framework (2003)
 
First Responder Course - Session 10 - Static Evidence Collection [2004]
First Responder Course - Session 10 - Static Evidence Collection [2004]First Responder Course - Session 10 - Static Evidence Collection [2004]
First Responder Course - Session 10 - Static Evidence Collection [2004]
 
First Responders Course - Session 5 - First Response [2004]
First Responders Course - Session 5 - First Response [2004]First Responders Course - Session 5 - First Response [2004]
First Responders Course - Session 5 - First Response [2004]
 
Cyber Resilience: Managing Cyber Shocks
Cyber Resilience: Managing Cyber ShocksCyber Resilience: Managing Cyber Shocks
Cyber Resilience: Managing Cyber Shocks
 
Managing Insider Risk
Managing Insider RiskManaging Insider Risk
Managing Insider Risk
 
Resilience is the new cyber security
Resilience is the new cyber securityResilience is the new cyber security
Resilience is the new cyber security
 
Security Analytics Beyond Cyber
Security Analytics Beyond CyberSecurity Analytics Beyond Cyber
Security Analytics Beyond Cyber
 
Shri krishna
Shri krishnaShri krishna
Shri krishna
 

Similar to First Responders Course - Session 4 - Forensic Readiness [2004]

Network Forensics
Network ForensicsNetwork Forensics
Network Forensics
primeteacher32
 
Intrusion Discovery on Windows
Intrusion Discovery on WindowsIntrusion Discovery on Windows
Intrusion Discovery on Windows
dkaya
 
Report: Study and Implementation of Advance Intrusion Detection and Preventio...
Report: Study and Implementation of Advance Intrusion Detection and Preventio...Report: Study and Implementation of Advance Intrusion Detection and Preventio...
Report: Study and Implementation of Advance Intrusion Detection and Preventio...Deepak Mishra
 
Lecture 09 - Memory Forensics.pdfL E C T U R E 9 B Y .docx
Lecture 09 - Memory Forensics.pdfL E C T U R E  9  B Y .docxLecture 09 - Memory Forensics.pdfL E C T U R E  9  B Y .docx
Lecture 09 - Memory Forensics.pdfL E C T U R E 9 B Y .docx
smile790243
 
Digital Forensic ppt
Digital Forensic pptDigital Forensic ppt
Digital Forensic ppt
Suchita Rawat
 
Big Data Security Analytic Solution using Splunk
Big Data Security Analytic Solution using SplunkBig Data Security Analytic Solution using Splunk
Big Data Security Analytic Solution using Splunk
IJERA Editor
 
Infocyte - Digital Forensics and Incident Response (DFIR) Training Session
Infocyte - Digital Forensics and Incident Response (DFIR) Training SessionInfocyte - Digital Forensics and Incident Response (DFIR) Training Session
Infocyte - Digital Forensics and Incident Response (DFIR) Training Session
Infocyte
 
ArcSight Basics.ppt
ArcSight Basics.pptArcSight Basics.ppt
ArcSight Basics.ppt
neoalt
 
The objective of this assignment is to learnabout the IDS.Write .pdf
The objective of this assignment is to learnabout the IDS.Write .pdfThe objective of this assignment is to learnabout the IDS.Write .pdf
The objective of this assignment is to learnabout the IDS.Write .pdf
amitpalkar82
 
SIEM presentation final
SIEM presentation finalSIEM presentation final
SIEM presentation finalRizwan S
 
Computer forensics
Computer forensicsComputer forensics
Computer forensics
deaneal
 
computerforensics-140529094816-phpapp01 (1).pdf
computerforensics-140529094816-phpapp01 (1).pdfcomputerforensics-140529094816-phpapp01 (1).pdf
computerforensics-140529094816-phpapp01 (1).pdf
Gnanavi2
 
Introduction to Forensics and Steganography by Pardhasaradhi C
Introduction to Forensics and Steganography by Pardhasaradhi CIntroduction to Forensics and Steganography by Pardhasaradhi C
Introduction to Forensics and Steganography by Pardhasaradhi C
n|u - The Open Security Community
 
