Journal of Information Security, 2010, 1, 1-10
doi:10.4236/jis.2010.11001 Published Online July 2010 (
Copyright © 2010 SciRes. JIS
Micro-Architecture Support for Integrity Measurement on
Dynamic Instruction Trace
Hui Lin1, Gyungho Lee2
1ECE Department, University of Illinois at Chicago, Chicago, USA
2College of Information and Communications, Korea University, Seoul, Korea
Received July 12, 2010; revised July 16, 2010; accepted July 20, 2010
Trusted computing allows attesting remote system’s trustworthiness based on the software stack whose in-
tegrity has been measured. However, attacker can corrupt system as well as measurement operation. As a
result, nearly all integrity measurement mechanism suffers from the fact that what is measured may not be
same as what is executed. To solve this problem, a novel integrity measurement called dynamic instruction
trace measurement (DiT) is proposed. For DiT, processor’s instruction cache is modified to stores back in-
structions to memory. Consequently, it is designed as a assistance to existing integrity measurement by in-
cluding dynamic instructions trace. We have simulated DiT in a full-fledged system emulator with level-1
cache modified. It can successfully update records at the moment the attestation is required. Overhead in
terms of circuit area, power consumption, and access time, is less than 3% for most criterions. And system
only introduces less than 2% performance overhead in average.
Keywords: Integrity Measurement, Remote Attestation, Software Vulnerability, Trusted Computing
1. Introduction
Nowadays, computer under different platforms interacts
with each other through internet environment. Although
this provides convenience and increased functionality, it
is necessary to securely indentify software stack running
in remote systems. Effective remote attestation mecha-
nism has drawn lots of research interests. Trusted Com-
puting Group (TCG) first standardized the procedure to
launch a remote attestation [1]. As defined, the protocol
consists of three stages: integrity measurement, integrity
logging, and integrity reporting [2]. The function of in-
tegrity measurement is to derive a proper measure that is
an effective representation of a given platform status. In
order to narrow down the range of such measures,
Trusted Computer Base (TCB) is defined as hardware
components and/or software modules whose integrity
decides the status of a whole platform. Consequently,
integrity measurement can simply based on measures
from the TCB, which reduce performance overhead in
measurement and attestation. Integrity logging is the
process of storing aforementioned integrity measure in
protected storing space. This process is not mandatory,
but highly recommended to reduce the overhead due to
repeated calculation for integrity measurement. The last
step, which is called integrity reporting, is to attest sys-
tem based on the stored or calculated integrity measures.
Computer systems emphasize different security goals
per contexts. While system integrity is more important in
one situation, the other may concern more about data
privacy. Integrity measurement is strongly related to se-
curity policy applied to specific computer system and
consequently results in different attestation mechanism.
TCG’s specification describes an integrity measurement
during system’s booting process. This mechanism is
called “trusted boot”. At the very beginning, a hardware
signature, which is stored in some security-related hard-
ware components, is used as the root of the trust. Current
hardware vendors design Trusted Platform Module
(TPM) to provide such functionality. As each entity is
loaded into memory, the integrity measures on the bina-
ries are calculated one by one and form a trust chain at
last. Unlike secure booting, system takes measurements
and leaves them to the remote party to determine sys-
tem’s trustworthiness. TCG’s attestation based on such a
trusted booting is also called binary attestation [2].
Other integrity measurements still follow TCG’s
“measure-before-load” principle. Property attestation and
semantic attestation both try to extract the high level
Copyright © 2010 SciRes. JIS
property or semantic information from binary measure-
ment. So it will be more efficient and effective to vali-
date whether a security policy is hold or violated on such
a measured property a priori. IBM’s Integrity Measure-
ment Architecture (IMA) based on the TCG’s trusted
booting extends the approach into application software
stack. IMA is now a security module provided by Linux
kernel since version 2.30 [3].
A good integrity measurement should be able to derive
a reliable measure that represents the status of computer
system. From the resulting measure, a challenger (the
remote entity which is interested in attesting the system)
should be able to tell the system’s updated security-rela-
ted capability such as whether the memory has been ever
corrupted by attacker, or whether programs can be prop-
erly executed in isolation, or whether cryptography keys
are securely stored, and so on. On the other hand, meas-
urement procedure should be transparent to the local user
and introduces little performance overhead.
Current integrity measurements face problems of gath-
ering sufficient history information on what has been
done to the computing device. When each entity is
loaded into memory, measurement of its binary codes is
recorded. However, there will be a “measurement gap” at
the moment when measurement results are requested.
