4 Third-party cloud computing On-demand computing outsouring, share third-party infrastructure Amanzon s EC2 Microsoft s Azure Rackspace s Mosso
5 risk from cloud provider new trust realtionship customers must trust their cloud provider to respect the privacy of their data and the integrity of their computations.
6 threat from VM placement Threat multiplex the virtual machines of different customer upon the same physical hardware.
7 threat from VM placement Threat multiplex the virtual machines of different customer upon the same physical hardware. The adversary may be able to place the malicious instance on the same physical machine where the victim instance reside through legal process. Then lunch certain cross-vm attacks
8 steps Two steps Placement: adversary arranging to place their malicious VM on the same physical machine as that of a target customer
9 steps Two steps Placement: adversary arranging to place their malicious VM on the same physical machine as that of a target customer extraction: extract confidential information via a cross-vm attack.
10 Terminology terminology Instance: a running os image co-resident: instances that are running on the same physical machine
11 Research questions Qeuestions where an instance is located? how to determine if two instances are co-resident how to launch instance that will be co-resident with other user s instance how to exploit cross-vm information leakage once co-resident.
13 Amazon EC2 service Virtual machine monitor:xen run linux, freebsd, opensolaris and Windows as guest A privileged virtual machine call Dom0 is configured to route packets for guest images and report itself as a hop in trceroutes Credit:[Figure from Thomas s pape, CCS 2009]
14 more about Amazon EC2 service configuration parameter contain 3 availability zones" with separate power and network connectivity 5 instance type:m1.small, c1.medium, m1.large, m1.xlarge, c1.xlarge.
15 more about Amazon EC2 service Instance information external ipv4 address and domain name, internal address and domain name. within the cloud, both domain names resolved to internal IP address.
16 Goal Goal understand where potential target are located in the cloud and the instance creation parameters need to attempt establishing co-residence of an adversarial instance.
17 Cloud Cartography Hypothesis different availability zones likely to correspond to different internal IP address ranges and may be true for instance types as well. Thus mapping the use of the EC2 internal address allows an adversary to determine which IP address correspond to which creation parameters.
18 Empirical measurement study Two Data sets launching a number of EC2 instance of varying types and surveying the resulting IP address assigned. enumerating public EC2-based web servers and translate public IP to internal IP
19 Instance placement parameters Experiment 1 iteratively launching 20 instances for each of the 15 availability zone/instance type pairs.
20 Instance placement parameters Experiment 1 iteratively launching 20 instances for each of the 15 availability zone/instance type pairs. Credit:[Figure from Thomas s pape, CCS 2009]
21 Instance placement parameters Experiment 1 iteratively launching 20 instances for each of the 15 availability zone/instance type pairs. Credit:[Figure from Thomas s pape, CCS 2009] different availability zones use different IP regions.
22 Instance placement parameters Experiment 2 launching 100 instances(20 of each type) in zone 3 for two account A and B.
23 Instance placement parameters Experiment 2 launching 100 instances(20 of each type) in zone 3 for two account A and B. Credit:[Figure from Thomas s pape, CCS 2009]
24 Instance placement parameters Experiment 2 launching 100 instances(20 of each type) in zone 3 for two account A and B. Credit:[Figure from Thomas s pape, CCS 2009] The same instance type within the same zone use similar IP regions even for different accounts
25 Enumerate public EC2-based web servers Experiment 1 utilize WHOIS queries, identify 4 distinct IP address prefix associate with EC2 2 perform a tcp connect probe on port 80, result in responsive IPs. 3 perform a tcp port 443 scan, result in 8375 responsive IPs. 4 translate the public address into an internal EC2 address, result in potential target.
26 map of EC2-based web server Goal map the potential target into a instance type and availability zone. The data we have? 4499 sample instances, which has 997 unique internal IPs and 611 unique Dom0 IPs associated with these instances.
27 map of EC2-based web server mapping algorithms All IP from a /16 are from the same availability zone. A /24 inherit any included sampled instance type. A /24 containing a Dom0 IP address, we associate this /24 address the type of the Dom0 s associated instance All /24 s between consecutive Dom0 /24 s inherit the former s associated type.
29 Dectecting co-residence co-residence checking algorithms Internal IP address are close.(within 7)
30 Dectecting co-residence co-residence checking algorithms Internal IP address are close.(within 7) obtain and compare xen Dom0 address. sending TCP SYN and set TTL small: VM->Dom0->VM
31 Dectecting co-residence co-residence checking algorithms Internal IP address are close.(within 7) obtain and compare xen Dom0 address. sending TCP SYN and set TTL small: VM->Dom0->VM network latency: small packet round-trip times.
32 Observations On EC2, VMs can be co-resident only if they have identical creation parameters(region, availability zone, instance type) cloud-internal IP address assigned to VMs are strongly correlated with their creation parameters. use EC2 s DNS service to map public IP address to private internal address.
33 Achieving co-residence Overall strategy Derive target s creation parameters Create similar VMs unitl co-residence is detected.
34 Achieving co-residence Improvement exploiting EC2 s sequential assignment strategy trigger new creation of new VMs by the victim by inducing load(eg. RightScale)
35 Experiment probing 1 A single victim instance was launched 2 20 probe instances were launched by another account 3 co-residence checks were performed.
36 Result Credit:[Figure from Thomas s pape, CCS 2009]
38 What is side-channel attack side-channel attack cross-vm information leakage due to sharing of physical resources.like cpu s data caches, memory bus, network access.
39 cache attacks example cpu contains small, fast memory cache shared by all applications.
40 cache attacks example cpu contains small, fast memory cache shared by all applications. if only attacker access memory, it is served from the fast cache.
41 cache attacks example cpu contains small, fast memory cache shared by all applications. if only attacker access memory, it is served from the fast cache. if victim also access memory, the cache fills up and attacker notices a slow-down.
42 cache attacks example cpu contains small, fast memory cache shared by all applications. if only attacker access memory, it is served from the fast cache. if victim also access memory, the cache fills up and attacker notices a slow-down. from this, attacker can deduce the memory access pattens of victim
43 Measuring cache usage Prime+Trigger+Probe techinque allocate buffer B, enough to fill significant portion of caches Prime: Read B at cache line size to ensure it is cache Trigger: busy loops until the cpu s cycle increase by a large value. Probe: Measure the time it take to read B.
44 Estimating traffic rates performance cache load measurement, and sent http requests at different rates. Credit:[Figure from Thomas s pape, CCS 2009]
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