SlideShare a Scribd company logo
A Novel Approach to Prevent Cache-Based Side-Channel
Attack in the Cloud
Writtenby MuhammedSadiqueUK*, DivyaJamesin 2016
AGENDA
1. Cloud and side channels
2. Side channel attacks
3. Existing sol vs. proposed sol.
4. Decision algorithm
5. Conclusion
BACKGROUND
❏ THE CLOUD MODEL
❏ SIDE CHANNEL
❏ SIDE CHANNEL ATTACK
❏ CACHE BASED SIDE CHANNEL ATTACk
The Cloud Model
❏ Resources for more than one client
❏ Hidden details of infrastructure
❏ Always on
❏ Pay per use
❏ Servers accessed remotely
❏ Example : Amazon web services, Google cloud, Microsoft Azure
Side-Channel
❏ A mode of bypassing virtual machine for gaining information from the physical
implementation rather than brute force or theoretical weaknesses in the algorithm
Side-Channel Attack
❏ A side channel attack is any attack based on information gained from the
implementation of a computer system, rather than a weakness in the
implemented algorithm itself.
❏ The things which can be exploited in side channel attack can be timing information,
power consumption, electromagnetic leaks or even sound as all of these can
provide an extra source of information.
How secure is your cache against side-
channel attacks?
❏ caches are essential for the performance of modern computers
❏ Security-critical data can leak through very unexpected side channels, making side-
channel attacks very dangerous threats
Cache-Based Side-Channel Attack
❏ Cache side channel attacks are basically attacks based on attackers ability to
monitor cache accesses made by the victim in a shared physical system asi in
virtualized environment or a type of cloud service
❏ AIM: Extract Information
❏ Source : leakage
❏ Procedure : convert leakage into information
❏ Types: sequential and parallel
Purpose of the paper
❏ cache-based side-channels in a cloud environment
❏ sequential type of side channel attack.
❏ There are several server-side defences inpace to handle cache-based side
channels.
❏ Ex. cache flushing - Make cache useless
❏ prevent the side-channel’s occurrence - an algorithm designed to implement the
technique.
❏ Minimalistic fashion to help minimize resulting overhead
Existing solution vs. suggested solution
Currentscenario
❏ Focusses on flushing the cache
❏ Reduces usefulness of the cache
❏ Increased cost due to flushing the cache
Solution
❏ Focuses on disabling the difference in access time
❏ Includes two new functions in hypervisor: wait function, Algorithm
❏ Prevents time information parameter leakage in the cache of the cloud
❏ Usefulness of cache, decrease the cost and prevent the data loss
Cache-Wait
❏ If the time taken for the cache miss is greater than the cache hit, Cache-Wait operates.
❏ Cache-Wait will hold the cache execution process for the specific time.
❏ The specific time is determined from the difference in the accessing time required for
fetching data from the main memory and the cache memory. That is, the difference in
accessing time required between cache miss and cache hit.
❏ In general, a wait would only be necessary before the Probe step
Decision Algorithm
Function contextSwitch(DomX,DomY)
{
// from DomX to DomY
If Main_T > Cache_T
waitCache();
return;
}
EndFunction
Statistical Analysis
This analysis suggests that algorithm is efficient
Graphical presentation
Conclusion
❏ cloud’s architecture is particularly susceptible to cache-based side-channel attacks.
❏ interfering with the cloud model is necessary (
❏ sequential side-channels are taken care by their solution
❏ Focus cache-based side-channels in the Cloud and does not interfere with the
Cloud model
❏ The time information parameter leakage
❏ Efficient algorithm proposed
Our Opinion
❏ Great job in reducing cost when
❏ Future plan is to implement this approach in real- time environment and in the
Docker
❏ Amount of flush function execution is much more when there are five or more
virtual machines.
❏ Parallel cache-based side channel attacks or hardware based side channel attacks
are still large area to focus in security terms.