Intrusion detection
Intrusion detectionIntrusion detection
Intrusion detection
Programmer
 
Backtrack Manual Part8
Backtrack Manual Part8Backtrack Manual Part8
Backtrack Manual Part8
Nutan Kumar Panda
 
Wc4
Wc4Wc4
Incident handling is a clearly defined set of procedures to manage and respon...
Incident handling is a clearly defined set of procedures to manage and respon...Incident handling is a clearly defined set of procedures to manage and respon...
Incident handling is a clearly defined set of procedures to manage and respon...
Varun Mithran
 
Logs for Information Assurance and Forensics @ USMA
Logs for Information Assurance and Forensics @ USMALogs for Information Assurance and Forensics @ USMA
Logs for Information Assurance and Forensics @ USMA
Anton Chuvakin
 
Power of logs: practices for network security
Power of logs: practices for network securityPower of logs: practices for network security
Power of logs: practices for network security
Information Technology Society Nepal
 
Cryptography and system security
Cryptography and system securityCryptography and system security
Cryptography and system security
Gary Mendonca
 

Similar to First Responders Course - Session 4 - Forensic Readiness [2004] (20)

Network Forensics
Network ForensicsNetwork Forensics
Network Forensics
 
Intrusion Discovery on Windows
Intrusion Discovery on WindowsIntrusion Discovery on Windows
Intrusion Discovery on Windows
 
Report: Study and Implementation of Advance Intrusion Detection and Preventio...
Report: Study and Implementation of Advance Intrusion Detection and Preventio...Report: Study and Implementation of Advance Intrusion Detection and Preventio...
Report: Study and Implementation of Advance Intrusion Detection and Preventio...
 
Lecture 09 - Memory Forensics.pdfL E C T U R E 9 B Y .docx
Lecture 09 - Memory Forensics.pdfL E C T U R E  9  B Y .docxLecture 09 - Memory Forensics.pdfL E C T U R E  9  B Y .docx
Lecture 09 - Memory Forensics.pdfL E C T U R E 9 B Y .docx
 
Digital Forensic ppt
Digital Forensic pptDigital Forensic ppt
Digital Forensic ppt
 
Big Data Security Analytic Solution using Splunk
Big Data Security Analytic Solution using SplunkBig Data Security Analytic Solution using Splunk
Big Data Security Analytic Solution using Splunk
 
Infocyte - Digital Forensics and Incident Response (DFIR) Training Session
Infocyte - Digital Forensics and Incident Response (DFIR) Training SessionInfocyte - Digital Forensics and Incident Response (DFIR) Training Session
Infocyte - Digital Forensics and Incident Response (DFIR) Training Session
 
ArcSight Basics.ppt
ArcSight Basics.pptArcSight Basics.ppt
ArcSight Basics.ppt
 
The objective of this assignment is to learnabout the IDS.Write .pdf
The objective of this assignment is to learnabout the IDS.Write .pdfThe objective of this assignment is to learnabout the IDS.Write .pdf
The objective of this assignment is to learnabout the IDS.Write .pdf
 
SIEM presentation final
SIEM presentation finalSIEM presentation final
SIEM presentation final
 
Computer forensics
Computer forensicsComputer forensics
Computer forensics
 
computerforensics-140529094816-phpapp01 (1).pdf
computerforensics-140529094816-phpapp01 (1).pdfcomputerforensics-140529094816-phpapp01 (1).pdf
computerforensics-140529094816-phpapp01 (1).pdf
 
Introduction to Forensics and Steganography by Pardhasaradhi C
Introduction to Forensics and Steganography by Pardhasaradhi CIntroduction to Forensics and Steganography by Pardhasaradhi C
Introduction to Forensics and Steganography by Pardhasaradhi C
 
Intrusion detection
Intrusion detectionIntrusion detection
Intrusion detection
 
Backtrack Manual Part8
Backtrack Manual Part8Backtrack Manual Part8
Backtrack Manual Part8
 
Wc4
Wc4Wc4
Wc4
 
Incident handling is a clearly defined set of procedures to manage and respon...
Incident handling is a clearly defined set of procedures to manage and respon...Incident handling is a clearly defined set of procedures to manage and respon...
Incident handling is a clearly defined set of procedures to manage and respon...
 
