SlideShare a Scribd company logo
DECEPTION
TECHNOLOGIES
Raj Gopalakrishna
Co-founder & Chief Product Architect
AcalvioTechnologiesCopyright AcalvioTechnologies 1
Brief History of Deception
DeceptionTypes
Under the Hood
Some Use Cases
Touch Points
Deception over the years
• Millions of years in Natural World for
survival/aggression
• Millions of years in bacteria and virus to
thrive
• 1000s of years in Warfare/Military to attack
or defend
• Decades in Cyber Warfare
• Attackers use Deception
• Phishing, spoofing, encryption
• Defender should use Deceptions
• Honeypots, Cryptographic Camouflage
• French Election used it recently
Owl Butterfly for Survival
3Copyright AcalvioTechnologies
Transmitter
Passion Fruit Leaf
with spots
UNDER
THE
HOOD
Copyright AcalvioTechnologies 4
Breadcrumbs: Extend Deceptions to Production Devices
Many flavors and forms:
1. Registry entries
2. Files & Folders
3. Memory hashes
4. User Profiles
5. Browser cookies
Few Challenges:
1. Need deployment Automation
and Intelligence
2. Avoid Accidental Alerts by
Users
5
DeceptionAnywhere or Everywhere
Copyright AcalvioTechnologies
Lures: Another powerful arrow in the quiver
Deliberately placed
1. Vulnerabilities in OS, Application,
Protocols
2. Weak configurations and permissions
3. Powerful fake Service Accounts
4. Shares
5. Interesting Data
6
Make Deceptions more attractive
Copyright AcalvioTechnologies
DecoyTypes
Low Interaction Deceptions
• Attacker typically cannot login
• Emulated Hosts, Applications,
Database Servers
High Interaction Deceptions
• Attacker can login – full interaction
• RealVM Hosts, Applications, Database
Servers, Shares
Copyright AcalvioTechnologies 7
Low Interaction Deceptions
 Deploy OS, Network services orApplications
 Lots of deceptions possible.
 Low IT cost
 Low Risk to Enterprise Networks
 Dynamic: easy to morph on-the-fly
 Need not be emulations!
× Cannot Engage with theAttacker
× If Emulated then Easy to fingerprint Deceptions
Key Challenge:
Odds of attacker identifying deceptions
Copyright AcalvioTechnologies 8
High Interaction Deceptions
Deploy real OS, Services, Applications
Deceptions are not finger-printable.
Possible to Engage with Attacker
× Only Few deceptions
× High Cost of licensing & maintaining
× Need Containment to reduce RISK
× Static: pre-build, unable to morph quickly
× Often used with Breadcrumbs to lead
attacker to Decoys. But then attacker needs
to find breadcrumbs first
Key Challenge:
Odds of Attacker/Malware running into the few deceptions
Copyright AcalvioTechnologies 9
Often we need both
Scale and Depth (Believable Deceptions)
Copyright AcalvioTechnologies 10
Static vs Dynamic Deceptions
Static Deceptions
• Hardly changes
• Easy to fingerprint & avoid
Dynamic Deceptions
Mimic Octopus:
Mimics upto 15 creatures
ActiveCamouflage:
Counter-illumination by Squids
• Changing always
• Hard to predict or identify
HoneyAnts
Copyright AcalvioTechnologies
11
Intelligence Component
Human only
• Expert decides type and number of
deceptions to deploy
• Manually/Automatically configures
traps atTime T0
Key Challenges:
• What happens atTimeT1 orT10 ?
• How many Experts can company
send to front-line for 24x7x365?
Human + AI based = Future
System recommends type, number,
placement, duration of deceptions.
System Responds to
• Events and Incidents
• Adversary Behavior
• When you are sleeping
Copyright AcalvioTechnologies 12
Some major Challenges in Cyber Security
Compromise Detection Identifying malicious intent
© AcalvioTechnologiesCompany 13
Alerts Deluge Too many False positives
DeceptionTechnology can help in all of above
Internal Facing vs External Facing
Deceptions
Internal Facing
• Good for Enterprises
• A new layer of Defense
• Acts like a motion detector inside
Enterprises
• Corporate Network
• Data Centers
• Detects attackers who have gone past
the perimeter defenses
• Few, High FidelityAlerts raised
• Can optionally Engage & Respond
External Facing
• Great for security researchers
• Typically deployed on the Internet or
in the DMZ.
• Lots of alerts per hour/day as there
are lots of malicious Attackers and
Bots on the Internet
• Often used to show demo of
DeceptionTechnologies
Copyright AcalvioTechnologies 14
Detecting Ransomware: current
approaches
AV and Sandbox approach
• Look for known Signatures
• Look for known C&C
 Low False +ve
× High False -ve
Data Science approach
• Look for Anomalous Behavior
• High File I/O
• Lots of different Files accessed
• Lots of crypto
× Anomaly ≠Threat
× High False +ve
15Copyright AcalvioTechnologies
Detecting Ransomware using Deceptions
• Leverages Decoys,
Breadcrumbs and Lures
• Set specific traps in specific
locations
• Monitor only activity against
decoys, breadcrumbs & lures
Auto Detects and confirms
Ransomware
Very Efficient and Accurate
16
Always High Fidelity Signals
Zero false +ve
Copyright AcalvioTechnologies
Protecting Secrets in
software is hard
Examples
Crypto keys
Passwords
Payment card
numbers
Copyright AcalvioTechnologies 17
THANKYOU
Copyright AcalvioTechnologies 18
• Raj Gopalakrishna
• raj@Acalvio.com
• AcalvioTechnologies Inc
CONTACT ACALVIO
IFYOU ARE LOOKING FOR
DECEPTION PRODUCT
Copyright AcalvioTechnologies 19

