Combating the enemy within – an elegant mathematical approach to insider threat eradication_whitepaper_1509

COMBATING THE ENEMY WITHIN – AN ELEGANT MATHEMATICAL APPROACH TO INSIDER THREAT ERADICATION PAGE 2 OF 19
Combating
the enemy
within – an
elegant
mathematical
approach to
insider threat
eradication
www.guardtime.com

Abstract
“In God we trust, all others we virus scan.”
-Author Unknown
The occurrence of insider threats in organizations is not a matter of ‘if’; it is a
matter of ‘when’. Insider attacks on organizations are continuously occurring and
in most cases involve simple exploitation of inadequate practices, policies and
procedures within the enterprise. The implications for government, financial ser-
vices, telecommunications and IT industry are profound. If we factor in the contin-
ued erosion of traditional layered security boundaries through the increased use
of mobile devices (BYOD) and portable storage devices it’s not hard to see that
mitigating insider threat is getting significantly harder.
There is no magic bullet to prevent or detect insider threats in an organization.
This article discusses how Keyless Signature Infrastructure (KSI) enables se-
curity professionals to mathematically prove the state of a network or computer
asset using hash tree-based real-time authentication schemes, and details some
use-cases where such a proof enables detection of malicious activity by privi-
leged users.
By integrating KSI into networks, irrespective of where an asset is transmitted or
stored, every component, configuration, and digital asset generated by humans
or machines can be tagged, tracked and located with real-time verification inde-
pendent of trusted administrators. KSI provides a truth-based system wherein the
need for trust can be completely eliminated.

OF 19COMBATING THE ENEMY WITHIN – AN ELEGANT MATHEMATICAL APPROACH TO INSIDER THREAT ERADICATION

Introduction to Insider Threats
Today, organizations where privileged users have access
to sensitive information employ traditional role based
access control or other established security policies to
restrict user actions to reduce the likelihood of sensitive
information being compromised. Current network moni-
toring tools are configured to look for explicit violations of
security policies, but data loss nevertheless occurs when
‘insiders’ intentionally violate policies before detection or
are able to remove evidence of their activities from logs
and other monitoring systems. Only reliable detection and
subsequent intervention can prevent them from wreaking
irreparable damage (e.g. the Edward Snowden and Brad-
ley Manning case). Such violations occur in many other
situations, including for example financial institutions (e.g.
Jerome Kerviel of SocGen). We also know that system
vulnerabilities can exist long after organizations are made
aware of them (Verizon’s 2015 DBIR reports that 99% of
exploited vulnerabilities were still compromised more than
a year after the CVE was published).
Every year, data loss from insider breaches costs enterprises
millions of dollars. Verizon’s 2015 DBIR shows that insider
threat is the third largest category of incidents at 20.6% with
55% of the incidents being related to privilege abuse.
Threat actors fall into three major categories:
• Unintentional and careless insider who accidentally
modifies/leaks data.
• Malicious insider who engages in activity with intent
of fraud, personal financial gain or grudge.
• Exploited insider who falls victim to manipulation
and/or trickery by another malicious entity inside the
organization – typically through social engineering.
• External/partner actors
Irrespective of the category, there are often significant
delays between the occurrence of malicious activity and its
detection using current technologies. (Reference #7)
Figure 1 Incident classification patterns - Reference 2015 DBIR
Figure 2 The defender-detection deficit - Reference 2015 DBIR