System status may be different from the recording in
measure. Furthermore, measurements are made directly
on program’s executable code residing in main memory.
There exists another “behavior gap” between instructions
executed in the processor and executable codes in the
memory. The integrity measure of executable code in the
memory can be a good measure to represent the system
state. However, as different attacks occur from internet,
this is becoming less sufficient for a remote challenger.
For those programs running for a long time, such as
server programs, a static measurement prior to execution
may have little relation to the system status at the current
moment. As a result, more accurate measurement, which
can include program behavior, needed to tell challenger
all history of bad behavior. This results in a better deci-
sion on trustworthiness of the system.
However, with more information included, overhead
to measure programs’ state increases. As a result, some
measurements are targeted to specific data, such as proc-
essor control data, function pointer in memory, network
traffic, intrusion detection, and so on. Measurement is
often restricted in order to utilize only limited amount of
information. Consequently validation of system against a
certain security policy introduces little performance
overhead. This policy-driven attestation or validation
schemes are largely based on limited information spe-
cific per intended attack scenarios. The problem is that
although it is efficient in their proposed situation, port-
ability of such measurement is very low. In different
situation, attestation may require a big modification
which also exerts a large performance penalty.
In order to provide updated integrity measurement as
system evolves, we propose an original dynamic instruc-
tion trace measurement (DiT) to include in the metric
dynamic instructions-level behaviour in the processor
with the help of simple micro-architecture modification.
However, instruction-level trace can vary from time to
time, with some part of the program being executed more
frequent than the other. Directly recording the processor
behavior causes lots of performance overhead and with-
out increasing any accuracy. In stead of applying meas-
urement in processor, we still perform the operations on
the memory. As a result, most function interfaces pro-
vided before, such as the ones proposed in TCG or
IBM’s IMA, can be maintained.
Cache is an evolutionary design building a bridge be-
tween the memory and processor to reduce access delay.
However, in this paper, we modify the structure of the
instruction cache to the one similar to the data cache. The
consequence is that instructions can also be written back
to the memory. As program continues its execution, code
region in its address space no longer stores codes loaded
before execution but records instructions which are exe-
cuted. We improve the integrity measurement for trusted
computing in the following aspects:
1) Extending the measurement scope. When the secu-
rity-sensitive program is loaded and starts execution, DiT
writes back instructions into memory. Consequently,
binary code located in its address space records instruc-
tions which are actually executed.
2) Facilitating attestation for different security policy.
DiT only replaces static measurement with dynamic one.
As a result, it changes little on the high level interface
and provides a better general solution to diverse scenar-
3) Writing back instructions does not require the in-
volvement of operating system. Thus, DiT builds a con-
nection between what has seen inside processor and what
resides in memory. This procedure does not require
trusting operating system, which in some cases can be
corrupted by attackers.
The paper is structured as follows. Section 2 presents
the background on trusted computing and integrity meas-
urement. In Section 3, we present DiT’s design in details.
To avoid potential hazards from attacks, we propose
several hardware-wise recommendations in Section 4.
The experimental results and analysis are given in Sec-
tion 5. Finally, the related work and conclusion are made
in Section 6 and Section 7.
2. Background
2.1. Trusted Computing
Trusted computing deals with computer system in a haz-
Copyright © 2010 SciRes. JIS
ard environment. Though there is lack of ubiquitous
definition of trust, this paper refers the one from Trusted
Computing Group (TCG) specification. Trust is men-
tioned as the expectation that a device will behave in a
particular manner for a specific purpose [2].
Trusted Computing Base (TCB) is specified as any
hardware and/or software components within the inter-
ested platform, whose safety can affect the status of the
whole system. The assumption is made that if TCB is
safe, system can be trusted. However, TCB’s compo-
nents vary from systems. In some situations, it may work
with integrity validation mechanism; as a result, run-time
critical data values are included in TCB. However, on
other situations, execution of security-sensitive programs,
such as encryption/decryption operation, is important to
system’s proper function; some architecture components,
which guarantee privacy of such application program,
are chosen in TCB. TCG has summarized diverse appli-
cation scenarios and concludes that it should include the
following two characteristics:
1) Isolated Execution, or protected execution. The
computing platform should be able to equip security-
related application program with an isolated environment.
As a result, no other legacy programs can access or cor-
rupt information it relies on. To achieve this property,
many researchers adopt the virtualization approach or
hardware extension to legacy computer architecture [4].
