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Cyber Defenses for Physical Attacks and Insider Threats in Cloud Computing

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In cloud computing, most of the computations and data in the data center do not belong to the cloud provider. This leaves owners of applications and data concerned about cyber and physical attacks which may compromise the confidentiality, integrity or availability of their applications or data. While much work has looked at protection from software (cyber) threats, very few have looked at physical attacks and physical security in data centers. In this work, we present a novel set of cyber defense strategies for physical attacks in data centers. We capitalize on the fact that physical attackers are constrained by the physical layout and other features of a data center which provide a time delay before an attacker can reach a server to launch a physical attack, even by an insider. We describe how a number of cyber defense strategies can be activated when an attack is detected, some of which can even take effect before the actual attack occurs. The defense strategies provide improved security and are more cost-effective than always-on protections in the light of the fact that on average physical attacks will not happen often -- but can be very damaging when they do occur.
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Cyber Defenses for Physical Attacks and Insider Threats
in Cloud Computing
Jakub Szefer
Yale University
jakub.szefer@yale.edu
Pramod Jamkhedkar
Princeton University
pjamkhed@princeton.edu
Diego Perez-Botero
Princeton University
diegop@cs.princeton.edu
Ruby B. Lee
Princeton University
rblee@princeton.edu
ABSTRACT
In cloud computing, most of the computations and data in
the data center do not belong to the cloud provider. This
leaves owners of applications and data concerned about cy-
ber and physical attacks which may compromise the con-
fidentiality, integrity or availability of their applications or
data. While much work has looked at protection from soft-
ware (cyber) threats, very few have looked at physical at-
tacks and physical security in data centers. In this work,
we present a novel set of cyber defense strategies for phys-
ical attacks in data centers. We capitalize on the fact that
physical attackers are constrained by the physical layout and
other features of a data center which provide a time delay
before an attacker can reach a server to launch a physical
attack, even by an insider. We describe how a number of
cyber defense strategies can be activated when an attack is
detected, some of which can even take effect before the ac-
tual attack occurs. The defense strategies provide improved
security and are more cost-effective than always-on protec-
tions in the light of the fact that on average physical attacks
will not happen often – but can be very damaging when they
do occur.
Categories and Subject Descriptors
K.6 [Managements of Computing and Information
Systems]: Security and Protection; H.1.2 [Models and
Principles]: User/Machine Systems—Human factors
Keywords
Data Center Security; Physical Attacks; Insider Threats;
Cloud Computing; Migration; Cloning; Virtual Machines
1. INTRODUCTION
Physical attacks on computers are less frequent and harder
to launch, compared to software attacks. However, a phys-
ical attack may give an attacker absolute control over a
secure sever, resulting in potentially greater damage. For
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this reason, most institutions that deal with highly sensitive
data, including military and financial companies, go to great
lengths to protect their servers and data centers from physi-
cal attacks. For example, to get into the building, one needs
to scan one’s hand, pass a guard, then go through a hallway
where one is trapped if found suspicious. Also, some servers
are locked in cages and separated from other systems [20].
Many physical attacks are, however, carried out by insid-
ers who have authorized physical access to secure servers,
making them difficult to prevent. Physical attacks on infor-
mation technology (IT) infrastructures have been identified
as one of the most overlooked aspects of IT security [12], and
there has been no, or limited, cyber-physical defenses pro-
posed. Furthermore, as cloud computing becomes prevalent,
data centers will become prime targets for attackers.
Problem Overview: Defense mechanisms against phys-
ical attacks have typically followed two approaches: 1) phys-
ically controlled access and surveillance (entirely in physical
space) and 2) design of secure servers with pervasive en-
cryption and hashed storage (entirely in cyber space). On
the one hand, physical access control mechanisms [11] may
prevent unauthorized physical access to a certain degree,
but are not effective against insider attacks. On the other
hand, pervasive encryption, or continuous encryption of all
memory (and swap disk space), is done by secure processor
proposals, e.g. [22, 15, 6, 3], in which everything is automat-
ically encrypted in DRAM. While the performance overhead
may be acceptable, such architectures are not implemented
in existing commodity processors – and hence are not avail-
able in current servers inside data centers.
