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Comparative Analysis of Secured Hash Algorithms for Blockchain Technology and Internet of Things

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(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 12, No. 3, 2021
Comparative Analysis of Secured Hash
Algorithms for Blockchain Technology and
Internet of Things
Monika Parmar1
1Chitkara University School of Engineering and
Technology, Chitkara University, Himachal
Pradesh. monika.parmar@chitkarauniversity.edu.in
Harsimran Jit Kaur2
2Chitkara University Institute of Engineering and
Technology, Chitkara University, Punjab.
harsimran.kaur@chitkara.edu.in
Abstract
Cryptography algorithms play a vital role in Information
Security and Management. To test the credibility,
reliability of metadata exchanged between the sender and
the recipient party of IoT applications different
algorithms must be used. The hashing is also used for
Electronic Signatures and based on how hard it is to hack
them, various algorithms have different safety protocols.
SHA-1, SHA-2, SHA3, MD4, and MD5, etc are still the
most accepted hash protocols. This article suggests the
relevance of hash functions and the comparative study of
different cryptographic techniques using blockchain
technology. Cloud storage is amongst the most daunting
issues, guaranteeing the confidentiality of encrypted data
on virtual computers. Several protection challenges exist
in the cloud, including encryption, integrity, and secrecy.
Different encryption strategies are seeking to solve these
problems of data protection to an immense degree. This
article will focus on the comparative analysis of the SHA
family and MD5 based on the speed of operation, its
security concerns, and the need of using the Secure Hash
Algorithm.
Keywords: Blockchain Technology, IoT, Secured Hash
Algorithms, IoT Security, SHA, MD5
I Introduction
The Internet of Things is a connecting network of
multiple things that are not only connected to one
other but are also connected to the Internet. The
basic services of IoT are rapidly increasing owing
to its enormous range of applications by providing
scalable solutions with lowered expenditure [1].
These scalable solutions always need fast and
efficient authorization, information protection,
confidentiality, intrusion responsiveness, fast
implementation, and self-maintenance. Through
implementing blockchain technology, certain
specifications can be provided to the IoT solution
of a business.
Blockchain is a program with a vast variety of
implementations, typically related to cryptography.
Besides that, it has subsequently been recently
implemented as a distributed and permanent ledger
that enables the phase of transfer registration and
consultation. one should think about transactions
happening in banking sectors as blockchain
network transactions as a hypothetical example [2].
These days, to transact currency, the individual
is dependent on banking and perhaps other
reputable financial institutions. The payment
respondents confirmed that the entity handling the
transfer has the requisite infrastructure to ensure
that it is conducted efficiently and, quite notably, in
a secure way. Besides that, as in the event of
unforeseen failure, these intermediate institutions
can collapse and therefore the faith is violated and
so will be the transactions and products entrusted to
them [3]. In distributed ledger technology, the
confidence element is taken into account through
the use of encrypted structures to include the
statistical evidence of the total transaction
performance. This testimony is unequivocally valid
that the members in a blockchain are equipped with
safety and integrity.
IoT systems can exchange data with others, to
improve the knowledge of all members of the
network and the surroundings. The IoT operation
consists of a mixture of Interconnection,
actuators, programmable controllers, and sensors
[4]. Methods of a certain level IoT is applied at a
quick speed with ideas such as smart homes, smart
cities, and wearable devices which map out their
characteristics prospective, and efficient usage.
Provided that blockchain is a hierarchical ledger
system and also the IoT framework is naturally
decentralized, it can be concluded that, in a real
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Vol. 12, No. 3, 2021
possibility, their synergy can be advantageous,
thereby adding to the protection and accountability
of IoT transactions. In view to improve the
effectiveness of applications, Blockchain uses a
technology in which computers consume large
quantities of resources and processing power. IoT,
on the contrary, is a network of objects that usually
have a comparatively fewer number of resources,
but it may even be of significant impact to merge
these solutions [5]. The goal of this study is to
explain the application of blockchain technologies
in IoT applications, and even the effect on
resource-constrained systems of many hash
functions. At first, as we seek to explain how the
system performs and the mechanisms involved, the
blockchain concept will be explored in specific.