COMBATING THE ENEMY WITHIN – AN ELEGANT MATHEMATICAL APPROACH TO INSIDER THREAT ERADICATION

Insider threat -
the problem space
Since the widely publicized disclosures by Edward
Snowden, the issue of insider threat has been at the fore-
front for governments, corporations and security practi-
tioners. Organizations typically take a holistic approach to
the insider threat problem, with granular access controls,
continuous monitoring and data confidentiality using en-
cryption. However, these solutions can be expensive to
deploy and maintain, and do not address the fundamental
problem of integrity of the monitoring tools. If one can’t trust
the threat assessment provided to a Security Operations
Center (SOC) or a Network Operations Center (NOC), the
protection promised by these security solutions is funda-
mentally flawed.
Current insider threat detection/prevention tools and tech-
nologies typically use statistical driven assessments or a
series of hypothesized Boolean (if-then constructs) rules
to identify behavioral patterns, and to measure the likley
detection time of malicious insider actions on IT systems.
However, insider behavior is overwhelmingly dominated
by noise that obscures detection (i.e. this ‘noise’ normally
includes many actions that are not indicative of compliant
or non- compliant behavior). It rapidly becomes too time
consuming or even impossible to filter out irrelevant data.
Even when such rules would be effective, an insider with
sufficient privileges can often remove the evidence before
it is analyzed.
Although identifying and correlating potential indicators of
insider threat activity is fundamentally hard, Socio-techni-
cal approaches can provide a holistic analysis of various
threat indicators. Despite these approaches, findings from
insider threat studies across critical infrastructures show
that the majority of the attacks are only detected after there
was a noticeable irregularity in the system, or after the sys-
tem became unavailable.
An effective and complimentary approach to deal would be
to make the evidence of inappropriate usage immutable –
regardless of privilege. To do this, however, there is a need
for a technology that can provide evidence of unauthorized
changes that cannot be modified or changed without de-
tection. Keyless Signature Infrastructure (KSI) provides
such immutable information. KSI can be used to detect
the changed state of any digital asset, which can then be
investigated or audited. When coupled with correlation
and reporting tools this provides real-time awareness, data
loss prevention and network attribution, without the need
to trust privileged users.

Keyless
Signature
Infrastructure
What is KSI?
Keyless Signatures Infrastructure (KSI) is a data-centric
security technology based on cryptographic hash func-
tions and requires knowledge of only hash-values and bi-
nary trees. Every second, a federated and distributed bina-
ry tree is generated using hash-values of data generated
around the globe within that second. A hash tree is essen-
tially a binary tree of hash values. Two input values, along
with any other desired parameters, are concatenated and
run through a hash function. This process is iterated, re-
sulting in a single root hash value (Reference #6).
The word “keyless” means that signatures can be verified
without assuming continued secrecy of any keys. While
shared secrets may still be used for authenticating cli-
ents during the signature creation process, no keys are
needed for the signature verification itself. The integrity of
the signatures is protected using one-way, collision-free
hash functions. The collision probability of a typical cryp-
tographic hash function is very small, i.e. it is very hard to
find distinct inputs X ≠ X’ with h (X’)= h (X). This is normal-
ly referred to as ‘collision resistance’.
In the use of KSI, the root hash is calculated and “pub-
lished” in a distributed “calendar” database that every
customer (or subscriber) has a copy of. For every hash
value entered into the tree, there is a unique hash-chain,
or series of hash-values that allows the root hash-value to
be recreated. This hash chain is returned and stored as the
signature. A signature for a given digital asset identifies the
computation path, through the hash tree, from the asset’s
own hash value, up to the root calendar value. The signa-
ture also includes “sibling” values that were concatenated
at every step in the hash tree, which are necessary to rec-
reate the root hash. With access to the public “calendar”
database, anyone, anywhere, can receive data and verify
the signature, which includes indications of time, identity
and integrity, without reliance on a central trust authority.
How does KSI work?
The KSI infrastructure comprises four main components
- Cores, Aggregators, Verifiers and Gateways as shown
in Figure 3. The core cluster manages the calendar and se-
lects the top root hash for each second. The aggregation
network aggregates the hash values and distributes the
signatures. The verification network provides widely wit-
nessed access to the state of the calendar. KSI signatures
provide proof of signing entities, since parent aggregators
accept requests only from authenticated child aggregators.
The hierarchy of aggregation servers creates the global
hash tree for each round. Each aggregation server pro-
cesses requests from the servers below it, adds them to a
hash tree and sends the local root hash to the next high-
er-level server. The servers at each layer wait for respons-
es from the higher-level servers. The first layer of aggre-
gation servers are the gateways that are responsible for
collecting and processing requests from clients and then
sending the aggregate request to the upstream cluster.
This gateway is the customer-facing component of the in-
frastructure and delivers KSI service to the clients that pro-
vide KSI signing and verification services (Reference #2).
Requests are aggregated through multiple layers of ag-
gregator servers and the core cluster chooses the top
Figure 2: Calendar hash block chain