Malicious Insiders: A particular concern is that a ma-
licious cloud provider employee, who has “legitimate” phys-
ical access to the data center, can trivially launch physi-
cal attacks. This is precisely the scenario where our cyber-
physical security countermeasures come into play. Today,
any person, authorized or not, can launch physical attacks.
In our proposed system, thanks to the use of the physical
sensors, any sensitive data will be moved or scrubbed before
an authorized employee or outsider has a chance to launch
a physical attack. Note that we add the missing, physi-
cal, piece to the plethora of current, cyber-only, defenses.
Also, our solution applies equally well to external attackers,
insiders and maintenance personnel. Hence, our proposed
solution is different from the existing ones, which are pri-
marily aimed at keeping out the intruders, or are cyber-only
mechanisms which focus on software attacks and not physi-
cal attacks.
Our primary contributions are:
A new cyber-physical defense strategy, where cyber
defenses are used to protect against physical attacks
(Section 2), including a new defense based on virtual
machine (VM) cloning;
Qualitative and quantitative analysis of four cyber de-
fense strategies for physical attacks (Section 3); and
A formalization of concepts and ideas needed to rea-
son about physical attacks in data centers or other
distributed networked systems (Section 4).
Key Advantages: Our cyber-physical defense frame-
work is cost-effective. The cyber defenses against potential
physical attacks are triggered only when the possibility of
such an attack is detected by the surveillance and physi-
cal access control mechanisms. This precludes the need for
pervasive encryption and hashing. It also offers a realistic
solution against insider threats.
Paper Organization: Section 2 provides an overview of
our cyber-physical defense architecture. Section 3 provides
an analysis of the four defense strategies. Section 4 discusses
physical attacks and equations needed to reason about them.
Related work is described in Section 5 and we conclude in
Section 6.
2. CYBER-PHYSICAL DEFENSES
Our work is based on a cloud computing paradigm where
computation is performed inside VMs. Our goal is to protect
code and data inside the VMs from physical attackers.
2.1 Threat Model and the Attacks
We aim to protect against physical attacks on compute,
storage and networking equipment in a data center setting.
This work is orthogonal to, and complements, work on de-
fenses of these entities from cyber attacks. There is already
a plethora of security research on software defenses, e.g. [4].
The main protected entities in our cyber-physical defense
framework are the VMs running on the physical servers in
a data center, which contain applications and data belong-
ing to cloud customers. By extracting the contents of the
VMs, an attacker can gain valuable information, including
proprietary or sensitive data or programs belonging to cloud
customers.
The human threat we are worried about is a physical at-
tacker who has or gains physical access to a data center and
the servers where the VMs are running. A physical attack
can be carried out by different types of individuals including
outsiders, maintenance individuals, insiders, etc. We assume
that once an attacker has physical access to a machine, at
that point in time the attack can be considered successful.
Physical attacks include cold boot style attacks for extract-
ing information from memory chips of the server [10], or
stealing hard drives to extract data from the local disk. At-
tacks can also include turning off the power, or allowing the
machine to overheat by turning off the cooling, to cause loss
of availability to the VMs running on this server.
2.2 Attack Detection and Defense Timeline
Our defense framework against physical attacks is based
on the time difference between potential attack detection
and the actual attack, as shown in Figure 1, which we have
first proposed in [24]. We now, however, consider three
timestamps in our framework: time of detection (tdetect ) de-
noting the time at which a physical attack is detected, and
the time of attack (tattack) denoting the time at which the
attacker has physical contact with the equipment. We also
consider tpreempt, which denotes a pre-attack time when
some defenses can be pro-actively launched. The physical
sensors mentioned previously are used to trigger a warning
at tdetect. Scheduled events, such as maintenance, could be
used to trigger pre-emptive actions at tpreempt .
Figure 1 shows different security mechanisms and their re-
lationship with these three time stamps. Mechanisms shown
in case Aare preventive mechanisms that operate wholly in
the physical space, and their effectiveness ends after an at-
tack occurs. The goal of these measures is to delay tatta ck,
possibly forever (i.e., to shift tattack as far right as possible).