More Related Content

PPTX
Deception technology for advanced detection
PPTX
Cyber Deception - AttivoNetwork
PDF
SACON - Deception Technology (Sahir Hidayatullah)
PPTX
Honeypot ppt1
PDF
Threat Intelligence
PDF
MITRE ATT&CKcon 2.0: State of the ATT&CK; Blake Strom, MITRE
PPTX
SOC Cyber Security
PPTX
Penetration testing
Deception technology for advanced detection
Cyber Deception - AttivoNetwork
SACON - Deception Technology (Sahir Hidayatullah)
Honeypot ppt1
Threat Intelligence
MITRE ATT&CKcon 2.0: State of the ATT&CK; Blake Strom, MITRE
SOC Cyber Security
Penetration testing

What's hot (20)

PPT
Honeypots
PDF
Knowledge for the masses: Storytelling with ATT&CK
PPTX
PDF
MITRE ATT&CKcon 2.0: Using Threat Intelligence to Focus ATT&CK Activities; Da...
PPTX
Cyber threat intelligence: maturity and metrics
PDF
Cyber Deception After Detection: Safe Observation Environment Using Software ...
PPTX
Cyber Threat Intelligence
PDF
Using MITRE PRE-ATTACK and ATTACK in Cybercrime Education and Research
PPTX
How is ai important to the future of cyber security
PPTX
Adversary Emulation and the C2 Matrix
PPTX
Cyber Threat Intelligence | Information to Insight
PPTX
Application Threat Modeling
PDF
Building an InfoSec RedTeam
PPTX
Tushar mandal.honeypot
PPT
Cyber Crime
PPTX
Artificial Intelligence and Cybersecurity
PDF
Zero Trust Model Presentation
PDF
Detecting fraud with Python and machine learning
PPTX
The Zero Trust Model of Information Security
Honeypots
Knowledge for the masses: Storytelling with ATT&CK
MITRE ATT&CKcon 2.0: Using Threat Intelligence to Focus ATT&CK Activities; Da...
Cyber threat intelligence: maturity and metrics
Cyber Deception After Detection: Safe Observation Environment Using Software ...
Cyber Threat Intelligence
Using MITRE PRE-ATTACK and ATTACK in Cybercrime Education and Research
How is ai important to the future of cyber security
Adversary Emulation and the C2 Matrix
Cyber Threat Intelligence | Information to Insight
Application Threat Modeling
Building an InfoSec RedTeam
Tushar mandal.honeypot
Cyber Crime
Artificial Intelligence and Cybersecurity
Zero Trust Model Presentation
Detecting fraud with Python and machine learning
The Zero Trust Model of Information Security
Ad

Similar to Deception Technology: Use Cases & Implementation Approaches (20)