root hash. The core operates a distributed state machine
which sits at the top of the aggregation network and is
responsible for agreeing upon the top root hash for each
aggregation period, which it then stores in the calendar
database, returning the result to the aggregation network.
The regularly spaced rounds used in the aggregation and
core processes produce an accurate measure of time,
which is embedded into the KSI signature.
KSI – System Level
Considerations
In contrast to PKI, a KSI based solution is highly scalable,
since an increase in the number of signature requests
leads only to a distributed increase in the number of hash
computations during aggregation. Each aggregator only
forwards a single hash value each round, which means that
the upstream traffic remains constant, and the computa-
tional burden on the core remains the same. To expand the
network, one simply needs to add additional servers at the
lower levels, mostly as gateways, which are normally client
facing. These can be added (as needed) to without affect-
ing other servers or the core. By distributing the calendar
downward to aggregators, the burden of actually verifying
signatures is also distributed.
On an average, it takes one second for the clients to re-
ceive signature responses from the KSI infrastructure, and
much less than a second for verification.
How is KSI delivered to an end
user application?
Software Development Kits (SDKs) are required to integrate
KSI into End-User Applications. Clients who wish to digitally
sign objects using KSI use the client side KSI SDKs to com-
municate with a KSI gateway. The application presents the
data hash to the gateway, receives and must then store the
signature, and performs verification calls.
How is KSI used to sign digital
assets?
KSI can be used to protect any type of digital asset, such
as file system objects, virtual machine images, system con-
figuration files, access control files, log files, application and
firmware images, and many others.
KSI uses hash tree aggregation techniques. Every piece of
data in the ecosystem can be attributed back to a source,
whether human or machine. Figure 4 below shows a hash
tree computation. The owner (user) sends a hash of a
document (or other digital assets such as a log file) to be
signed – say x1. All received requests are aggregated into
a hash tree as shown below. Signatures comprise of data
for reconstructing a path from a leaf node to the top of the
tree. For verification the owner of x3 needs x4, x12 and
x58 to regenerate the root hash value to prove x3 partic
pated in the original computation (see Figure 4).
Figure 3: KSI Infrastructure
Figure 4: Hash tree aggregation example

How does the calendar
blockchain help?
The core cluster maintains the hash calendar. A new tree
(with new leaves) is built every second, and each leaf is re-
turned the hash chain to allow it to recreate the public hash
value. If a leaf node can recreate the root then the time, in-
tegrity and authenticity of the original data can be proven.
Only the root hashes are kept in the public calendar data-
base. The calendar block chain is a perpetual hash tree with
data only being appended to it (importantly existing nodes
are never removed or updated, in the same way an account-
ing ledger is maintained). The block chain has one leaf for
each second since 1970-01-01 00:00:00 UTC. The sign-
ing time is encoded in the form of the calendar hash chain.
The concatenation order bits encode the path from the root
to the leaf and prove the time offset of the leaf from the
publication time of the root hash value if the hash function
is pre-image resistant (i.e. for all pre-speciﬁed outputs it is
computationally infeasible to ﬁnd an input which hashes to
that output). No trusted time source is needed.
How can insider threat be
mitigated?
To mitigate the insider threat, an understanding of human
behavior and motivation as well as a review of critical assets
needs to be taken in conjunction with technical solutions.
This includes:
KNOWING THE PEOPLE
• Who would target your organization?
• Who are the high-risk individuals in your organization?
• Who has privileged access within your organizations?
• Which business partners pose a risk, based on
their access privileges?
KNOWING THE DATA
• What are the critical/sensitive assets in your system?
• Which assets are likely to be the targets?
• Are your systems logs (which track inbound/
• outbound data) integrity protected?
• Do the systems holding those assets have
vulnerabilities?
KSI offers a highly scalable data-centric security measure to
address the insider threat problem by protecting the critical
data assets in the system, both data at rest and potentially
in transit. It manages this using widely witnessed evidence
- via an industrial-strength block chain.
Figure 5: Hash chain signature publication