While these defenses may prevent outsider access, they are
ineffective against insider attacks. Mechanisms shown in
case Bare detection mechanisms that operate in physical
space, and their defensive purpose ends after an attack has
been detected; although they can be utilized for evidence
gathering as the attack proceeds, and for forensics after-
wards. Pervasive encryption mechanisms shown in case C
assume that an attacker can attack without any warning
(tdetect =tattack), and data is kept encrypted all the time.
Such mechanisms, even though effective, either incur signif-
icant overhead, or require new hardware.
In our defense framework, we capitalize on the fact that
effective detection mechanisms (case B) coupled with effec-
tive protection mechanisms (case A), both operating in the
physical space, will induce a significant delay between tdetect
and tattack. The physical detection mechanisms (case B)
would produce a warning at tdetect that can trigger appropri-
ate cyber defense mechanisms, based on the expected time
available for realtime response (Tr). We have identified four
primary types of cyber defenses, which fall under cases D
and E, that could be used against physical attacks to pro-
tect VMs. Our defenses consist of three reactive defenses
(case D) and one proactive defense (case E).
1. Migrate: Applications and data are moved away from
the physical servers being attacked so that when the
attacker gains access to these servers, the data is no
longer there.
2. Encrypt: Applications and data are encrypted within
the servers, so that when the attacker gains access to
the servers, the attacker cannot make use of the data
since it is in encrypted form.
3. Delete: Applications and data are deleted from the
servers so that when the attacker gains access to the
servers, the data is no longer there.
tattack
tinfinity
Physical Barriers, Locks, Guards
Cameras, Sensors
Encryption
Migrate, Encrypt, Delete
A
t0 tdetect
B
C
D
physical
defense
cyber
defense
Trespond
tpre-empt
Clone E
Figure 1: Defense strategy timeline. Our new proposed
defense strategies are highlighted with D
and E
.
4. Clone: A VM with applications and data is cloned in
anticipation of a potential attack; when an attack is
detected we only need to erase the memory and any
local storage on the victim server as a clone is already
active and running in a different location.
Each defense strategy has its own limitations in terms of
the time taken to carry out the response, practical feasibility,
computation cost, and the security protections it offers; we
present evaluation of these four in Section 3.
2.3 Cyber-Physical Defense Architecture
The Cyber-Physical Defense architecture consists of three
main components: physical sensors, security management
infrastructure, and compute equipment (e.g. servers).
2.3.1 Sensors and Physical Security Monitors
Data centers routinely include motion sensors, cameras,
electronic locks on doors, etc., [19] and others can easily
be added. These sensors provide an infrastructure that can
signal an alert about an impending physical attack. Such a
warning system provides various degrees of response times
for defense mechanisms to be triggered.
We propose a physical security monitor which collects all
the data from the attached sensors, formats it and converts
it to the standard API (application programming interface)
calls to the management infrastructure.
2.3.2 Security Management Infrastructure
The security manager integrates all the data from the var-
ious sensors. In order to calculate minimum response times
based on attacker paths and hurdles, the data center floor
plan, location of access doors, and location of servers can
be automatically extracted from CAD (computer aided de-
sign) files describing the data center. The possible paths to
a given server and the hurdles (e.g. locks on cages) can be
pre-calculated, based on these inputs, to optimize reaction
times to threats. The management software also calculates
defense times for individual VMs based on bandwidth avail-
ability, server availability, safe destination servers, VM sizes,
etc. The defense trigger mechanism also takes into consider-
ation scheduled events (such as cleaning and maintenance)
and mobility of data center personnel to trigger appropriate
defenses. The defense strategies are translated into actual
VM management commands so that the compute equipment
can carry out the needed defensive countermeasures.
2.3.3 Compute Equipment
The cloud providers use management software such as
OpenStack [17] to manage the actual compute servers. Fig-
ure 2 shows a possible realization of our cyber-physical de-
fense system using the OpenStack cloud computing frame-
work. The sensor and physical monitor infrastructure pro-
vides inputs via a modified Nova API. The inputs are then
passed, via the Queue, to the management infrastructure
which includes the Threat Evaluation and Defense Selection
logic. This obtains floor plans and sensor locations from
a CAD database. The defense triggers are passed via the
message Queue to a modified Nova Compute server, which
then carries out the defense strategies. Nova database is
used by the OpenStack components to store (and retrieve)
information about the different physical servers, status of
the running VMs, etc.