This study investigated certain hashes methods that
have been submitted by academics, but the majority
of them have not been checked against blockchain
and IoT threats. Section II summarizes the
literature review of cryptographic hash functions in
blockchain technology. Section III introduces the
Blockchain technology and Cryptographic Hash
functions, Section IV addresses the potential threats
in blockchain and IoT, Blockchain Implementation
to IoT is depicted in Section V, Section VI analyzes
the proposed scheme for an effective hash function,
and Section VII comprises the result and
conclusion.
II Literature Review
Zeyad et. al. [6] suggested the Pros and Cons of
the optimization techniques and the impact on the
performance level by performing experimental
setup for SHAs by FPGA optimization methods.
B.P. Kosta et. al. [7] demonstrated a Strong and a
Secure lightweight cryptographic hash function is
proposed in which each 512-bit of a data is
compressed to 256-bit. Afterward, it is divided
further into 8 blocks having 32-bits each.
F. Pfautsch et. al. [8] validated the SHA-1 and
SHA-3 hash functions because of the brute force
threats on UltraScale+ FPGA dual-core systems.
They have evaluated the passwords with 6
characters in 3 minutes time span and because of
high complexity, the time raises by 5.5 for the
SHA-3 Hash Algorithm.
N. Khan et. al. [9] surveyed a thorough and in-
depth survey of traditional authentication and the
hash function is performed in this article,
supported by a reasonable contrast of the period
and computer processes usage of such
methodologies.
C. White et. al. [10] suggested Blockchain
technology and picture hashes are used to create an
image verification system. The concept developed
in this paper, however, needs to be refined, as it
tends to strive in some circumstances. This research
demonstrates whether blockchain can be used to
authenticate images, especially through picture
hashing. Other findings provide the fact that in
certain instances, utilizing adjacent frames hash
operations around the same time will enhance
efficiency, but that each type of cryptocurrency
experiment will have its own distinct set of data.
Table 1 and Table 2 summarizes the literature
review for the given context.
III Blockchain Technology and Cryptographic
Hash Functions
A Peer to Peer network may be a decentralized
computing model if any of its technical services,
such as computing power, space, and scanners, are
shared by its members. To provide the
infrastructure and information provided on the
platform, these common services are essential.
Blockchain is a distributed platform with no data
analysis resources and no users to order them [12].
A node, therefore, depicts a system member. Every
member has the authority to function as a server as
well as the client, leading to the absence of a
hierarchical system between them and providing
the identical function in all networks. A protection
scheme should be perceived when blockchain
technology is decentralized because, unlike a
centralized system where there is a single point of
failure, is not the case here and can be targeted,
thus it is tougher to interpret the information. This
characteristic, even then, is not adequate to
secure information passes through the system
security and reliability. Blockchain is based on
encryption to accomplish that. Generally, the
cryptographic hash functions are of various types
that provide different bit values depending on the
type of hash and the same is depicted in figure 1.
Typically, 2 cryptographic methods are used for the
blockchain framework: private and public key for
the hash functions. The public key, which confirms
the authenticity of whoever made the transfer, has
to be used for exchanges to be digitally
authenticated. It relies on a key to encipher and a
dissimilar key to decipher [13]. Two very different
keys are conceptually difficult to find,
understanding only the encryption techniques used
to produce them. This ensures the security and
authenticity of the information if somebody
confirms their transfer through its secret key since
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(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 12, No. 3, 2021
decrypting is only primarily driven by that of the
hash value that is public in nature.
Fig. 1. Types of Cryptographic Hash Function
When the decoding results are positive, clients
realize that the author of the secret key is someone
who validated the agreement, and the information
is not compromised or altered, else the decoding
will not be efficient. The conversion of some form
of data into a sequence of words is translated by
these mechanisms. The same knowledge will still
lead to almost the same key, and the slightest shift
in the source information will create a hash that
varies from the previous one. It's a minor
processing operation to create a hash, but the
reverse does not occur. It is virtually impossible to
execute the reverse process to retrieve the
actual data once the hash data is known [14]. As
soon as the new block is generated, these hash
functions are being utilized to confirm the block.