How does KSI
help solve the
insider threat
problem?
KSI provides a data-centric approach to deter, detect
and disrupt insider threat activities. If all critical assets are
signed with KSI then - in conjunction with policy enforce-
ment, and appropriate log instrumentation, monitoring and
auditing tools, abnormal behavior can be flagged imme-
diately to detect a potential breach, while attempts to re-
move evidence of such activities can be made impossible.
For example, unauthorized file copying can be quickly de-
tected using appropriate policy decisions on the kind of
events to log.
Data exfiltration can happen in so many different ways.
Insiders could print hard copy of the sensitive data and
hand-carry it outside the building, or data could be cop-
ied to a thumb drive. The underlying premise behind in-
sider threat detection tools is the interrogation of log file
state (where applicable and available) to identify suspi-
cious or anomalous data transfers, and proving that the
security controls in place cannot be subverted. Many data
exfiltration techniques exist which use data encoding and
manipulation to steal data, but if one can guarantee that
the log files, access control files, and other critical system
configuration files, applications (firewalls, AV systems etc.)
cannot be tampered with, then the evidence of these ac-
tivities can be fully trusted. An integrity failure (implying
tampering) can be detected and appropriate incident re-
sponse measures taken.
Example 1 –
Manipulation of log files:
KSI provides the capability to digitally timestamp and sign
any type of system event. Since KSI provides proof of his-
tory by aggregating hash values and linking them to time
(using rigorous mathematics), an insider can no longer
hide his tracks by tampering with the log files.
If the insider copied sensitive company IP and then tried to
delete/edit the log files to remove traces of their actions,
any software tool that is monitoring the KSI-stamped logs
would see a change alert resulting from a failed KSI sig-
nature verification. This event can be reported immediately
so that the security operations team can take appropriate
action quickly. In the absence of technology like KSI, the
logs would typically need to be examined manually/visually
to interpret changes/malicious events before any action
taken, often far too late.
Figure 6: Example of an Insider threat attack via log file manipulation

Example 2 – Replacing a
legitimate application with
malware:
Through a change in signature value, KSI can detect when
a malicious entity tries to embed information in otherwise
‘clean’ files (e.g. through steganography) or tries to replace
a legitimate system application with malware. It is also
possible to add KSI signatures on sensitive directories,
which will enable detection of rootkit or other malicious
file insertion.
If the insider tried to replace a legitimate system applica-
tion with a malware-ridden one, any software application
monitoring the KSI-stamped applications would see a
change alert resulting from the failed KSI signature veri-
fication on the application file. This change would be re-
ported immediately so that the IT team can take appropri-
ate action. In the absence of technology like KSI, some of
these malicious applications can go undetected, despite
use of AV software (for example – The Home Depot and
Target breach). It is also feasible to instrument KSI at the
OS level to ensure application binaries pass KSI signature
verification prior to execution.
Example 3 – Insider collusion:
Even if multiple insiders collude to carry out a malicious
operation, KSI can detect this activity, since events cannot
be deleted from log files (KSI signature change on the logs
causes an alert event). User identity information can be
included as part of the signature creation thus providing
the ability for an ‘innocent’ insider to prove they didn’t take
part in a malicious operation.
If for example several insiders colluded to replace a legit-
imate system application with a malware-ridden one and
then to delete traces of their actions from the system log
files, any software tool monitoring the KSI-stamped appli-
cations would see a change alert resulting from the failed
KSI signature verification on the application file and would
be reported immediately. By augmenting KSI with exter-
nal identity providers (e.g LDAP), the identity of the user
performing the action can be included in the signature,
innocent employees can prove it was not them who partic-
ipated in the malicious operation. In the absence of tech-
nology like KSI, most current deployments require manual
interpretation of system logs/monitoring information to de-
termine the identity of the malicious insider.
Note that KSI is complementary to AV detection; with KSI
detection being based on the change in an asset’s con-
tent, not on the presence of malware. A document master
(such as a contract, under strict revision control), might
be edited by a user with insufficient authority, either ma-
liciously or in error. The resulting change will trigger an
alert from a monitoring tool as explained previously. This
Figure 7: Example of an Insider threat attack using malware
Figure 8: Example of Insider Collusion

is particularly important in situations where access con-
trol is difficult to implement within an organization, and for
example in applications such as mergers and acquisitions,
or master spreadsheets used in financial services, where
strict document control is extremely important.
IN SUMMARY, KSI PROVIDES:
• Forensic quality – an immutable chain of custody
with independent proof of time, integrity and proof
that events occurred in the correct order while
ensuring no human interference with the data. This
is invaluable to forensic investigators because it pro-
vides mathematic certainty of the time and integrity
of those logs (Reference #5). It is even possible for
external auditors to verify everything that happens
to data independently from those who manage the
data, since the evidence is completely portable. In
the previous examples, the events from planning
to execution of the attack will all be time-stamped
which provides irrefutable forensic evidence of the
sequence of actions.
• Mutual auditability – the enterprise no longer has
to trust the service provider, the administrator, and/
or auditor to validate the integrity of the evidence.
Immutable evidence of authenticity, time, and identity
can be preserved for the lifecycle of any digital asset.
KSI provides independent proof because it does not
rely on humans or any central authority. In the pre-
vious examples, all that is needed is the data, hash
chain, and the root value, to prove that an insider
took part in a malicious action.
• Zero hour problem detection – On average, orga-
nizations take 229 days to detect a data breach,
according to a recent study from the cybersecurity
firm FireEye. (Reference #7)
• Any change to a KSI protected data store is instantly
detected through a change in the widely-witnessed
keyless signature. Thus, in the above examples, in
conjunction with analytic capabilities, KSI provides
a new class of information on which an alert can be
generated as soon as a malicious activity takes place
in the system/network.