Dashboard Nova-
Volume
Nova-
Network
Nova-
API
Queue
Nova
Database
nova
Storage
iSCSI, ..
.
.
.
Nova-
Schedule
Threat
Evaluation &
Defense
Selection
Logic
Nova-
Compute
Nova-
Compute
Nova-
Compute
Nova-
Compute
CAD
Database
Sensor
Infrastructure
Figure 2: Our cyber-physical security architecture over-
lays onto the OpenStack cloud architecture. Modules
shown in gray are OpenStack parts that would need to
be added or modified.
3. FEASIBILITY OF CYBER DEFENSES
We now describe the defense strategies in more detail, and
also evaluate the feasibility of our defenses with respect to
the available attack response times. We follow the instance
type definitions for Amazon’s EC2 instances [1] (see Table
9 in the Appendix).
3.1 Migrate Strategy Analysis
When employing migration as a defense strategy, confi-
dentiality, integrity and availability are all preserved after
the attack; even if the attacker destroys or steals the equip-
ment. Use of live VM migration allows for reduced down-
time, and erasure of the source host’s memory ensures that
attacker does not gain information from the server he or she
captures. Thus, our proposed defense is actually realized
by complementing migration with the erasure or deletion of
relevant RAM or local disk sectors1, as described in Table 1.
Table 1: Migration based defense strategies.
Strategy Details
Migrate +
Erase RAM
The VM is migrated and the VM’s main mem-
ory contents on the local machine need to be
scrubbed.
Migrate +
Erase RAM
& Disk1
Same as “Migrate + Erase RAM”, but if any
of the VM’s main memory has been swapped
to local disk by the hypervisor, that disk space
needs to be erased as well.
Migrate +
Encrypt
RAM
The VM is migrated, but the local main mem-
ory that was allocated to the VM is encrypted
(local copy of encryption key is scrubbed).
Migrate +
Encrypt
RAM &
Disk1
Same as “Migrate + Encrypt RAM”, but any
local disk contents (e.g. swapped memory of
the VM) are encrypted (local copy of encryp-
tion key is scrubbed).
3.1.1 Migrate Strategy – Feasibility Analysis
The time taken to enact live migration as a defense de-
pends on the type of applications which are running inside
the VMs. We base our evaluation on the 7 types of work-
loads that most commonly arise in data centers [13], which
are listed in Table 2.
To evaluate the migration strategy, our test bed is com-
prised of two hosts with identical hardware and software
1The local disk need not be erased or encrypted if always-
on encrypted storage is used. The encryption key, however,
still needs to be scrubbed from the system’s main memory.
Table 2: Data Center Workloads
Workload Benchmark
Mail
Server
mstone [16] as remote SMTP client; smtp-sink
[21] as SMTP server inside VM
App
Server
Faban Benchmarking Framework [7] as remote
client; Glassfish Server [9] with sample Java
EE application inside VM
File Server Dbench [5] inside VM
Web
Server
Faban Benchmarking Framework [7] as remote
client; Apache HTTP Server [2] inside VM
DB Server Sysbench [23] inside VM
Stream
Server
VideoLAN [26] inside VM; Wireshark [28] cap-
turing stream packets remotely
Idle Server No workload
configurations. Each host comes with dual quad-core Intel
Nehalem CPUs (1.6GHz), on top of which a KVM hyper-
visor is running. The network connecting both hosts sup-
ports 1Gbps speeds. Meanwhile, the VMs being migrated
come with 1 GB of dedicated RAM and 1 CPU core. For
each workload, we ran 2-minute long benchmarks and per-
formed migrations at five different migration points within
that 2-minute interval; those results were then averaged.
This solves the problem of variations due to the migration
point [13].