Logs for Information Assurance and Forensics @ USMA
Logs for Information Assurance and Forensics @ USMALogs for Information Assurance and Forensics @ USMA
Logs for Information Assurance and Forensics @ USMA
 
Power of logs: practices for network security
Power of logs: practices for network securityPower of logs: practices for network security
Power of logs: practices for network security
 
Cryptography and system security
Cryptography and system securityCryptography and system security
Cryptography and system security
 

Recently uploaded

To Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMsTo Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
Paul Groth
 
How world-class product teams are winning in the AI era by CEO and Founder, P...
How world-class product teams are winning in the AI era by CEO and Founder, P...How world-class product teams are winning in the AI era by CEO and Founder, P...
How world-class product teams are winning in the AI era by CEO and Founder, P...
Product School
 
Accelerate your Kubernetes clusters with Varnish Caching
Accelerate your Kubernetes clusters with Varnish CachingAccelerate your Kubernetes clusters with Varnish Caching
Accelerate your Kubernetes clusters with Varnish Caching
Thijs Feryn
 
UiPath Test Automation using UiPath Test Suite series, part 3
UiPath Test Automation using UiPath Test Suite series, part 3UiPath Test Automation using UiPath Test Suite series, part 3
UiPath Test Automation using UiPath Test Suite series, part 3
DianaGray10
 
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
Product School
 
Generating a custom Ruby SDK for your web service or Rails API using Smithy
Generating a custom Ruby SDK for your web service or Rails API using SmithyGenerating a custom Ruby SDK for your web service or Rails API using Smithy
Generating a custom Ruby SDK for your web service or Rails API using Smithy
g2nightmarescribd
 
The Future of Platform Engineering
The Future of Platform EngineeringThe Future of Platform Engineering
The Future of Platform Engineering
Jemma Hussein Allen
 
Connector Corner: Automate dynamic content and events by pushing a button
Connector Corner: Automate dynamic content and events by pushing a buttonConnector Corner: Automate dynamic content and events by pushing a button
Connector Corner: Automate dynamic content and events by pushing a button
DianaGray10
 
UiPath Test Automation using UiPath Test Suite series, part 4
UiPath Test Automation using UiPath Test Suite series, part 4UiPath Test Automation using UiPath Test Suite series, part 4
UiPath Test Automation using UiPath Test Suite series, part 4
DianaGray10
 
DevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA ConnectDevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA Connect
Kari Kakkonen
 
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR EventsMonitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
Ana-Maria Mihalceanu
 
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Albert Hoitingh
 
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
UiPathCommunity
 
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdfFIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance
 
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Jeffrey Haguewood
 
Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*
Frank van Harmelen
 
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
BookNet Canada
 
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
KatiaHIMEUR1
 
The Art of the Pitch: WordPress Relationships and Sales
The Art of the Pitch: WordPress Relationships and SalesThe Art of the Pitch: WordPress Relationships and Sales
The Art of the Pitch: WordPress Relationships and Sales
Laura Byrne
 
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Designing Great Products: The Power of Design and Leadership by Chief Designe...Designing Great Products: The Power of Design and Leadership by Chief Designe...
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Product School
 

Recently uploaded (20)

To Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMsTo Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
 
How world-class product teams are winning in the AI era by CEO and Founder, P...
How world-class product teams are winning in the AI era by CEO and Founder, P...How world-class product teams are winning in the AI era by CEO and Founder, P...
How world-class product teams are winning in the AI era by CEO and Founder, P...
 