2) Remote Attestation. Each computing platform
should be able to provide mechanisms to: (1) securely
measure TCB’s safety state; (2) protect measure log
stored locally; (3) transmit measure to remote challenger.
2.2. TCG’s Binary Attestation
TCG defines a binary attestation to provide a trusted
booting. Whenever an entity is loaded into memory from
the moment machine is physically turned on, TPM ap-
plies cryptographic hash function, say Hash, on its ex-
ecutable code to make a measurement result, say M. The
binary measurement for each entity is logged separately.
Additionally, each measurement is also stored in one of
Platform Configuration Registers (PCRs) in TPM by
making the cryptographically extend operations with
PCR’s current value, PCRt, i.e., new PCR values PCRt + 1
= Hash(PCRt|M), where|denotes concatenation. When
verifier requires attestation, TPM sends measurement
logs (in local hard disk) and the corresponding PCR
value to the verifier. He will recalculate hash result based
on measurement logs. The comparison between newly-
computed hash result and PCR value can tell whether
untrusted behaviour within the environment has ever
modified PCR value, measurement log, or executable
code itself.
Using binary attestation facilitate verification in mainly
two aspects. 1) measurement with such format hides
many different high-level implementations and reduces
the complexity to calculate measure log and PCR value;
2) It successfully separates measuring and verification.
Attestation does not try to prevent a system from illegal
behaviour that might compromise system. It only records
the history of loaded code, securely sends them to the
verifier and leaves the verifier to make trustworthiness
2.3. Integrity Measurement on the Application
Starting from the root of trust provided by TCG, Integ-
rity measurement architecture (IMA) from IBM takes the
first step to extend measurements from booting process
to application level programs. IMA is provided as a
software module to Linux kernel from the version 2.30.
It provides measurements regarding to current system’s
software stack. The whole project provides integrity
measurement but does not propose any detailed attesta-
tion mechanism. Measurements provide evidences sh-
owing whether system is corrupted by certain rootkit
attacks or not.
IMA measures each individual component before it is
loaded. With the help of extend operation, trusted boot-
ing forced execution to follow only one legal order.
However, in application level, programs can execute
different threads in parallel; program order does not re-
lated to trusted condition any more. So IMA groups
measure together instead of applying extend operation
one by one.
But IMA’s is following TCG’s “measure before load-
ing” principle, therefore it inevitable maintains short-
comings of the binary attestation, such as its ineffective-
ness to reveal hardware attacks or the software attacks
after the program is loaded and executing.
3. Architecture Extension to Measure
Instruction Level Behaviour
3.1. Design of Integrity Measurement in
Application Level
DiT is based on IBM’s IMA which provides comprehen-
sive measurement over software stack. In IMA, all ex-
ecutable codes and chosen structured data are included in
the measurement log. Any data which are loaded by op-
erating system, dynamic loaders, or applications with
identifiable integrity semantics are hashed. Measurement
can be made automatically at the moment when codes or
data are loaded into main memory. As programs continue
their execution, kernel is able to measure its own
changes. Similarly, every user level process can measure
Copyright © 2010 SciRes. JIS
its own security sensitive inputs, such as its configuration
files or scripts. The consequent 160 bit value from hash
calculation becomes an unambiguous identity for such
software module. Challenger can distinguish different
file types, versions, and extensions by this unique fin-
As system evolves, IMA collects hash results into a
measurement list which is stored locally. The integrity of
this list is of a great importance. Therefore, IMA uses
TPM to prevent any modifications made on measurement
list. Platform Configuration Register whose value can
only be changed by physically system rebooting or TPM
extend operation provides protected storage. Extend op-
eration is applied on each value stored in the measure-
ment list. Since it is impossible to restrict application-
level softwares into a small number of orders, order of
each value in the list is not used to validate the trustwor-
thiness of the system.
3.2. Writing Back the Instructions
Although IMA provides measurement of all loaded soft-
ware, it still follows TCG’s “measure-before-loading”
mechanism. As a result, “metric gap” and “behaviour
gap” can largely degrade efficacy of measure log.
The “metric gap” occurs when measurement does not
represent the updated state of the system. Application
program can run for a long time, such as server program.
So it may be a long period since the measurement is
made. During this time interval, memory is possible to be
corrupted. Attacks, who can take root privilege, can
modify loaded executable codes. However, it is possible
to detect such modification when the codes are being
executed again. This is the basic assumption made in
former tamper resistance design [5]. As executable code
is hashed again, resulting measure will be different.