The results are shown in Table 3. From the data, it can
be seen that total migration time for a 1 GB VM (and thus
the time needed to deploy the defense) is on the order of 10
seconds. The liveness of this strategy is clear: downtimes
experienced by users are negligible (<1 second) for all but
one server workload. A key fact to bear in mind is how criti-
cal available bandwidth is to live migration. For instance, it
took us 19.4 seconds to migrate an Idle Server when using a
100 Mbps link, but only 2.6 seconds with a 1 Gbps link. The
overhead is the difference between execution time when mi-
grating and when not migrating, divided by the time when
not migrating.
Table 3: Migration Performance and Resource Usage
with Data Center Workloads with 1Gbps link.
Server
Type
Total
Time
(s)
Down
-time
(ms)
Data
Sent
(MB)
Over
-head
(%)
Avg.
BW
(Mbps)
Mail 8.9 300 800.39 15.71 702.86
App 7.5 4050 728.21 6.25 766.52
File 7.2 250 697.87 12.71 764.77
Web 7.2 250 645.33 10.08 707.08
DB 7.4 450 717.52 1.39 765.22
Stream 8.0 650 716.21 3.03 705.31
Idle 2.6 200 212.34 0.00 629.11
3.2 Cloning Strategy Analysis
When employing VM cloning as a pre-emptive defense
strategy, confidentiality, integrity and availability are pre-
served. Cloning strategies are listed in Table 4. Cloning
relies on proactively cloning the VM and running multiple
clones of the VM; if any clone is in danger of attack, that
clone is simply terminated while other clones keep running.
3.2.1 Cloning Strategy – Feasibility Analysis
The cost of cloning comes from the duplicate resources
used as the VMs run, e.g. DRAM memory allocated to each
clone. This can be dealt with by installing more resources.
Table 4: Cloning based defense strategies.
Strategy Details
Clone and erase
RAM
Scrub RAM containing VM’s
memory pages.
Clone and erase
RAM & Disk
Scrub RAM and any disk contain-
ing VM’s memory pages.
Also, to be able to use cloning as a defense strategy, there
needs to be sufficient time to proactively clone the VMs.
Cloning re-uses many of the facilities of migration and has
almost identical total time. Details of the cloning strategy
are omitted for space reasons, they are available in [18].
The time required to clone a 2GB VM ranges between
4 and 15 seconds if the full bandwidth of a 1Gbps link is
available between the host machines. As with live migra-
tion, the bandwidth and current processor load will influence
the cloning time. However, since cloning is proactive, the
cloning time can be selected when there is available band-
width and the servers are not under heavy load.
3.3 Encrypt Strategy Analysis
When employing encryption as a reactive defense strat-
egy, confidentiality is preserved after the attack. Avail-
ability, however, is not preserved if the attacker destroys
or steals the equipment and there is no redundant backup
copies stored elsewhere. The defense is realized through en-
cryption of relevant RAM or local disk sectors, as described
in Table 5.
Table 5: Encryption based defense strategies.
Strategy Details
Encrypt RAM Encrypt all of VM’s memory pages in RAM.
Encrypt RAM
& Disk
Encrypt all of VM’s memory pages in RAM
and any memory that has been swapped to
local disk.
Move to En-
crypted Disk
& Erase RAM
If swap space is already using encrypted
storage, move all memory pages to swap
space, and erase RAM contents.
3.3.1 Encrypt Strategy – Feasibility Analysis
Encryption will be affected by both by the algorithm used
as well as the hardware available. We benchmark symmetric
key encryption using the popular TrueCrypt [25] . Based on
the speeds and the VM’s RAM and local disk sizes (from
Table 9) the times for encryption of RAM are obtained and
shown in Table 6. On the test servers (see Section 3.1.1 on
migration) the AES algorithm performed at 450 MB/s and
Twofish at 409 MB/s. We also tested the encryption on a
different system with Intel Core i7 processor with dedicated
AES instructions. The AES algorithm benefited greatly
from the new Intel AES-specific hardware instructions and
achieved 2253 MB/s All measurements were done when the
CPU was not utilized for any other purpose.
Table 6: Encryption of VMs’ RAM (a) without and (b)
with dedicated AES encryption instructions.