PDF
Strengthening Cyber Defenses with Deception Technology: Top Tools and Techniques
PDF
Threat Deception - Counter Techniques from the Defenders League
PPTX
6 Ways to Deceive Cyber Attackers
PDF
Applying intelligent deception to detect sophisticated cyber attacks
PDF
Acalvio Deception Cyberdefense Manual
PDF
Webinar: Hunting maturity through cyber deception
PDF
(SACON) Sudarshan Pisupati & Sahir Hidayatullah - active deception for red a...
PDF
Why Deception Technology is Gaining Momentum in Banking, Healthcare, and Gove...
PPTX
Deception. An overview in Cybersecurity
PDF
Deception Technology: The Cybersecurity Paradigm We Didn’t Know We Needed
PDF
Honeypots, Deception, and Frankenstein
PDF
HackFormers Talk: Beware Wolves in Sheep's Clothing
PPTX
The Perils that PCI brings to Security
PDF
File-Based Deception Technology for Impeding Malicious Users
PDF
Honeypots, Deception, and Frankenstein
PDF
Deception Technology in Cybersecurity.pdf
PDF
A Definitive Market Guide to Deception Technology
PDF
Capture the Flag Exercise Using Active Deception Defense
PDF
Fidelis - Live Demonstration of Deception Solution
Strengthening Cyber Defenses with Deception Technology: Top Tools and Techniques
Threat Deception - Counter Techniques from the Defenders League
6 Ways to Deceive Cyber Attackers
Applying intelligent deception to detect sophisticated cyber attacks
Acalvio Deception Cyberdefense Manual
Webinar: Hunting maturity through cyber deception
(SACON) Sudarshan Pisupati & Sahir Hidayatullah - active deception for red a...
Why Deception Technology is Gaining Momentum in Banking, Healthcare, and Gove...
Deception. An overview in Cybersecurity
Deception Technology: The Cybersecurity Paradigm We Didn’t Know We Needed
Honeypots, Deception, and Frankenstein
HackFormers Talk: Beware Wolves in Sheep's Clothing
The Perils that PCI brings to Security
File-Based Deception Technology for Impeding Malicious Users
Honeypots, Deception, and Frankenstein
Deception Technology in Cybersecurity.pdf
A Definitive Market Guide to Deception Technology
Capture the Flag Exercise Using Active Deception Defense
Fidelis - Live Demonstration of Deception Solution
Ad

More from Priyanka Aash (20)

PPTX
AI Code Generation Risks (Ramkumar Dilli, CIO, Myridius)
PDF
From Chatbot to Destroyer of Endpoints - Can ChatGPT Automate EDR Bypasses (1...
PDF
Cracking the Code - Unveiling Synergies Between Open Source Security and AI.pdf
PDF
Oh, the Possibilities - Balancing Innovation and Risk with Generative AI.pdf
PDF
Lessons Learned from Developing Secure AI Workflows.pdf
PDF
Cyber Defense Matrix Workshop - RSA Conference
PDF
A Constitutional Quagmire - Ethical Minefields of AI, Cyber, and Privacy.pdf
PDF
Securing AI - There Is No Try, Only Do!.pdf
PDF
GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdf
PDF
Coordinated Disclosure for ML - What's Different and What's the Same.pdf
PDF
10 Key Challenges for AI within the EU Data Protection Framework.pdf
PDF
Techniques for Automatic Device Identification and Network Assignment.pdf
PDF
Keynote : Presentation on SASE Technology
PDF
Keynote : AI & Future Of Offensive Security
PDF
Redefining Cybersecurity with AI Capabilities
PDF
Demystifying Neural Networks And Building Cybersecurity Applications
PDF
Finetuning GenAI For Hacking and Defending
PDF
(CISOPlatform Summit & SACON 2024) Kids Cyber Security .pdf
PDF
(CISOPlatform Summit & SACON 2024) Regulation & Response In Banks.pdf
PDF
(CISOPlatform Summit & SACON 2024) Cyber Insurance & Risk Quantification.pdf
AI Code Generation Risks (Ramkumar Dilli, CIO, Myridius)
From Chatbot to Destroyer of Endpoints - Can ChatGPT Automate EDR Bypasses (1...
Cracking the Code - Unveiling Synergies Between Open Source Security and AI.pdf
Oh, the Possibilities - Balancing Innovation and Risk with Generative AI.pdf
Lessons Learned from Developing Secure AI Workflows.pdf
Cyber Defense Matrix Workshop - RSA Conference
A Constitutional Quagmire - Ethical Minefields of AI, Cyber, and Privacy.pdf
Securing AI - There Is No Try, Only Do!.pdf
GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdf
Coordinated Disclosure for ML - What's Different and What's the Same.pdf
10 Key Challenges for AI within the EU Data Protection Framework.pdf
Techniques for Automatic Device Identification and Network Assignment.pdf
Keynote : Presentation on SASE Technology
Keynote : AI & Future Of Offensive Security
Redefining Cybersecurity with AI Capabilities
Demystifying Neural Networks And Building Cybersecurity Applications
Finetuning GenAI For Hacking and Defending
(CISOPlatform Summit & SACON 2024) Kids Cyber Security .pdf
(CISOPlatform Summit & SACON 2024) Regulation & Response In Banks.pdf
(CISOPlatform Summit & SACON 2024) Cyber Insurance & Risk Quantification.pdf