Each block is connected to the previous block with
the hash key and if someone wants to intrude in
between, the hash value will change and will no
longer be the same value in the blockchain. So
there the frauds can be detected. hen an intruder
happens to alter a block that is a member of a
blockchain, together with its key, its value will alter
in that way that this will not fit with the hash value
present over the upcoming block in the chain.
The SHA functions in the SHA family comprise
SHA-0, SHA-1, SHA-2, and SHA-3. While there
are functionally distinct ones from that very same
group. SHA-0 had several bugs and was not very
common So, SHA-1 was subsequently developed
in 1995 to fix suspected SHA-0 vulnerabilities. Of
the current SHA algorithms, SHA-1 might be the
most commonly used one for SSL authentication.
It has many variations in bits, for example, SHA-
224, SHA-256, SHA-384, and SHA-512. It is
based on the number of hash bits in the hash
function. However, SHA-2 is a good
cryptographic algorithm but it follows the same
architecture as SHA-1 [15]. NIST introduces
another algorithm that is Keecak algorithm
considered as the SHA-3 Hash function.
It presents various advantages, including efficient
quality and reasonable tolerance for threats.
However, SHA-2 is a good cryptographic
algorithm but it follows the same architecture as
SHA-1. NIST introduces another algorithm that is
Keecak algorithm considered as the SHA-3 Hash
function. It presents various advantages, including
efficient quality and reasonable tolerance for
threats.
IV Potential Threats in Blockchain and IoT
Each technology comes with its pros and cons so is
blockchain technology. Several threats that deal
with blockchain technology include double-
spending threats, threats involved in mining, threats
in wallets, threats based on the network, and threats
in the smart contracts. Each above mentioned has
many threats/attacks associated with it that can
have a significant impact on the blockchain
network and is shown in figure 2. Whenever a
network infrastructure is affected, a double-
spending threat can occur and virtual currency is
generally seized. To make it appear valid, the
hacker will indeed send a duplicate copy of the
currency or could expunge the transfer of funds
entirely. However, it is not widespread, double-
spending does happen. This type of threat includes
a 51% attack in which a node miner or team of
miners on a public ledger tries two times to invest
one's digital currency on that public ledger [16].
They are trying to invest twice in them; thus, the
title double-spending attack is given. This is not
always aimed at doubling crypto spending, but
almost always discrediting a particular crypto or
blockchain technology by influencing its
credibility.
It informs us that more successful clustering power
contributes to greater protection against a 51
percent attack while testing the Proof of Work
(PoW) algorithm [17]. However, small-
size blockchains that run on PoW could be slightly
more prone to this kind of attack, given that the
intruder does not cope with even more computing
power which is the reason that 51% of attacks tend
to happen on smaller blockchains whenever these
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(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 12, No. 3, 2021
occur in any way. The Bitcoin blockchain still
hasn't experienced a 51 percent intrusion yet.
Fig. 2. Threats at Blockchain Levels
Whenever an intruder makes two opposing
transfers, a race attack will be launched. The first-
ever transfer would be sent to the individual who,
instead of any wait for clarification of the transfer,
confirms the transfer (and delivers a service, for
example). At the same instance of time, a different
transfer is distributed to the server that returns the
equal amount of digital currency to the intruder,
ultimately rendering the very first transactions null.
A decentralized wallet that helps customers to
exchange and handle cryptocurrency, as well as
ether, is called a blockchain wallet. This wallet is
created by Blockchain which is an e-wallet that
helps users to manage and move bitcoins [18] A
pre-image threat on cryptographic operations in
hashing aims to locate a document that seems to
have a particular hash code. A hash of cryptography
can withstand threats upon the pre-image. Network
attacks include DDoS attacks, Sybil attacks,
Routing threats, etc. In general, a DDoS attack may
burden a network with new chunks of information
inside a network, which would compel a
blockchain to function slowly to use its computing
capacity. It is a Denial-of-Service intrusion and is a
tactic to interrupt connectivity to a network
interface or internet platform by normal nodes.
Usually, this is done by overburdening the endpoint
with a large amount of activity or by injecting fake
requests that enable the targeted system to fully fail
or collapse. Sybil attacks are prominent in P2P
systems where several nodes are successfully run
simultaneously by a network interface and
compromise the power in credibility schemes [19].