Encrypt
RAM
micro m1.small m1.medium m1.large
(a) AES 1.38 s 3.89 s 8.56 s 34.16 s
Twofish 1.52 s 4.28 s 9.41 s 37.58 s
(b) AES 0.27 s 0.77 s 1.71 s 6.82 s
Twofish 1.52 s 4.28 s 9.41 s 37.58 s
3.4 Delete Strategy Analysis
When employing deletion as a defense strategy, confiden-
tiality is preserved, but integrity and availability are not, as
the applications or data are not available anymore after the
deletion. The defense is realized through deletion of relevant
RAM or local disk sectors, as described in Table 7.
Table 7: Delete based defense strategies.
Strategy Details
Erase RAM Scrub RAM containing VM’s memory
pages.
Erase RAM &
Disk
Scrub RAM and any disk containing
VM’s memory pages.
3.4.1 Delete Strategy – Feasibility Analysis
RAM and disk erasure times depend on the write speeds of
the memories and disks. Data can also be overwritten with
a random pattern. Our results in Table 8 are for overwriting
RAM and disks with zeros.
On the test servers (see Section 3.1.1) sequential writes
to main memory can sustain 6481 GB/s. For disk, we use
the 70 MB/s throughput (assuming the erase is just writing
0s at the full speed of the disks we have tested). Table 8
summarizes the times needed to perform the erase strategy
for the different types of VMs.
Table 8: Erasing RAM and local disk of VMs.
Operation micro m1.small m1.medium m1.large
Erasing
RAM
0.10 s 0.27 s 0.59 s 2.37 s
Erasing lo-
cal disk
8.9 s 0.14 s 0.14 s 0.14 s
Note that micro instances have some local swap disk, while
other instance types only have configuration files that count
as data on local disk (they use remote persistent storage).
3.5 Comparing the Defense Strategies
We have presented details of the four defense strategies:
migrate, clone, encrypt and delete. In our evaluation, the
migration strategy takes longer (about 9s), compared to en-
crypting data or erasing the VM, but provides the most secu-
rity, and availability is maintained. The encryption strategy
is faster with less than 7 seconds even for the largest VMs,
if AES-specific hardware instructions are available. The ex-
ception is when there is a lot of VM memory that has been
swapped to disk and that disk has to be encrypted on the
fly. Use of encrypted swap space, however, alleviates this
problem. The fastest strategy, but also offering only confi-
dentiality protection, is the delete strategy. All data is lost,
but the attacker does not get to it. Alternatively, cloning is
a very good strategy if there are enough resources to create
clones, pre-emptively, of the VMs. The defense then is quite
fast (as fast as the erasure strategy) because, if the attacker
threatens one clone, the clone can simply be erased. More-
over, cloning can be configured to use less network resources
and take more time.
4. REASONING ABOUT PHYSICAL
ATTACK AND RESPONSE TIMES
The key to accurately determining the response time for
a given VM is to calculate how “close” is the nearest po-
tential threat from the server on which the VM is running.
This calculation must be carried out taking into considera-
tion the physical locations of different individuals in a data
center, their type, and how easily each of those individuals
can reach the server. We denote the current location of dif-
ferent individuals in a data center as the threat context. For
a given threat context, the estimation of time to respond is
not absolute, but relative to the type of security required by
the VM. For instance, one VM may consider the presence of
a maintenance person in the vicinity as a threat, whereas an-
other VM may not consider it as a threat according to which
security level was selected for the VM. Therefore, two VMs
operating on the same server, under the same threat con-
text, may perceive the same threat with different urgency
and hence have different response times. The calculation
of the estimated response time available for a given VM is
carried out in the following steps:
1. Obtain the locations of all the potential attackers (i.e.
the threat context) and the location of the host server.
2. For each attacker, calculate all paths from the attacker’s
position to the server.
3. Calculate the response time for each attacker based on
the shortest path (in terms of travel time and time to
clear hurdles) for that attacker.
4. Calculate the available response time as the smallest
of all the response times for each attacker.