Recently uploaded (20)

PDF
The Rise and Fall of 3GPP – Time for a Sabbatical?
PDF
CIFDAQ's Market Insight: SEC Turns Pro Crypto
PDF
Agricultural_Statistics_at_a_Glance_2022_0.pdf
PDF
Chapter 3 Spatial Domain Image Processing.pdf
PPTX
Cloud computing and distributed systems.
PDF
TokAI - TikTok AI Agent : The First AI Application That Analyzes 10,000+ Vira...
PDF
Machine learning based COVID-19 study performance prediction
PDF
Reach Out and Touch Someone: Haptics and Empathic Computing
PDF
How UI/UX Design Impacts User Retention in Mobile Apps.pdf
PDF
KodekX | Application Modernization Development
PDF
Encapsulation theory and applications.pdf
PPTX
PA Analog/Digital System: The Backbone of Modern Surveillance and Communication
PPTX
Detection-First SIEM: Rule Types, Dashboards, and Threat-Informed Strategy
PDF
Network Security Unit 5.pdf for BCA BBA.
PPTX
20250228 LYD VKU AI Blended-Learning.pptx
PPTX
Effective Security Operations Center (SOC) A Modern, Strategic, and Threat-In...
PPTX
Digital-Transformation-Roadmap-for-Companies.pptx
PDF
Bridging biosciences and deep learning for revolutionary discoveries: a compr...
PDF
Unlocking AI with Model Context Protocol (MCP)
PDF
Shreyas Phanse Resume: Experienced Backend Engineer | Java • Spring Boot • Ka...
The Rise and Fall of 3GPP – Time for a Sabbatical?
CIFDAQ's Market Insight: SEC Turns Pro Crypto
Agricultural_Statistics_at_a_Glance_2022_0.pdf
Chapter 3 Spatial Domain Image Processing.pdf
Cloud computing and distributed systems.
TokAI - TikTok AI Agent : The First AI Application That Analyzes 10,000+ Vira...
Machine learning based COVID-19 study performance prediction
Reach Out and Touch Someone: Haptics and Empathic Computing
How UI/UX Design Impacts User Retention in Mobile Apps.pdf
KodekX | Application Modernization Development
Encapsulation theory and applications.pdf
PA Analog/Digital System: The Backbone of Modern Surveillance and Communication
Detection-First SIEM: Rule Types, Dashboards, and Threat-Informed Strategy
Network Security Unit 5.pdf for BCA BBA.
20250228 LYD VKU AI Blended-Learning.pptx
Effective Security Operations Center (SOC) A Modern, Strategic, and Threat-In...
Digital-Transformation-Roadmap-for-Companies.pptx
Bridging biosciences and deep learning for revolutionary discoveries: a compr...
Unlocking AI with Model Context Protocol (MCP)
Shreyas Phanse Resume: Experienced Backend Engineer | Java • Spring Boot • Ka...