However, attestation is made asynchronously to system’s
operation. It is possible that attestation is made before
executable codes are hashed again. As a result, meas-
urements may give challenger a misinformation about
what is running at the moment.
Figure 1 makes a comparison between three meas-
urement mechanisms: DiT proposed in this paper, IMA
and Aegis which is a typical secure processor design to
achieve tamper evidence and resistance environment [5].
When IMA measures executable code, it makes com-
parison to values which are calculated before. In Aegis,
if software’s execution relies on a program, the meas-
urement of this program is calculated again and com-
parison is made to former calculated value. In these two
situations, the challenger may still get measurements
from which the system can regarded as trusted but actu-
ally the memory is already corrupted.
“Metric gap” can be resolved by applying a measure to
executable code at the moment of attestation is made
Load Execute Don e
Va lidat e
Program Execution Procedure
Represent the event of possible attacks which corrupt memory
Represent the event that remote attestation is required
Represent the event the program is used by other application
Figure 1. “Metric Gap” occurs in the design of IMA and
(which is also reflected in Figure 1). However, “behav-
iour gap” can further introduce more severe problem.
This describes the fact that static codes in memory are
different from instructions executed in processor. But it
is instructions executed in processor finally corrupt the
system. On the other words, executing instructions are
truly represent the trustworthiness of the system. What
makes things worse is that many attacks do not rely on
the modification on program’s executable code to launch
malicious behaviour any longer. For example, buffer
overflow attack has diverse implementations. One of
them is to insert codes directly in stacks which make
detection only possible for a very short period of time.
Challenger should also be able to know such deleterious
execution since this system is vulnerable to attacks in the
No matter how attacks exploit software vulnerability,
it finally needs to execute its code in the processor. As a
result, researchers also propose to records behaviour in
the processor. To reduce performance overhead, they
only analyse behaviour of critical instructions, such as
indirect branch or critical data. Measuring those data
may work for certain security policy but lacks of port-
ability and extendibility to future unknown attacks.
Measuring all instructions is a challenge. Instructions
are fetched from memory, but dynamic execution flow
varies from situation to situation. It is impossible to pro-
vide limited number of unique state to represent safety of
such execution. On the other hand, collecting all possible
states are computationally impossible to make.
DiT does not directly measure all executed instruction
in processor. It maintains large part of original measure-
ment interfaces which measure codes in memory. What
DiT successfully makes is to extend architecture’s pipe-
line to build connection between processor and memory
(Figure 2). It proposes to store back instructions into its
original locations after they are fetched into pipeline. The
Copyright © 2010 SciRes. JIS
Processor Pi
Instruction Cache
Main Memor
Figure 2. Strcture to measure dynamic instruction trace.
purpose is to resolve “behaviour gap” between processor
and memory. This is not an intention to record all possi-
ble run-time execution paths but to store instructions
which are truly executed into measure log.
With such modification, what to measure and when to
measure have to be carefully designed. Program’s ad-
dress space consists of data region, code region, and
stack to record program execution context. In IMA, all
executable code and part of related data, which are dy-
namically loaded by operating system, are measured
(Figure 3). DiT will cover all code regions, data regions
and stack as long as there are some instructions being
written back to them.
Due to attacks, instructions can come from other loca-
tions rather the code region. This not only makes DiT to
expand measurement range to include memory region
such as stack, but also require it to add several temporal
points to make such measurement. We can still use the
aforementioned buffer overflow as the example. Stack
contents vary as program enters into different contexts.
Malicious code hidden there may soon be overlapped by
unrelated information, such as parameter passed by fol-
lowing function call. As a result, malicious code should
be measured on time before it is eliminated by legal
To insert proper temporal points is a trade-off between
detection ability and performance overhead. The per-
formance overhead in original integrity measurement
mechanisms is amortized, which is due to the fact that
hash calculation is made at the frequency of program
loading. From many former anomaly-detection ap-
proaches, successful corruption usually results in some
changes in instructions level behaviour, such as cache
miss, prediction miss and so on [6]. Furthermore, hash
operation, which calculates memory code, is easily per-
formed in parallel with program’s normal operation. In
the current work, one inevitable measurement is added.
DiT launches the measurement at the moment of attesta-
tion requirement is made, which at least resolve the met-
ric gap between measure and system state.
Dynamic Code
Data with integrity semantic is loaded by operating
Executable codes are fetched from memory
Malicious codes are fetched in statck or other illegal location
Codes are executed dynamically
Figure 3. Behavior gap occurs due to attacks or dynamic
generated code.