More Related Content

What's hot

Apache Solr as a compressed, scalable, and high performance time series database
Apache Solr as a compressed, scalable, and high performance time series databaseApache Solr as a compressed, scalable, and high performance time series database
Apache Solr as a compressed, scalable, and high performance time series database
Florian Lautenschlager
 
Encrypted DNS research @ nic.at
Encrypted DNS research @ nic.atEncrypted DNS research @ nic.at
Encrypted DNS research @ nic.at
Alex Mayrhofer
 
Tradeoffs in Automatic Provenance Capture
Tradeoffs in Automatic Provenance CaptureTradeoffs in Automatic Provenance Capture
Tradeoffs in Automatic Provenance Capture
Paul Groth
 
DSD-INT 2017 The use of big data for dredging - De Boer
DSD-INT 2017 The use of big data for dredging - De BoerDSD-INT 2017 The use of big data for dredging - De Boer
DSD-INT 2017 The use of big data for dredging - De Boer
Deltares
 
The new time series kid on the block
The new time series kid on the blockThe new time series kid on the block
The new time series kid on the block
Florian Lautenschlager
 
Data Management in Cloud Platforms
Data Management in Cloud PlatformsData Management in Cloud Platforms
Data Management in Cloud Platforms
shnkoc
 
ResCUE rational behind FPGA
ResCUE rational behind FPGAResCUE rational behind FPGA
ResCUE rational behind FPGA
ResCUE
 
Load Balancing In Cloud Computing newppt
Load Balancing In Cloud Computing newpptLoad Balancing In Cloud Computing newppt
Load Balancing In Cloud Computing newppt
Utshab Saha
 
Replication and rebuild in cStor
Replication and rebuild in cStorReplication and rebuild in cStor
Replication and rebuild in cStor
OpenEBS
 
Cassandra To Infinity And Beyond
Cassandra To Infinity And BeyondCassandra To Infinity And Beyond
Cassandra To Infinity And Beyond
Romain Hardouin
 
Introducing gluster filesystem by aditya
Introducing gluster filesystem by adityaIntroducing gluster filesystem by aditya
Introducing gluster filesystem by aditya
Aditya Chhikara
 
OSDC 2013 | Neues in DRBD9 by Philipp Reisner
OSDC 2013 | Neues in DRBD9 by Philipp ReisnerOSDC 2013 | Neues in DRBD9 by Philipp Reisner
OSDC 2013 | Neues in DRBD9 by Philipp Reisner
NETWAYS
 
Scaling Islandora
Scaling IslandoraScaling Islandora
Scaling Islandora
Erin Tripp
 
Provenance for Data Munging Environments
Provenance for Data Munging EnvironmentsProvenance for Data Munging Environments
Provenance for Data Munging Environments
Paul Groth
 
A Fast and Efficient Time Series Storage Based on Apache Solr
A Fast and Efficient Time Series Storage Based on Apache SolrA Fast and Efficient Time Series Storage Based on Apache Solr
A Fast and Efficient Time Series Storage Based on Apache Solr
QAware GmbH
 

What's hot (15)

Apache Solr as a compressed, scalable, and high performance time series database
Apache Solr as a compressed, scalable, and high performance time series databaseApache Solr as a compressed, scalable, and high performance time series database
Apache Solr as a compressed, scalable, and high performance time series database
 
Encrypted DNS research @ nic.at
Encrypted DNS research @ nic.atEncrypted DNS research @ nic.at
Encrypted DNS research @ nic.at
 
Tradeoffs in Automatic Provenance Capture
Tradeoffs in Automatic Provenance CaptureTradeoffs in Automatic Provenance Capture
Tradeoffs in Automatic Provenance Capture
 
DSD-INT 2017 The use of big data for dredging - De Boer
DSD-INT 2017 The use of big data for dredging - De BoerDSD-INT 2017 The use of big data for dredging - De Boer
DSD-INT 2017 The use of big data for dredging - De Boer
 
The new time series kid on the block
The new time series kid on the blockThe new time series kid on the block
The new time series kid on the block
 
Data Management in Cloud Platforms
Data Management in Cloud PlatformsData Management in Cloud Platforms
Data Management in Cloud Platforms
 
ResCUE rational behind FPGA
ResCUE rational behind FPGAResCUE rational behind FPGA
ResCUE rational behind FPGA
 
