a method for evaluating placement security of new iaas cloud … oral... · 2019. 4. 25. · new...

A Method for Evaluating Placement Security of New IaaS Cloud Providers

SCSE01 Pan Yue

Mentor: Dr. Ta Nguyen Binh Duong

Overview

Introduction to Problem

Background research

Experimental methodology

Results and analysis

Future development

Introduction to Problem

IaaS clouds- a popular model of cloud computing

• Configurable computing resources shared over the internet

• Hosts Virtual Machines (VM) on shared physical infrastructure(Multi-tenancy)

Co-location Attacks- a security risk in IaaS Clouds

• Launched on victim VMs on the same physical host as attacker

• Extract confidential data or degrade performance of victim

data

Aims of research

I. Examine an evaluation technique of IaaS cloud placement security (memory-bus locking)

II. Explore how the findings can be applied to evaluate commercial IaaS cloud providers

Background Research

Co-location Attack Mechanism

AttackerRequest VM

Attacker VM Victim VM

Co-location

Detection

Co-location Detection

• Covert side channel detection

create contention in shared hardware resources of host

cause observable performance degradation in victim

Attacker Victim

Shared hardware

resource

Intensive

request

Normal

requestRequest

delayed

Co-location Detection

• Memory bus locking

create contention in memory bus of host

observe degraded performance in accessing main memory

Attacker Victim

main memory bus

Continuous

access to memory

Access

delayed

Evaluating Placement Security

Susceptibility to co-location attacks

Susceptibility to co-location detection

indicates

MAY test for

memory bus locking

Hypothesis

Memory bus locking can achieve accurate co-location

detection in both cooperative and uncooperative cases,

and hence prove useful for evaluating placement security

of IaaS cloud providers.

Experimental Methodology

Cooperative memory-bus locking

• Lock and Probe model

• two VMs set up on same local host

• one locks memory-bus (attacker), one performs and measures

affected task(victim)

Cooperative experiment set-up

shared

hardware

memory

bus

Locking: Implementation

reference: github.com/jacnel/co-res

Probing: Implementation

reference: Varadarajan et al., 2017

Uncooperative memory-bus locking

• Lock and Probe model, revised

• Attacker and victim VMs set up retained

• Does not assume control over victim (cannot measure own

performance)

• A third VM (evaluator) on unknown host to measure victim’s

performance

Uncooperative experiment set-up

• Victim: web server• Virtual host with public domain

OR local host domain• Apache 2

• Evaluator• Accesses victim’s domain• Measures server performance• Apache Jmeter

Experiment summary

Attacker locks memory bus by executing Locking code

Victim performs task and

measures own performance

Victim performs task and Evaluator

measures performance

Observe performance degradation in

victim to detect co-location

cooperative uncooperative

Results and Analysis

Cooperative experiment results

Data collected as the number of CPU clock cycles required to execute one run of the probe program, taken for 100 runs

Cooperative experiment results

The average runtime with locking instance sees a 70% increase compared to without locking.

Performance degradation is apparent Co-location successfully detected

Conclusion for cooperative detection

• Memory-bus locking can accurately detect co-location in the cooperative case

• Hence, it can evaluate the placement security of IaaS clouds if a dedicated server can be purchased to ensure the co-location of lock and probe VMs

Overall Conclusion

Memory bus locking is an effective co-location detection technique in the cooperative case, which can be used for evaluating placement security in cloud providers under controlled conditions.

Future Developments

Future developments

• Complete experiments for the uncooperative case

• Apply memory-bus locking detection technique to commercial cloud providers

Thank You

Main ReferencesVaradarajan, V. (2015). A Placement Vulnerability Study in Multi-Tenant Public Clouds.

USENIX.

Delimitrou, C., & Kozyrakis, C. (2017). Bolt: I Know What You Did Last Summer...In the

Cloud. ACM SIGOPS Operating Systems Review,51(2), 599-613.

doi:10.1145/3093315.3037703

Han, Y., Chan, J., Alpcan, T., & Leckie, C. (2015). Using Virtual Machine Allocation Policies

to Defend against Co-resident Attacks in Cloud Computing. IEEE Transactions on Dependable

and Secure Computing,1-1. doi:10.1109/tdsc.2015.2429132

Nelson, J. (2017). Co-residency Detection and Memory Bus Locking.

Ristenpart, T. (n.d.). Hey, You, Get Off of My Cloud: Exploring Information ... Retrieved from

https://hovav.net/ucsd/dist/cloudsec.pdf

Alibaba Cloud Ranks the World's Third Largest Cloud Services Provider for Two Consecutive

Years. (n.d.). Retrieved from https://www.alibabacloud.com/press-room/alibaba-cloud-ranks-

the-worlds-third-largest-cloud-services-provider-for-two-consecutive-

time?spm=a2c5t.10695662.1996646101.searchclickresult.4a645316ZjqxGh