Deception Technology: Use Cases & Implementation Approaches

  • 1. DECEPTION TECHNOLOGIES Raj Gopalakrishna Co-founder & Chief Product Architect AcalvioTechnologiesCopyright AcalvioTechnologies 1
  • 2. Brief History of Deception DeceptionTypes Under the Hood Some Use Cases Touch Points
  • 3. Deception over the years • Millions of years in Natural World for survival/aggression • Millions of years in bacteria and virus to thrive • 1000s of years in Warfare/Military to attack or defend • Decades in Cyber Warfare • Attackers use Deception • Phishing, spoofing, encryption • Defender should use Deceptions • Honeypots, Cryptographic Camouflage • French Election used it recently Owl Butterfly for Survival 3Copyright AcalvioTechnologies Transmitter Passion Fruit Leaf with spots
  • 5. Breadcrumbs: Extend Deceptions to Production Devices Many flavors and forms: 1. Registry entries 2. Files & Folders 3. Memory hashes 4. User Profiles 5. Browser cookies Few Challenges: 1. Need deployment Automation and Intelligence 2. Avoid Accidental Alerts by Users 5 DeceptionAnywhere or Everywhere Copyright AcalvioTechnologies
  • 6. Lures: Another powerful arrow in the quiver Deliberately placed 1. Vulnerabilities in OS, Application, Protocols 2. Weak configurations and permissions 3. Powerful fake Service Accounts 4. Shares 5. Interesting Data 6 Make Deceptions more attractive Copyright AcalvioTechnologies
  • 7. DecoyTypes Low Interaction Deceptions • Attacker typically cannot login • Emulated Hosts, Applications, Database Servers High Interaction Deceptions • Attacker can login – full interaction • RealVM Hosts, Applications, Database Servers, Shares Copyright AcalvioTechnologies 7
  • 8. Low Interaction Deceptions  Deploy OS, Network services orApplications  Lots of deceptions possible.  Low IT cost  Low Risk to Enterprise Networks  Dynamic: easy to morph on-the-fly  Need not be emulations! × Cannot Engage with theAttacker × If Emulated then Easy to fingerprint Deceptions Key Challenge: Odds of attacker identifying deceptions Copyright AcalvioTechnologies 8
  • 9. High Interaction Deceptions Deploy real OS, Services, Applications Deceptions are not finger-printable. Possible to Engage with Attacker × Only Few deceptions × High Cost of licensing & maintaining × Need Containment to reduce RISK × Static: pre-build, unable to morph quickly × Often used with Breadcrumbs to lead attacker to Decoys. But then attacker needs to find breadcrumbs first Key Challenge: Odds of Attacker/Malware running into the few deceptions Copyright AcalvioTechnologies 9
  • 10. Often we need both Scale and Depth (Believable Deceptions) Copyright AcalvioTechnologies 10
  • 11. Static vs Dynamic Deceptions Static Deceptions • Hardly changes • Easy to fingerprint & avoid Dynamic Deceptions Mimic Octopus: Mimics upto 15 creatures ActiveCamouflage: Counter-illumination by Squids • Changing always • Hard to predict or identify HoneyAnts Copyright AcalvioTechnologies 11
  • 12. Intelligence Component Human only • Expert decides type and number of deceptions to deploy • Manually/Automatically configures traps atTime T0 Key Challenges: • What happens atTimeT1 orT10 ? • How many Experts can company send to front-line for 24x7x365? Human + AI based = Future System recommends type, number, placement, duration of deceptions. System Responds to • Events and Incidents • Adversary Behavior • When you are sleeping Copyright AcalvioTechnologies 12
  • 13. Some major Challenges in Cyber Security Compromise Detection Identifying malicious intent © AcalvioTechnologiesCompany 13 Alerts Deluge Too many False positives DeceptionTechnology can help in all of above
  • 14. Internal Facing vs External Facing Deceptions Internal Facing • Good for Enterprises • A new layer of Defense • Acts like a motion detector inside Enterprises • Corporate Network • Data Centers • Detects attackers who have gone past the perimeter defenses • Few, High FidelityAlerts raised • Can optionally Engage & Respond External Facing • Great for security researchers • Typically deployed on the Internet or in the DMZ. • Lots of alerts per hour/day as there are lots of malicious Attackers and Bots on the Internet • Often used to show demo of DeceptionTechnologies Copyright AcalvioTechnologies 14
  • 15. Detecting Ransomware: current approaches AV and Sandbox approach • Look for known Signatures • Look for known C&C  Low False +ve × High False -ve Data Science approach • Look for Anomalous Behavior • High File I/O • Lots of different Files accessed • Lots of crypto × Anomaly ≠Threat × High False +ve 15Copyright AcalvioTechnologies
  • 16. Detecting Ransomware using Deceptions • Leverages Decoys, Breadcrumbs and Lures • Set specific traps in specific locations • Monitor only activity against decoys, breadcrumbs & lures Auto Detects and confirms Ransomware Very Efficient and Accurate 16 Always High Fidelity Signals Zero false +ve Copyright AcalvioTechnologies
  • 17. Protecting Secrets in software is hard Examples Crypto keys Passwords Payment card numbers Copyright AcalvioTechnologies 17
  • 18. THANKYOU Copyright AcalvioTechnologies 18 • Raj Gopalakrishna • raj@Acalvio.com • AcalvioTechnologies Inc
  • 19. CONTACT ACALVIO IFYOU ARE LOOKING FOR DECEPTION PRODUCT Copyright AcalvioTechnologies 19