Load Balancing In Cloud Computing newppt
Load Balancing In Cloud Computing newpptLoad Balancing In Cloud Computing newppt
Load Balancing In Cloud Computing newppt
 
Replication and rebuild in cStor
Replication and rebuild in cStorReplication and rebuild in cStor
Replication and rebuild in cStor
 
Cassandra To Infinity And Beyond
Cassandra To Infinity And BeyondCassandra To Infinity And Beyond
Cassandra To Infinity And Beyond
 
Introducing gluster filesystem by aditya
Introducing gluster filesystem by adityaIntroducing gluster filesystem by aditya
Introducing gluster filesystem by aditya
 
OSDC 2013 | Neues in DRBD9 by Philipp Reisner
OSDC 2013 | Neues in DRBD9 by Philipp ReisnerOSDC 2013 | Neues in DRBD9 by Philipp Reisner
OSDC 2013 | Neues in DRBD9 by Philipp Reisner
 
Scaling Islandora
Scaling IslandoraScaling Islandora
Scaling Islandora
 
Provenance for Data Munging Environments
Provenance for Data Munging EnvironmentsProvenance for Data Munging Environments
Provenance for Data Munging Environments
 
A Fast and Efficient Time Series Storage Based on Apache Solr
A Fast and Efficient Time Series Storage Based on Apache SolrA Fast and Efficient Time Series Storage Based on Apache Solr
A Fast and Efficient Time Series Storage Based on Apache Solr
 

Similar to A novel approach to prevent cache based side-channel attack in the cloud (1)

Autopilot : Securing Cloud Native Storage
Autopilot : Securing Cloud Native StorageAutopilot : Securing Cloud Native Storage
Autopilot : Securing Cloud Native Storage
SF Bay Cloud Native Open Infra Meetup
 
CHARM(A Cost-Efficient Multi-Cloud Data Hosting Scheme with High Availability)
CHARM(A Cost-Efficient Multi-Cloud Data Hosting Scheme with High Availability)CHARM(A Cost-Efficient Multi-Cloud Data Hosting Scheme with High Availability)
CHARM(A Cost-Efficient Multi-Cloud Data Hosting Scheme with High Availability)
Deeksha Arya
 
IOT meetup presentation
IOT meetup presentationIOT meetup presentation
IOT meetup presentation
Cliff Gilmore
 
Big Data Day LA 2015 - Always-on Ingestion for Data at Scale by Arvind Prabha...
Big Data Day LA 2015 - Always-on Ingestion for Data at Scale by Arvind Prabha...Big Data Day LA 2015 - Always-on Ingestion for Data at Scale by Arvind Prabha...
Big Data Day LA 2015 - Always-on Ingestion for Data at Scale by Arvind Prabha...
Data Con LA
 
Deduplication in Open Spurce Cloud
Deduplication in Open Spurce CloudDeduplication in Open Spurce Cloud
Deduplication in Open Spurce Cloud
Mangali Praveen Kumar
 
Data Back-Up and Recovery Techniques for Cloud Server Using Seed Block Algorithm
Data Back-Up and Recovery Techniques for Cloud Server Using Seed Block AlgorithmData Back-Up and Recovery Techniques for Cloud Server Using Seed Block Algorithm
Data Back-Up and Recovery Techniques for Cloud Server Using Seed Block Algorithm
IJERA Editor
 
week_2Lec02_CS422.pptx
week_2Lec02_CS422.pptxweek_2Lec02_CS422.pptx
week_2Lec02_CS422.pptx
mivomi1
 
Understanding application requirements
Understanding application requirementsUnderstanding application requirements
Understanding application requirements
Cloud Genius
 
Towards the extinction of mega data centres? To which extent should the Clou...
 Towards the extinction of mega data centres? To which extent should the Clou... Towards the extinction of mega data centres? To which extent should the Clou...
Towards the extinction of mega data centres? To which extent should the Clou...
Thierry Coupaye
 
Notes
NotesNotes
Notes
Aball233
 
Cloud Native Microservices - Building Blocks for Digital Innovation
Cloud Native Microservices - Building Blocks for Digital InnovationCloud Native Microservices - Building Blocks for Digital Innovation
Cloud Native Microservices - Building Blocks for Digital Innovation
Diego Pacheco
 