3.3. Introduce Randomization through the Use
of Cache
Most personal computers usually have two level caches.
Instruction and data are divided in the level-1 cache
while level-2 cache is usually a unified cache which
stores them together. DiT includes cache into the proce-
dure of writing back instructions to the memory which
“reverse” the procedure when instructions are fetching
from it. In order to make write back work, instruction
cache should be appended with few state bit just as data
cache does.
By replacing structure of individual cache to the one
of data cache, processor actually does not need to have
the actual action of “writing back”. It only needs to set a
corresponding status bit and leave the work to cache and
memory management unit. Whenever cache miss occurs,
instruction cache first stores values in cache entry back
to the memory and then read other instructions instead of
overwriting it directly.
Usually, it is hard to predict cache miss. This random-
izes the time to write back instructions. As a result, an-
other level of protection which prevents attacks from
learning this measurement and hide its malicious codes
can be made. Besides, this operation does not need the
involvement of operating system. Even when OS is not
trusted, such as the kernel is corrupted, writing back op-
eration can always be executed properly.
Current micro-architecture design can further help our
design to write back instructions. Since level-2 cache is
unified, only level-1 instruction cache requires modifica-
tion. And the modification is restricted to small number
of status bits added to each cache entry. As a result,
overhead on chip area, power consumption and access
time to cache entry (which is also called cache hit la-
Copyright © 2010 SciRes. JIS
tency) is reasonable. Furthermore, instructions usually
holds much better locality references than data cache
which results in much less cache miss. Consequently,
performance effect from writing back instructions is also
possible to be restricted to a small amount.
4. Further Micro-Architecture
With the proposed design, DiT is able to measure large
amount of program’s execution. However, it may still
miss some situation due to current operating system de-
sign as well as diverse attacking mechanism. In this sec-
tion, we propose several extra hardware recommenda-
tions to further resolve those issues.
4.1. Adding Measurement Point
With the aid of DiT, measurement will be recalculated
with program’s execution. There is still a possible hazard
that attacker replaces correct codes to the malicious ones
(that he injects before) in memory to avoid proper meas-
uring (similar to the way he/she can insert malicious
code) after malicious codes are stored back. As a result,
adding more measurement points is necessary to provide
another protection level on DiT itself.
The cache miss or branch prediction miss indicate a
behaviour change in instruction level, which can be used
as a point to recalculate measurement. To further reduce
performance, we propose to make the measurement at
the moment when the potential attacks are going to hap-
pen. However, from current study in software vulnerabil-
ity, to detect the proper attacking potential is proved to
be another difficult issue. As program is running, its ad-
dress space records its execution state through the use of
stack and/or heap and so on. However, its code space
remains stable. Operating system design provides a good
protection when it launches different code space to exe-
cute, such as the design of context switch. However, at-
tackers successfully inject or exploit new or existing
code space to avoid reliable operation provided from
operating system.
As a result, we can make measurement when instruc-
tions are written back to the memory location which is
outside of the code region (not address space) for the
current running programs. As each program is loading its
code, we can records its physical address in memory into
a table and store it in a memory management unit. A
comparison between written back instruction and each
physical address of a code region can indicate which
program this instruction is belong to. If it does not be-
long to any legal program, we can raise an exception. On
seeing this exception, measurement is not also necessary
since action of avoiding measurement is made.
By such architecture recommendation, DiT can achieve
the validation such that every instruction executed in
processor should be from executable code space which is
properly loaded into memory before. Consequently, DiT
can prevent injected code attacks while making meas-
4.2. Measuring the Run-Time Generated Code
Different from compiler which generates executable
codes, interpreter executes machine instructions on the
fly. In our proposed design, integrity measurement is
only capable of measuring binary codes of interpreter
itself, dynamic codes generate by interpreter to processor
are not recorded (Figure 3). On the other hand, more
popular attacks begin to adopt this mechanism. Such
attacks, including sql injection, cross script attacks,
dominate current web applications. This presents a big
challenge to provide accurate measurements to remote
challenge, as malicious behaviours are extracted from
user input and getting execution one instruction by an-
other. Measuring executable codes from memory be-
comes impossible.
When instructions are generated from interpreter, DiT
finds that there is no source memory location to which
such dynamic instructions can transmit. Our proposed
method is to “deceive” the interpreter that the dynamic
executed codes is actually dynamic loaded. As a result, it
can follow the predefined procedure to make such meas-
This is achieved by creating a new memory region
which can be linked to the memory space of interpreter’s
process. Current operating system, such as Linux kernel,
provides safe interface to dynamically add or remove
memory region from process’ address space. It will be
easy to include such secondary code region to inter-
preter’s address space.