Accelerate your Kubernetes clusters with Varnish Caching
Accelerate your Kubernetes clusters with Varnish CachingAccelerate your Kubernetes clusters with Varnish Caching
Accelerate your Kubernetes clusters with Varnish Caching
 
UiPath Test Automation using UiPath Test Suite series, part 3
UiPath Test Automation using UiPath Test Suite series, part 3UiPath Test Automation using UiPath Test Suite series, part 3
UiPath Test Automation using UiPath Test Suite series, part 3
 
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
 
Generating a custom Ruby SDK for your web service or Rails API using Smithy
Generating a custom Ruby SDK for your web service or Rails API using SmithyGenerating a custom Ruby SDK for your web service or Rails API using Smithy
Generating a custom Ruby SDK for your web service or Rails API using Smithy
 
The Future of Platform Engineering
The Future of Platform EngineeringThe Future of Platform Engineering
The Future of Platform Engineering
 
Connector Corner: Automate dynamic content and events by pushing a button
Connector Corner: Automate dynamic content and events by pushing a buttonConnector Corner: Automate dynamic content and events by pushing a button
Connector Corner: Automate dynamic content and events by pushing a button
 
UiPath Test Automation using UiPath Test Suite series, part 4
UiPath Test Automation using UiPath Test Suite series, part 4UiPath Test Automation using UiPath Test Suite series, part 4
UiPath Test Automation using UiPath Test Suite series, part 4
 
DevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA ConnectDevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA Connect
 
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR EventsMonitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
 
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
 
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
 
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdfFIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
 
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
 
Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*
 
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
 
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
 
The Art of the Pitch: WordPress Relationships and Sales
The Art of the Pitch: WordPress Relationships and SalesThe Art of the Pitch: WordPress Relationships and Sales
The Art of the Pitch: WordPress Relationships and Sales
 
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Designing Great Products: The Power of Design and Leadership by Chief Designe...Designing Great Products: The Power of Design and Leadership by Chief Designe...
Designing Great Products: The Power of Design and Leadership by Chief Designe...
 

First Responders Course - Session 4 - Forensic Readiness [2004]