Harnessing the cloud for securely outsourcing large scale systems of linear e...
Harnessing the cloud for securely outsourcing large scale systems of linear e...Harnessing the cloud for securely outsourcing large scale systems of linear e...
Harnessing the cloud for securely outsourcing large scale systems of linear e...
JPINFOTECH JAYAPRAKASH
 
Cassandra in Operation
Cassandra in OperationCassandra in Operation
Cassandra in Operation
niallmilton
 
How Your DRAM Becomes a Security Problem
How Your DRAM Becomes a Security ProblemHow Your DRAM Becomes a Security Problem
How Your DRAM Becomes a Security Problem
mark-smith
 
Openstack on Fedora, Fedora on Openstack: An Introduction to cloud IaaS
Openstack on Fedora, Fedora on Openstack: An Introduction to cloud IaaSOpenstack on Fedora, Fedora on Openstack: An Introduction to cloud IaaS
Openstack on Fedora, Fedora on Openstack: An Introduction to cloud IaaS
Sadique Puthen
 
NetBackup CloudCatalyst – efficient, cost-effective deduplication to the cloud
NetBackup CloudCatalyst – efficient, cost-effective deduplication to the cloudNetBackup CloudCatalyst – efficient, cost-effective deduplication to the cloud
NetBackup CloudCatalyst – efficient, cost-effective deduplication to the cloud
Veritas Technologies LLC
 
Build cloud native solution using open source
Build cloud native solution using open source Build cloud native solution using open source
Build cloud native solution using open source
Nitesh Jadhav
 
AppFabric Velocity
AppFabric VelocityAppFabric Velocity
AppFabric Velocity
Dennis van der Stelt
 
OSCON 15 Building Opensource wtih Open Source
OSCON 15 Building Opensource wtih Open SourceOSCON 15 Building Opensource wtih Open Source
OSCON 15 Building Opensource wtih Open Source
Susan Wu
 
Planning for Disaster Recovery (DR) with Galera Cluster
Planning for Disaster Recovery (DR) with Galera ClusterPlanning for Disaster Recovery (DR) with Galera Cluster
Planning for Disaster Recovery (DR) with Galera Cluster
Codership Oy - Creators of Galera Cluster
 

Similar to A novel approach to prevent cache based side-channel attack in the cloud (1) (20)

Autopilot : Securing Cloud Native Storage
Autopilot : Securing Cloud Native StorageAutopilot : Securing Cloud Native Storage
Autopilot : Securing Cloud Native Storage
 
CHARM(A Cost-Efficient Multi-Cloud Data Hosting Scheme with High Availability)
CHARM(A Cost-Efficient Multi-Cloud Data Hosting Scheme with High Availability)CHARM(A Cost-Efficient Multi-Cloud Data Hosting Scheme with High Availability)
CHARM(A Cost-Efficient Multi-Cloud Data Hosting Scheme with High Availability)
 
IOT meetup presentation
IOT meetup presentationIOT meetup presentation
IOT meetup presentation
 
Big Data Day LA 2015 - Always-on Ingestion for Data at Scale by Arvind Prabha...
Big Data Day LA 2015 - Always-on Ingestion for Data at Scale by Arvind Prabha...Big Data Day LA 2015 - Always-on Ingestion for Data at Scale by Arvind Prabha...
Big Data Day LA 2015 - Always-on Ingestion for Data at Scale by Arvind Prabha...
 
Deduplication in Open Spurce Cloud
Deduplication in Open Spurce CloudDeduplication in Open Spurce Cloud
Deduplication in Open Spurce Cloud
 
Data Back-Up and Recovery Techniques for Cloud Server Using Seed Block Algorithm
Data Back-Up and Recovery Techniques for Cloud Server Using Seed Block AlgorithmData Back-Up and Recovery Techniques for Cloud Server Using Seed Block Algorithm
Data Back-Up and Recovery Techniques for Cloud Server Using Seed Block Algorithm
 
week_2Lec02_CS422.pptx
week_2Lec02_CS422.pptxweek_2Lec02_CS422.pptx
week_2Lec02_CS422.pptx
 
Understanding application requirements
Understanding application requirementsUnderstanding application requirements
Understanding application requirements
 
Towards the extinction of mega data centres? To which extent should the Clou...
 Towards the extinction of mega data centres? To which extent should the Clou... Towards the extinction of mega data centres? To which extent should the Clou...
Towards the extinction of mega data centres? To which extent should the Clou...
 