This is equivalent to adding a container to store dy-
namically executed code; however, the measurement will
not be possible at the “load” time, since the container is
empty at this moment. Only at the end of execution when
all executed codes are written back, proper measure is
going to be made on the full container.
5. Experiment and Result Analysis
In order to analyse applicability of DiT, two sets of ex-
periments are conducted respectively. The first one simu-
lated measurement mechanism, especially the situation to
hash program’s code upon asynchronous attestation.
Then another set of experiments are made to detect hard-
ware and performance overhead caused by modification
on level-1 instruction cache.
Copyright © 2010 SciRes. JIS
5.1. Implementing Measurement
Different from IBM’s IMA which implements all integ-
rity measurements within Linux kernel, we implement it
in the hardware level. DiT is integrated into Bochs which
is a full-fledged open source × 86 PC emulator. It is used
to emulate entire system from × 86 architecture to virtu-
ally instrumented monitor.
Through our experiment, we find that write back in-
structions to memory causes some instability for emu-
lated system. As a result, DiT focuses on certain target
program and only stores its on-fly instructions into m-
emory. As mentioned before, TCB provides an isolation
execution environment for the security-related programs.
By implementing writing back instructions for only in-
terested program, we believe that DiT can more practi-
cally simulate TCB’s execution model.
We install Gentoo Linux with the kernel of version
2.6.29 in the emulation. To track process information,
kernel is modified so that hardware emulator becomes
aware of software context switch. Since version 2.6.x,
kernel introduces the late binding for the context switch,
so both exec () and sched () functions are modified. Con-
sequently, process identity, such as Process ID and
Process name is updated into a global variable as soon as
process is created and loaded into memory.
Besides the operating system modification, we also
implement several virtual debugging monitor. One of the
most critical interfaces which DiT inserts is the one that
halts the execution of current program in emulated oper-
ating system and hashes the code region in the address
space of current active process. This efficiently emulates
the situation that measurement is made upon the attesta-
tion request is sent from remote challenger.
5.2. Performance Overhead
In order to make instructions cache to write back, several
extra status bits are required to each cache entry which is
similar to the structure in data cache. Since in most mi-
cro-architecture design, level-2 cache is designed as a
unified cache, only level-1 instructions cache needs
modifications. To make a comprehensive analysis of
such change, area, power consumption and access time is
emulated under CACTI 5.0 [7]. The parameter of un-
modified cache is the same as the one used in Table 1,
which is also used in SimpleScalar for performance ex-
periment. Five extra bits are added to each entry of the
instruction cache to implement the write back mecha-
nism. With the simulation results given from Table 2,
largest power overhead is less than 10%. Overhead of
other criterion is actually ignorable. Especially, modifi-
cation has little effect on access time of level-1 cache.
Table 1. Architecture parameters.
Parameter Value
width 4
Instruction window 128 entries
register update unit size 128 entries
Load/Store Queue 64 entries
I-cache 128K 1 way set-asso., 1-cycle hit time
D-cache 128K 1 way set-asso., 1-cycle hit time
L2 cache Unified, 1M, 4 way set-asso, 6 –cycle hit
Memory 100 cycles access time, 2 memory ports
Function unit 4 Int ALUs, 1 Int MUL/DIV, 4 FP
Adder, 1 FP MUL/DIV
Table 2. Area, power and access time overhead for modified
L1 cache.
L1 Cache
Modified L1
Cache Overhead
Area (mm^2)2.59811765 2.669091732.73%
Power (W) 5.23044172 5.237871430.142%90 nm
Access time (ns)1.40756434 1.407564340.00%
Area (mm^2)0.36714162 0.369299740.588%
Power (W) 3.54005779 3.879765419.59% 32 nm
Access time (ns)0.43442463 0.438758090.998%
We tested SPEC2000 benchmarks running in Simples-
calar which models an out-of-order superscalar processor
[8]. Reference inputs are adopted and we skip instruc-
tions of the number which is specified by SimPoint [9].
Writing back instructions are not supported in Sim-
plescalar, as a result, we modify source codes of sim-
outorder (the out of order simulators) such that right after
each time a read access is performed to the level-1 cache,
a write access to the same entry in the cache is launched.
The parameter to run Simplescalar is given in Table 1.
We collect all number of level 2 cache access and
cache misses for each program in SPEC 2000. The num-
ber of level-2 cache access varies to different programs.