How does a
KSI-enabled
solution differ
from other
insider threat
solutions?
There is no silver bullet for the insider threat problem, but
most current insider threat detection controls are woefully
inadequate for the job. This is primarily because they are
deployed with an external threat actor in mind, and main-
taining foolproof access controls to cope with both exter-
nal and internal users at scale is near impossible. “Trusted”
insiders are frequently able to circumvent the access con-
trols and other security mechanisms in place, and remove
evidence of their activities. To effectively address the in-
sider threat problem, the solution should be data centric,
scalable, and address the “human problem” while provid-
ing contextual intelligence and anomaly detection.
Encryption is widely used to provide confidentiality. How-
ever, without integrity, encryption brings a false sense of
security in cases where malware can be introduced into
systems, compromising the integrity of the system se-
curing sensitive data assets. KSI enables auditability and
transparency of evidence that in turn offers provable com-
pliance with regulatory and governance frameworks. By
focusing on the ‘state’ of data assets KSI goes beyond AV,
and can trigger alerts on any material change to content.
Most existing solutions (including log analysis and SIEM
solutions) promote the ability to continuously monitor threat
and risk profiles of people in an organization, while main-
taining white lists of approved applications. These solutions
are not effective unless the system can guarantee, irrefut-
ably, that the logs or applications have not been tampered
with, or there is a way to verify beyond doubt that your secu-
rity measures are working. KSI provides such a verification
mechanism for any digital asset. Any change to the asset is
detected rapidly via a verification failure of the KSI signature.
Some insider threat solutions rely on profiles of users/ap-
plications along with metadata, to compare live access be-
havior against these profiles. However, when a malicious
insider has escalated privileges, the challenge remains to
maintain the integrity of these profiles/metadata to ensure
such an insider doesn’t modify them. Log management
techniques are constantly evolving with log collection
and sophisticated event correlation being increasingly
combined to combat insider threat– indeed log files are
a significant data source for activity in the system. KSI
can provide integrity on sensitive files and logs. For further
protection and assurance of availability, KSI signatures can
be protected by an escrow service, with signatures moved
offsite to a tamper-proof location if required.
In the case of Target and Home depot, the hackers used
stolen credentials to install custom-built malware (that was
designed to evade AV) that then stole sensitive customer
information. If the system-critical applications had been
stamped with KSI signatures, any change to the baseline
could have been detected via a KSI signature change alert
KSI HIGHLIGHTS:
• Complements encryption solutions – encryption
doesn’t mitigate insider threat manipulation of assets
– having integrity protection using KSI (so their tracks
cannot be covered) does. Knowing that they will be
caught may also deter wrongful insider activity.
• Open/offline verification – the KSI calendar informa-
tion can be downloaded by clients, and hence clients
can perform verification based on only publicly avail-
able (‘widely-witnessed’) information - without need
for network connectivity.
• No single point of failure - since the core and aggre-
gation network are fully distributed systems.
• Quantum Immunity – organizations worldwide
typically rely on PKI for authentication and secure
communications. KSI is quantum immune i.e. keyless
signatures are resistant to quantum computational
attacks, unlike traditional public key cryptosystems
like RSA - since they are purely based on cryp-
tographic hash functions that are second pre-image