Step 1 is calculated from inputs provided by the sensors
and physical access control mechanisms providing locations
of all individuals in the data center. Step 2 is calculated
based on the result of Step 1 and the pre-calculated physical
layout of the data center. We define Step 3 as follows. For
a given VM vm running on Server s, and an attacker a, let
P(a, s) represent the set of all the paths from the position
of ato the position of server s. The response time for VM
vm from threat awhile being hosted on server sis:
(1)Tr(vm, s, a) = minpP(a,s){Tw(a, s, p) + Th(a , vm, p)}
where Tw(a, s, p) is the amount of time required by the
attacker ato cover the distance between himself and the
victim server son a given path pand Th(a, vm, p) is the
amount of time required by the attacker to clear all the
hurdles between himself and the victim server son path p.
The value for Tw(a, s, p) can be calculated by the length of
path pdivided by the average running time for a human.
In order to calculate Th(a, vm, p) we need to know what
type of hurdles are present on path p, and how much time
attacker awould take to clear those hurdles. Whether or
not an individual (intruder, maintenance personnel, etc.) is
an attacker or not is determined by the vmssecurity policy.
Step 4 calculates the nearest threat as follows. Let A be
the set of all the potential attackers detected in the data
center. The available response time for a given VM vm
hosted on server sis the minimum of all the response times
calculated for each potential attacker, calculated as follows:
(2)Tr(vm, s) = minaA{Tr(vm, s, a)}
The type of defensive action chosen must fit this Tr.
5. RELATED WORK
Numerous physical measures are used in data centers to
ensure the security of the data center infrastructure from
physical attacks [11]. Barriers, alarms, entry control, con-
traband detection, CCTV (closed-circuit television) surveil-
lance, and other means have been used for protecting data
centers [8]. Many physical security measures mainly focus
on environmental, not human, factors such as fire security
or the failure of supporting utilities (e.g., power ) [27].
Preventing outsiders from gaining access to data centers
typically includes measures like security locks, biometric au-
thentication, isolation of secure areas, sign-in books, two-
factor authentication, etc. These mechanisms are often com-
plemented with mechanisms for detecting physical intrusions
such as cameras, sensors, surveillance guards, etc. These
physical defense measures provide time constraints on any
potential attackers. Each measure slows down the attackers,
although it does not necessarily stop them.
An example of a moving target defense” [14] strategy is
one where VMs are pro-actively moved from one server to
another. Past work, however, has not looked at using physi-
cal triggers as a means of influencing moving target defenses.
6. CONCLUSION
We presented details of a cyber-physical security frame-
work for cloud computing data centers that combines the
security mechanisms in cyber and physical spaces, and ex-
ploits the power of virtualization to provide dynamic se-
curity against physical attackers. The framework protects
against human attackers who can use physical access (ille-
gitimate or legitimate as in the case of insider attacks) to
extract applications or data from the compute infrastructure
inside the data centers. We secure applications and data
through the use of cyber defenses based on the strategies of
migration, encryption, deletion and cloning. While the first
three are reactive defenses triggered by the detection of a po-
tential human attacker, the last one, cloning, is a preemptive
strategy that can be used for the most security-sensitive ap-
plications. We leverage physical intrusion detection systems
to warn of an impending physical attack to trigger the first
three defenses: migration, encryption and deletion. When
there are enough resources for pre-emptive cloning, it can
provide faster response when an attack actually happens.
We hope our work will simulate more research into the use
of cyber defenses for protecting against physical attacks.
7. REFERENCES
[1] Amazon EC2 Instance Types.
http://aws.amazon.com/ec2/instance-types/.
[2] The Apache HTTP Server Project.
http://httpd.apache.org/.
[3] D. Champagne and R. B. Lee. Scalable architectural
support for trusted software. In Proceedings of the
International Symposium on High Performance Computer
Architecture, HPCA, pages 1 –12, January 2010.
[4] W. Dawoud, I. Takouna, and C. Meinel. Infrastructure as a
Service Security: Challenges and Solutions. In Proceedings
of the International Conference on Informatics and
Systems, INFOS, March 2010.
[5] Dbench filesystem benchmark.
http://dbench.samba.org/.