In eon, perlbmk and vortex, the modified level-1 cache
increases more than 50% of level 2 cache accesses. But
for other benchmarks, the change is not that obvious. We
only select the increase of level-2 cache access with
more than 0.01% among all 26 programs (Figure 4).
Copyright © 2010 SciRes. JIS
L2 Cache access L2 Cache Miss
Figure 4. Normalized Level-2 cache access and cache misss.
Although there are big increases in level-2 caches ac-
cess, this does not simply increase the corresponding
cache miss. All cache miss due to the modification of
level-1 cache is increased with less than 1%. This is
probably due to the fact that level-2 cache holds a good
locality references for instructions. As a result, perform-
ance overhead for all benchmark programs is ignorable
as shown in Figure 5. The largest performance overhead
measured in IPC is less than 5%.
6. Related Work
6.1. Tamper Resistance Design
Execute Only Memory (XOM) has included whole me-
mory space in the trusted computing base as most adver-
saries launch the attacks to corrupt memory [10]. In or-
der to guarantee both integrity and privacy of the data in
memory, encryption components are included in the leg-
acy architecture design. Data transmitted from processor
to memory is encrypted and reversely, they are decrypted
for execution in processor
Aegis [5] follows the same assumption that memory
can not be trusted. It hashes executable code when a
program is loaded into the memory for execution. At this
moment, any other code and data that the program relies
on is checked to guarantee that the program is started in a
trusted environment. In the situation that operating sys-
tem can not be trusted, Aegis introduce security related
module and hardware component into the legacy proc-
essor. Tamper resistance design does not make assump-
tion on how memory is corrupted thus it is able to detect
simple hardware attacks.
Tamper resistance design is similar to our approach in
the way of measuring untrusted code. However, they are
holding the assumption that detection of static code can
be found on moment the software is used again. As men-
tioned before, attestation can be made before next-use of
Normal L1 Cache Modified L1 Cache
Figure 5. Comparison of IPC number with normal Level-1
cache and modified L1 cache in processor.
software modules, so directly adopting tamper resistance
approach introduces “metric gap”. On the other hand,
they are unable to measure program’s runtime behaviour
as well.
6.2. Integrity Measurement
TCG first standardize the procedure to make a remote
attestation, besides, it also recommends an integrity
measurement methods which is efficient during system
booting. This binary attestation can only record what the
programs are running on the platform and use the iden-
tity and the loading order of programs to system state
after booting.
IBM’s IMA, Integrity Measurement Architecture, in-
serts measurement interface into Linux kernel. As each
program is loaded into memory, its executable code is
hashed. When a program is further loading other codes
or security-critical data structure, measurement is made
as program transfer its control flow. However, software
vulnerability which is exploited by attackers during each
individual program’s execution can also spoil measure-
Based on the observation that modifications made in
kernel space is usually permanent, Loscocco et al. pro-
pose to measure dynamic data structure which is critical
to kernel control flow [11]. Such dynamic data structure
is called contextual information, which is used to repre-
sent the state of the whole computing system. But this
method is not efficient to be used in the user space op-
6.3. Property Driven Remote Attestation
Binary measurement has the advantage of easy calcula-
tion and application-independence. Since hash calcula-
tion is irreversible, directly exploiting such metrics pro-
Copyright © 2010 SciRes. JIS
vides a big challenge and performance overhead. As a
result, different attestation, which adopts different met-
rics, is proposed.
With specific security policy being set for the attesting
system, property attestation and semantic attestation [12-
14] propose to derive system high level information in-
stead of the pure software stack. The extracted metrics
can be directly used against security policy. Measure-
ment methods may be implemented differently, but
measurement is decided by security policy. As security
policy changes, it is less flexible to change measurement
implementation accordingly. As they indirectly include
validation part into attesting platform, attesting plat-
form’s performance overhead is increased and validation
procedure is also put under the hazardous environment.
We propose DiT which designs an application-inde-
pendent measurement which separates validation and
measurement just as binary attestation does.
Some other researches also consider that program’s
run-time behaviour as a validation metrics, however,
with many limitations. Alam et al. propose a behaviour
attestation method [15]. However, the behaviour is de-
fined as the quality of service the system can provide,
connection latency, and so on. Consequently, this attesta-
tion implementation designed for web services only
which lack the portability to be applied to other applica-
tions programs.