The primary purpose of this threat is to obtain the
bulk of the power in the systems to enable unlawful
acts in the framework. Such numerous false
profiles tend to be legitimate specific attributes for
the system. The absence of smart contract
technology requirements passes more of the
pressure to the organization as it opens its
connection details to possible damage. As when the
event reveals, the contract applied cannot reflect
the agreeing partners' real purpose. In IoT, some
architectural levels layers include the Physical
layer, Network layer, Middleware, and Application
layer [20]. On each layer of IoT, there are different
threats and are shown in figure 3.
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Double
Spending
Attack
Race Attack
51% Attack
Vector 76
Mining
Attack
Pool Hopping Attack
Sel'sh Attack
Block Withholding Attack
Wallet
Attack
Vulnerable Signature
Collision and Pre-image
Attack
Malware Attack
Network
Attack
DDoS Attack
Routing Attack
Delay Routing Attack
Sybil Attack
Eclipse Attack
Smart
Contract
Attack
Contract Source Code
Vulnerability
Blockchain Vulnerability
Low Level Attacks
(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 12, No. 3, 2021
Fig. 3. Security Threats at IoT Architectural
Levels
As IoT is growing at a rapid so its challenges
include security issues in many IoT applications, it
is cost and traffic, Increased load capacity on Cloud
Service and services insufficiently, Issues in
System infrastructure/Architecture, and
manipulating information [21]. Table 1 shows the
challenges towards IoT applications, various
attacks included, and the possible blockchain
solution for the same.
Table 1: Challenges of IoT applications and the Possible blockchain solution
Challenge Towards
IoT
Inclusion Attacks Specification Possible Blockchain Solution
Security Issues in IoT
Applications
• Node Capturing
IoT applications
are prone to
exposure to
personal
information.
For this issue, permission type blockchain
can be used that can enhance security[15].
• SQL Injection Attacks
• Man-In-Middle-Attack
• Data Thefts
• Sniffing Attacks
Cost and Traffic
• Phishing Site Attack
To handle
exponential growth
in IoT devices
It can be solved by the decentralization
feature of the blockchain. In this, central
servers are not being used as every node can
directly communicate to each other [27] [28]
[29].
• Booting Attacks
• Data Transit Attacks
• Routing Attacks
• Access Control Attack
Increased load capacity on
Cloud Service and services
insufficiently
• DDoS/DoS Attack
Owing to security
issues/threats or the
attacks on the
cloud, the services
from the cloud
discontinues
Each file is updated separately as a ledger
on every node/device on the network so
single point failure is not possible in such a
case [31].
• Firmware Updates
• Service Interruption
Attacks
• Flooding Attack in
Cloud
Issues in System
infrastructure/Architecture
• Side-Channel Attacks
Every section in
IoT are prone to
single point failure
and it affects the
systems and whole
infrastructure
Verification of data is done with the help of
encryption techniques utilizing the
blockchain[28].
• Eavesdropping and
Interferences
• Sleep Deprivation
Attacks
• Secure On-Boarding
• Extra Interfaces
• Reprogram Attacks
Manipulating Information • False Data Injection
Attack
Information is
deliberately taking
out from IoT units
and manipulating
the information
maliciously.
A blockchain ledger is updated at every node
so if there is any malicious node that updates
the information, other nodes will decline
that[30].
• Malicious Code
Injection Attack
• Access Attack
• Signature Wrapping
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Attack
• End-to-End encryption
V Blockchain Implementation to IoT
Today many IoT implementations rely on a
centralized server/client model, in which clients
link across the Network to services virtualized on
to the cloud. While these methods are feasible, as
IoT expands a new mechanism is required.
Decentralized alternatives have been suggested yet
Peer to Peer alone cannot assure security and
confidentiality [32]. Blockchain has the power to
respond to a number of the problems that come
from the use of IoT: IoT implementations are costly
because of the expense of central server
management in the cloud. To improve protection
and loyalty, accountability is important. An open-
source approach is desired and should be
considered in the development of the next version
of IoT products. Since IoT usually requires a
central agency, the central level failure problem is
prevalent. Factors such as time synchronization,
registries, anonymity, and reliability are tough to
control reliably [33]. IoT applications are
renowned for moderate computational power and
also energy efficiency. This system may not be able
to use the highest cryptographic algorithms since it
takes much longer to access. as per storage: is
concerned, all nodes hold a backup of all dealings
which has existed in the database since its
development. The scale would grow as time has
gone through or IoT devices might not even be
capable of storing it [34]. The problems of ledger
extended to IoT originate in its minimal
investment. Although the computing capacity is
limited, these machines can still execute activities
as long as protocols and frameworks designed for
them are utilized [35].