[6] J. S. Dwoskin and R. B. Lee. Hardware-rooted trust for
secure key management and transient trust. In Proceedings
of the ACM Conference on Computer and Communications
Security, CCS, pages 389–400, October 2007.
[7] Faban Harness and Benchmark Framework.
http://java.net/projects/faban/.
[8] L. J. Fennelly. Effective Physical Security.
Butterworth-Heinemann, 3rd edition, 2003.
[9] GlassFish - Open Source Application Server.
http://glassfish.java.net/.
[10] J. A. Halderman, S. D. Schoen, N. Heninger, W. Clarkson,
W. Paul, J. A. Calandrino, A. J. Feldman, J. Appelbaum,
and E. W. Felten. Lest we remember: cold-boot attacks on
encryption keys. Communications of ACM, 52(5):91–98,
May 2009.
[11] S. Heare. Data Center Physical Security Checklist.
Technical report, SANS Institute, December 2001.
http://www.sans.org/.
[12] K. J. Higgins. The 10 Most Overlooked Aspects of Security,
November 2006. http://www.darkreading.com/
security/application-security/208808177/the-
10-most-overlooked-aspects-of-security.html.
[13] D. Huang, D. Ye, Q. He, J. Chen, and K. Ye. Virt-lm: a
benchmark for live migration of virtual machine. In
Proceedings of the International Conference on
Performance Engineering, ICPE, pages 307–316, March
2011.
[14] S. Jajodia, A. K. Ghosh, V. Swarup, C. Wang, and X. S.
Wang, editors. Moving Target Defense: Creating
Asymmetric Uncertainty for Cyber Threats. Springer, 2011.
[15] R. B. Lee, P. Kwan, J. P. McGregor, J. Dwoskin, and
Z. Wang. Architecture for protecting critical secrets in
microprocessors. In Proceedings of the International
Symposium on Computer Architecture, ISCA, pages 2–13,
June 2005.
[16] mstone multi-protocol testing system.
http://sourceforge.net/projects/mstone/.
[17] OpenStack. OpenStack Compute: An Overview.
[18] D. Perez-Botero. Pwnetizer: Improving Availability in
Cloud Computing Through Fast Cloning and I/O
Randomization. Master’s Thesis, Princeton University,
Princeton, NJ, 2013.
[19] S. D. Scalet. 19 Ways to Build Physical Security into a
Data Center. http://www.csoonline.com/.
[20] J. S. Schultz. Should you trust mint.com? From New York
Times http://bucks.blogs.nytimes.com/2010/07/
06/should-you-trust-mint-com/?_r=0.
[21] smtp-sink(1) - Linux man page.
http://linux.die.net/man/1/smtp-sink.
[22] G. E. Suh, D. Clarke, B. Gassend, M. van Dijk, and
S. Devadas. Aegis: architecture for tamper-evident and
tamper-resistant processing. In Proceedings of the Annual
International Conference on Supercomputing, ICS, pages
160–171, June 2003.
[23] SysBench: a system performance benchmark.
http://sysbench.sourceforge.net/.
[24] J. Szefer, P. Jamkhedkar, Y.-Y. Chen, and R. B. Lee.
Physical Attack Protection with Human-Secure
Virtualization in Data Centers. In Workshop on Open
Resilient human-aware Cyber-physical Systems, WORCS,
June 2012.
[25] TrueCrypt - Free Open-Source On-The-Fly Disk
Encryption Software. http://www.truecrypt.org/.
[26] VLC media player. http://www.videolan.org.
[27] M. E. Whitman and H. J. Mattord. Principles of
Information Security. Cengage Learning, 2011.
[28] Wireshark: the world’s foremost network protocol analyzer.
http://www.wireshark.org/.
APPENDIX
Table 9: Local server resources used by different Ama-
zon EC2 VM instance types.
Resource micro m1.small m1.medium m1.large
VM’s RAM 613MB 1741MB 3840MB 15370MB
VM config 10MB 10MB 10MB 10MB
Total
RAM
623MB 1751MB 3850MB 15380MB
Local Swap
for VM
613MB n/a n/a n/a
VM config 10MB 10MB 10MB 10MB
Total
Disk
623MB 10MB 10MB 10MB
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