  • 2.  The goals of Forensic Readiness are to decrease the time and cost of ForensicAnalysis (and ScopeAssessment) while increasing the effectiveness.  The main idea in Forensic Readiness is to build an infrastructure that supports the needs (data) of an investigation  The main areas include:  Logging and monitoring  Build Management & Inventory  User Policies  Reporting forms
  • 3.  Data is critical to Forensic Analysis  If the needed data is not being recorded, then it can not be used in the investigation.  Forensic Readiness assesses what network and system information should be recorded every day and what should be recorded during an incident
  • 4.  Goal:To create data entry forms that will contain the information that needs to be gathered during an incident  Every action performed during an incident should be documented  Forms help to ensure that the proper data is recorded  Examples:  Chain of Custody: Records who has control of the data at a given time  SystemAcquisition Form:When the response team takes a system from its owner, this records the system description and owner signature  Hard Disk Form: Records the history of each drive used during the incident, including serial numbers and what systems it was installed in  Investigator Log: Allows the responder to document their actions  Form templates are included in your course handbook and will be included on the course cd-rom.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.  Log data can be crucial to the investigation  There are two major issues with logging and forensics: 1.Many incidents involve someone having unauthorized privileged user access and most logs can be modified or deleted by such a user. 2.Not all systems are logging the needed information that is useful to an investigation
  • 10.  All servers send a copy of their log data to a dedicated log server  Server can be on the normal network or a dedicated network  Server is secured to only allow log data (syslog) and SSH access and is considered a critical asset when patching systems  Syslog Example:  UNIX servers are configured to redirect syslog output  Windows servers use 3rd party tools to send event logs to server
  • 11.  All logs can be analyzed on a periodic basis to detect anomalies  Makes it more difficult for attacker to modify the logs  It is important to correlate events from multiple sources, so we can compare the locally stored logs and the remotely stored logs  This server will be the target of many attacks, which may alert one to other attacks if it is watched closely
  • 12.  Windows stores logs in event files  3rd party programs run on a scheduler and send new event entries to the syslog server:  Event Reporter (www.eventreporter.com)  NT Syslog (www.ntsyslog.sourceforge.net)  evlogsys.pl (perl script)  Back Log (NT-Only)  There is a slight window of opportunity with this model for the attacker to delete the logs before the collection tool runs
  • 13.  Goal:To ensure that the proper data is logged and that it is stored in a method that can be used during forensics  Send logs to central server to secure them during an attack  Ensure log files have strict permissions so only a privileged user can write to them.  If possible, only allow the log to be appended to and deny all read access  Identify what OS events should be logged:  User Logins  System Reboots  As much as possible, based on space requirements  Process logging can require large amounts of storage
  • 14.  Identify which application events should be logged:  As much as possible, based on space requirements  Log all network devices:  Firewalls  VPNs  Routers  Dialups  Servers  Use NetworkTime Protocol (NTP) to make log processing across multiple machines easier  Log by IP, do not resolve hostname
  • 15.  Log Integrity  Generate MD5 sums of log files when they are saved and rolled over  Use a secure (crypto-based) logging system:  Core SDI  syslog-ng  IETF Secure Syslog
  • 16.  Goal:To record needed network traffic to provide new evidence and correlate activity. This is from the investigation perspective, not detection.  An IDS system can be used to record all events, but not generate alerts  A general sniffer can record all raw data  tcpdump  Ethereal  Protocol analyzers can process raw output of tcpdump  NetWitness  Ethereal
  • 17.  Available storage will be the only limitation of how much data can be stored  Specialized hardware or a SAN could be worthwhile  If monitoring is not always on, a dedicated system should exist that can start monitoring when an incident occurs
  • 18.  Goal:To record host activity, not already being logged, which will assist in a forensic investigation.  This level of recording is needed for only the most sensitive systems  Keystroke recorders can be either:  software: Run as services and can hide data in an encrypted file or will email them to a remote location  hardware: Device that the keyboard plugs into and saves the keystrokes in hardware (does not record the window title)
  • 19.  Goal:To document a system’s state  A common task in forensics is to identify which binaries were replaced with a trojan version  Change management identifies which patch-level the systems should be  MD5 checksums can be calculated for each machine and stored off-line (similar toTripwire)  Configurations are recorded to identify which services are supposed to be running and which are backdoors
  • 20.  Goal:To document ownership of hardware and addresses  This is most useful with internal investigations  Allows one to identify the system with a given MAC address (from DHCP logs)  Allows one to identify who has a given hostname (which is found in system logs)
  • 21.  Goal:To set users expectation of privacy appropriately  An investigation may need access to a users mailbox or other “private” data  Identifying how much privacy users have should be discussed before an incident occurs  Data Protection Act requires users to be notified and to accept any monitoring and for monitoring to be a normal administration task. Suddenly increasing monitoring is not acceptable under the DPA.
  • 22.  Goal:To build the infrastructure needed for an in-house forensics lab (if one does not outsource it)  The forensics lab has unique requirements from other technology labs because of its legal requirements  Location:  Little traffic  Secured by key badge or other auditable mechanism  Camera surveillance  Separate computer network  A safe for long-term data storage (with sign-out sheets)
  • 23.  Contents will vary depending on supported platforms  At least one system of each supported platform  Linux can mount most file system images and tools exist for more advanced analysis (The Sleuth Kit)  Windows does not have many tools native to it, but specialized tools exist for analysis of windows systems (EnCase etc.)  Binary analysis capabilities  Malicious code monitoring capabilities
  • 24.  Many proactive steps can be performed to effectively handle incidents  Readiness forces an organization to consider how to handle an incident before it occurs  The amount of documentation required will depend on the organization