7. Conclusions
Ever since TCG standardized the procedure to launch a
remote attestation, how to exchange the trust measure
efficiently between computer systems under diverse pla-
toforms has been a popular open research issue. Locally,
attesting mechanism derives integrity measure based on
software stacks on which trust decision is made. TCG
introduces a binary attestation during system booting and
many integrity measurement implementations are pro-
posed following the “measure-before-loading” principle.
Those measurements do not take into the account the
actions after each program begins its execution. As a
result software vulnerability which can corrupt both sys-
tem status as well as measurement operation can intro-
duce the “behavior gap” and the “metric gap” between
program runtime behavior and consequent measurement.
DiT, the dynamic instruction trace integrity measurement,
is proposed as assistance to the current integrity meas-
urement methods. By changing the structure of instruc-
tion cache, instructions are stored back into memory
when cache miss occurs. As a result, code region in pro-
grams address space actually contains dynamic instruc-
tions trace executed in processor. By applying integrity
measurement based on this change, DiT successfully
include most updated system state to the moment when
attestation is required.
We have experimented this attestation mechanism in
bochs, a full-fledged emulator, with a current updated
version of Linux kernel installed. We have successfully
simulated the procedure of measuring program’s code (or
trace) at the time when attestation is made. To further
analyze the change made in level-1 instruction cache,
Cacti is exploited to check area, power consumption and
access time overhead. SPEC2000 benchmarks are run on
the modified Simplescalar to analyze the performance
overhead. As we only limit our small modification in
level 1 instruction cache, the overhead in terms of circuit
area, power consumption, and access time are all rea-
sonable, and also the performance overhead is marginal.
8. Acknowledgement
This work was supported by the IT R&D Program of
MKE/KEIT (2010-KI002090, Development of Technol-
ogy Base for Trustworthy Computing).
9. References
[1] “Trusted Computing Group.” http://www.trustedcompu-
[2] TCG Specification Architecture Overview Specification
Revision 1.4, Trusted Computing Group (TCG), 2007.
[3] IBM Integrity Measurement Architecture (IMA). http:
[4] J. M. McCune, B. Parno, A. Perrig, M. K. Reiter and A.
Seshadri, “How Low can you Go Recommendations for
Hardware-Supported Minimal TCB Code Execution,”
Proceedings of ASPLOS’08, Seattle, Vol. 43, No. 3, 2008,
pp. 14-25.
[5] G. Edward Suh, D. Clarke, B. Gassend, M. Dijk and S.
Devadas, “AEGIS: Architecture for Tamper-Evident and
Tamper-Resistant Processing,” Proceedings of ICS’03,
San Francisco, 2003, pp. 160-171.
[6] Y. X. Shi and G. H. Lee, “Augmenting Branch Predictor
to Secure Program Execution,” Proceedings of DSN 07.
[8] T. Austin and D. Burger, “The SimpleScalar Tool Set,”
University of Wisconsin CS Department, Technical Re-
port No. 1342, June 1997.
[9] T. Sherwood, E. Perelman, G. Hamerly and B. Calder,
“Automatically Characterizing Large Scale Program Be-
havior,” Proceedings of the 10th ASPLOS, California,
Vol. 37, No. 10, 2002, pp. 45-57.
[10] D. Lie, C. Thekkath, M. Mitchell, P. Lincoln, et al., “Archi-
Copyright © 2010 SciRes. JIS
tectural Support for Copy and Tamper Resistant Software,”
SIGPLAN Notice, Vol. 35, No. 11, 2000, pp. 178-179.
[11] P. Loscocco, P. Wilson, A. Pendergrass and C. McDonell,
“Linux Kernel Integrity Measurement Using Contextual
Inspection,” STC’07: Proceedings of the 2007 ACM
Workshop on Scalable Trusted Computing, Virginia,
[12] L. Chen, R. Landfermann, H. Lohr and C. Stuble, “A
Protocol for Property-Based Attestation,” Proceedings of
STC’06, the ACM Press, Virginia, 2006, pp. 7-16.
[13] A. Sadeghi and C. Stuble, “Property-Based Attestation
for Computing Platforms: Caring about Properties, not
Mechanisms,” Proceedings of NSPW’04, New York,
2004, pp. 67-77.
[14] V. Haldar, D. Chandra and M. Franz, “Semantic Remote
Attestation: A Virtual Machine Directed Approach to
Trusted Computing,” Proceedings of VM’04, San Jose,
2004, p. 3.
[15] M. Alam, X. W. Zhang, M. Nauman and T. Ali, “Behav-
ioral Attestation for Web Services (BA4WS),” Proceed-
ings of the 2008 ACM Workshop on Secure Web Services,