resistant (Reference #3, #4).
• Mishandling of secrets - unlike traditional public key
infrastructure (PKI) signatures, KSI does not require
the use of secrets to sign objects or assets. Hence a
malicious insider cannot misuse any secrets to hide
their tracks.
• The hash chain - contains the information needed to
regenerate the root hash value from a given leaf of
the tree. The hash chain proves that the input value
was part of the original set the tree was built upon.
Thus, KSI provides proof of participation of each
node in any given hash chain.
• Immutable - the global fingerprint is published
electronically every second and also in the world’s
physical media. Since the publication code (against
which the KSI signatures are verified) cannot be tam-
pered with, this ensures that any attempt by rogue
administrators to manipulate data can be detected
quickly (since it will result in a KSI signature verifica-
tion failure).
Other KSI Use Cases
• Cloud Security and Forensics - KSI enables re-
al-time authentication against tampering of log files
and Virtual Machine images, prior to deployment in
the Cloud, thus offering forensic evidence as to the
expected state of these assets.
• Storage Integrity – KSI offers independent authenti-
cation of data stores
• Executable Integrity - KSI offers a mechanism for
protection against tampering of executable files
• Unauthorized Change Control - KSI offers a mech-
anism for protection against document changes to
sensitive documents and files
• Connected Cars - KSI can be used to digitally time
stamp application and software files to ensure they
are not tampered with. Signing of audit and log files
ensures a malicious entity cannot cover their tracks.
• Secure Provenance - KSI offers a means to cryp-
tographically verify ownership of a file/digital object
in a way that it cannot be denied by the party modify-
ing the object.
• Enterprise SOC/NOC - KSI instrumented in a se-
curity operations center can be used in conjunction
with SIEMs and other reporting solutions to monitor
critical digital assets.
Figure 10: Proving participation using hash chain

Conclusion
The application of KSI will materially improve enterprise
environments for controlling insider threat and by provid-
ing a real deterrent. A potentially malicious insider in a KSI
controlled environment will quickly realize that they cannot
cover their tracks, and that their activities will be detected
and responded to swiftly. This knowledge will also signifi-
cantly reduce the efficacy of social engineering attempts
on peers. Advanced dashboards can be built to extract
KSI attributed information from the system and promote
custom integration with legacy SIEMs.
KSI-based detection and attribution is widely witnessed
owing to the calendar publication and hence it is possible
for malicious activity to be provably detected and commu-
nicated before the insider even leaves the premises; thus
deterring others from attempting similar acts. The ability to
rapidly extract chain of custody evidence without exposing
critical information from the digital assets under KSI-pro-
tection makes same-day forensic responses now possible.
As enterprises further utilize cloud services, KSI helps
them stay one step ahead of malicious insiders. Insider
threats start with the propensity of a an insider to engage
in malicious acts, followed by a planning and execution
phase. KSI deters insiders who have a propensity to en-
gage in malicious acts, since it provides digital timestamps
that cannot be forged. All critical components in the net-
work are essentially attributable, and the evidence of inter-
actions between users and these assets immutable. With
KSI, you can continue to trust your administrators and us-
ers, but more importantly you can now independently verify
their actions.
100% crime prevention is impossible, however it is now pos-
sible to have 100% detection, accountability and auditability,
and across highly complex systems. Whilst an effective solu-
tion to insider threat has so far proved elusive, KSI now of-
fers a truly scalable solution based on mathematical certain-
ty. Where human motivation and behavior must to be verified
in conjunction with effective security controls - think KSI.
References
1. 2015 Data Breach Incident Report - http://www.verizo-
nenterprise.com/DBIR/2015/
2. Ahto Buldas, Andres Kroonmaa, and Risto Laanoja:
Keyless Signatures’ Infrastructure – How to Build Global
Distributed Hash-Trees, Cryptology ePrint Archive –Re-
port 2013/834, https://eprint.iacr.org/2013/834.pdf
3. Ahto Buldas, Risto Laanoja, AhtoTruu: Efficient Quan-
tum-Immune Keyless Signatures with Identity, Cryptolo-
gy ePrint Archive- Report 2014/321, https://eprint.iacr.
org/2014/321
4. Ahto Buldas, Risto Laanoja, AhtoTruu: Efficient Imple-
mentation of Keyless Signatures with Hash Sequence
Authentication, Cryptology ePrint Archive – Report
2014/689, https://eprint.iacr.org/2014/689
5. Ahto Buldas, AhtoTruu, Risto Laanoja, Rainer Gerhards:
Efficient Record-Level Keyless Signatures for Audit Logs,
Cryptology ePrint Archive- Report 2014/552, https://
eprint.iacr.org/2014/552
6. http://en.wikipedia.org/wiki/Hash_calendar
7. https://www2.fireeye.com/rs/fireye/images/rpt-m-
trends-2015.pdf

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