Notes
NotesNotes
Notes
 
Cloud Native Microservices - Building Blocks for Digital Innovation
Cloud Native Microservices - Building Blocks for Digital InnovationCloud Native Microservices - Building Blocks for Digital Innovation
Cloud Native Microservices - Building Blocks for Digital Innovation
 
Harnessing the cloud for securely outsourcing large scale systems of linear e...
Harnessing the cloud for securely outsourcing large scale systems of linear e...Harnessing the cloud for securely outsourcing large scale systems of linear e...
Harnessing the cloud for securely outsourcing large scale systems of linear e...
 
Cassandra in Operation
Cassandra in OperationCassandra in Operation
Cassandra in Operation
 
How Your DRAM Becomes a Security Problem
How Your DRAM Becomes a Security ProblemHow Your DRAM Becomes a Security Problem
How Your DRAM Becomes a Security Problem
 
Openstack on Fedora, Fedora on Openstack: An Introduction to cloud IaaS
Openstack on Fedora, Fedora on Openstack: An Introduction to cloud IaaSOpenstack on Fedora, Fedora on Openstack: An Introduction to cloud IaaS
Openstack on Fedora, Fedora on Openstack: An Introduction to cloud IaaS
 
NetBackup CloudCatalyst – efficient, cost-effective deduplication to the cloud
NetBackup CloudCatalyst – efficient, cost-effective deduplication to the cloudNetBackup CloudCatalyst – efficient, cost-effective deduplication to the cloud
NetBackup CloudCatalyst – efficient, cost-effective deduplication to the cloud
 
Build cloud native solution using open source
Build cloud native solution using open source Build cloud native solution using open source
Build cloud native solution using open source
 
AppFabric Velocity
AppFabric VelocityAppFabric Velocity
AppFabric Velocity
 
OSCON 15 Building Opensource wtih Open Source
OSCON 15 Building Opensource wtih Open SourceOSCON 15 Building Opensource wtih Open Source
OSCON 15 Building Opensource wtih Open Source
 
Planning for Disaster Recovery (DR) with Galera Cluster
Planning for Disaster Recovery (DR) with Galera ClusterPlanning for Disaster Recovery (DR) with Galera Cluster
Planning for Disaster Recovery (DR) with Galera Cluster
 

Recently uploaded

How to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For FlutterHow to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For Flutter
Daiki Mogmet Ito
 
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfUnlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Malak Abu Hammad
 
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracyGraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Tomaz Bratanic
 
20240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 202420240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 2024
Matthew Sinclair
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Neo4j
 
“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”
Claudio Di Ciccio
 
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FMEEssentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
Safe Software
 
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
名前 です男
 
Mariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceXMariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceX
Mariano Tinti
 
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
SOFTTECHHUB
 
GenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizationsGenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizations
kumardaparthi1024
 
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERPBest 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Pixlogix Infotech
 
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
DianaGray10
 
Video Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the FutureVideo Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the Future
Alpen-Adria-Universität
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
innovationoecd
 
Infrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI modelsInfrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI models
Zilliz
 
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Speck&Tech
 
Removing Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software FuzzingRemoving Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software Fuzzing
Aftab Hussain
 
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptxHow to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
danishmna97
 
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfObservability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Paige Cruz
 

Recently uploaded (20)

How to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For FlutterHow to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For Flutter
 
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfUnlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
 
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracyGraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
 
20240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 202420240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 2024
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
 
“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”
 
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FMEEssentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
 
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
 
Mariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceXMariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceX
 
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
 
GenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizationsGenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizations
 
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERPBest 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
 
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
 
Video Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the FutureVideo Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the Future
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
 
Infrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI modelsInfrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI models
 
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
 
Removing Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software FuzzingRemoving Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software Fuzzing
 
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptxHow to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
 
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfObservability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
 

A novel approach to prevent cache based side-channel attack in the cloud (1)

  • 1. A Novel Approach to Prevent Cache-Based Side-Channel Attack in the Cloud Writtenby MuhammedSadiqueUK*, DivyaJamesin 2016
  • 2. AGENDA 1. Cloud and side channels 2. Side channel attacks 3. Existing sol vs. proposed sol. 4. Decision algorithm 5. Conclusion
  • 3. BACKGROUND ❏ THE CLOUD MODEL ❏ SIDE CHANNEL ❏ SIDE CHANNEL ATTACK ❏ CACHE BASED SIDE CHANNEL ATTACk
  • 4. The Cloud Model ❏ Resources for more than one client ❏ Hidden details of infrastructure ❏ Always on ❏ Pay per use ❏ Servers accessed remotely ❏ Example : Amazon web services, Google cloud, Microsoft Azure
  • 5. Side-Channel ❏ A mode of bypassing virtual machine for gaining information from the physical implementation rather than brute force or theoretical weaknesses in the algorithm
  • 6. Side-Channel Attack ❏ A side channel attack is any attack based on information gained from the implementation of a computer system, rather than a weakness in the implemented algorithm itself. ❏ The things which can be exploited in side channel attack can be timing information, power consumption, electromagnetic leaks or even sound as all of these can provide an extra source of information.
  • 7. How secure is your cache against side- channel attacks? ❏ caches are essential for the performance of modern computers ❏ Security-critical data can leak through very unexpected side channels, making side- channel attacks very dangerous threats
  • 8. Cache-Based Side-Channel Attack ❏ Cache side channel attacks are basically attacks based on attackers ability to monitor cache accesses made by the victim in a shared physical system asi in virtualized environment or a type of cloud service ❏ AIM: Extract Information ❏ Source : leakage ❏ Procedure : convert leakage into information ❏ Types: sequential and parallel
  • 9. Purpose of the paper ❏ cache-based side-channels in a cloud environment ❏ sequential type of side channel attack. ❏ There are several server-side defences inpace to handle cache-based side channels. ❏ Ex. cache flushing - Make cache useless ❏ prevent the side-channel’s occurrence - an algorithm designed to implement the technique. ❏ Minimalistic fashion to help minimize resulting overhead
  • 10. Existing solution vs. suggested solution
  • 11. Currentscenario ❏ Focusses on flushing the cache ❏ Reduces usefulness of the cache ❏ Increased cost due to flushing the cache Solution ❏ Focuses on disabling the difference in access time ❏ Includes two new functions in hypervisor: wait function, Algorithm ❏ Prevents time information parameter leakage in the cache of the cloud ❏ Usefulness of cache, decrease the cost and prevent the data loss
  • 12. Cache-Wait ❏ If the time taken for the cache miss is greater than the cache hit, Cache-Wait operates. ❏ Cache-Wait will hold the cache execution process for the specific time. ❏ The specific time is determined from the difference in the accessing time required for fetching data from the main memory and the cache memory. That is, the difference in accessing time required between cache miss and cache hit. ❏ In general, a wait would only be necessary before the Probe step
  • 13. Decision Algorithm Function contextSwitch(DomX,DomY) { // from DomX to DomY If Main_T > Cache_T waitCache(); return; } EndFunction
  • 14. Statistical Analysis This analysis suggests that algorithm is efficient
  • 16. Conclusion ❏ cloud’s architecture is particularly susceptible to cache-based side-channel attacks. ❏ interfering with the cloud model is necessary ( ❏ sequential side-channels are taken care by their solution ❏ Focus cache-based side-channels in the Cloud and does not interfere with the Cloud model ❏ The time information parameter leakage ❏ Efficient algorithm proposed
  • 17. Our Opinion ❏ Great job in reducing cost when ❏ Future plan is to implement this approach in real- time environment and in the Docker ❏ Amount of flush function execution is much more when there are five or more virtual machines. ❏ Parallel cache-based side channel attacks or hardware based side channel attacks are still large area to focus in security terms.

Editor's Notes

  1. AIM: preventing the time information parameter leakage in the cache in the cloud without affecting the cache functionality