So, hash algorithms have to be checked thoroughly
for their performance level. A comparative analysis
of blockchain and IoT-based systems is being
presented in Table 2.
VI Proposed Scheme for Effective Hash
Function
In the proposed scheme, three levels of comparison
are being carried out that is based on the output size
bits of the hash algorithm, size of the file and time
to execute these files through a hash function, and
based on the speed performance of various hash
algorithms. Six different iterations are taken to
compare the time execution of hash algorithms. For
the six iterations, 2 major cases are being taken that
include a short sequence of data that is to be hashed
and a large sequence of data that is to be hashed
and the comparison is in between MD5, SHA-1,
SHA-256, and SHA-512. Figure 4 depicts the three
levels of comparison for the hash algorithm.
`
Fig. 4. Three levels of Comparison of Hash
Algorithms
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SHA-3 -512 RIPEMD-128
RIPEMD-160 RIPEMD-256
RIPEMD-320
+" &.
'&%'&9;7&<
Based on the output size (in bits), different hash
algorithms are analyzed. It is depicted in figure 5
that the more the number of hash bits, the higher
the security. So, from this, it is shown that SHA-
512 and SHA-256 have comparable output bits.
Fig. 5. Comparative Analysis of Hash Algorithm
based on Output Size(bits)
Table 2 – A Comparative analysis of an existing survey on Blockchain and IoT based Systems.
** represents covered partially, = represents covered in detail, and > represents not covered in the literature
Application
Criteria
Year of
publication
Major Inclusion Considered Factors Discussion on
Storage Issues
Discussion on
Security Issues
Blockchain-based
IoT applications
2019 [11] Overview of Opportunities and challenges of
IoT and Blockchain is provided
Interoperability
Security and privacy of IoT
** **
2018[12] Detailed discussion on blockchain techniques,
applications, and challenges
Consensus
algorithms
Security issues in
blockchain
✗ ✔
IoT storage
optimization
2017[13] A detailed analysis of optimizing the level of
performance in distributed storage onto the
cloud.
Improvement in transmission
efficiency.
Distributed cloud storage
The adaptive network coding
scheme
>
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2020[14] An in-depth approach for optimizing the data
access storage architecture in the Internet of
Things, in which factors of data access storage
distribution are fully considered, and secured
hashing is being used to configure the data for
storage optimization.
Data processing efficiency
Time consumption for reading
the files
File download efficiency
>
Blockchain-based
IoT storage
optimization
2017[15] A brief discussion on lightweight BC-based
architecture for IoT that virtually eliminates
the overheads of classic BC.
Block validation processing
time
PoW
BC-based smart home
**
2019[16] [26] An investigation about lightweight blockchain
management with a superior reduction in
resource usage and also save the significant
information about IoT framework.
WSN
CPS
PoS consensus mechanisms
Mobility based blockchain
management
>
Blockchain for IoT
security
2017[9] [23] [24] A comprehensive case study of smart home Security analysis
DDoS attack
Packet overhead
Energy consumption
>
2020[18] [25] Detailed insights of a software-defined
blockchain architecture to realize the
configurations for blockchains. Also, a
consensus function virtualization approach
with application-aware workflow is proposed.
Consensus algorithms
SDN
Throughput of transactions
Energy consumption
Consensus switch accuracy
>
Security issues of
IoT
2019 [17] [19] A comprehensive survey of security, issues,
challenges, and considerations of IoT
Physical attacks
Networks attacks
Software attacks
Encryption attacks
>
2020 [21] [22]
[36]
A discussion about security, privacy, and trust
in the Internet of Things
Secured middleware
Mobile security in IoT
Public key cryptography
(PKC)
>
Comparative
analysis of a
secured hash
algorithm for IoT
applications
This article Detailed insights about cryptographic hash
algorithms for Blockchain and IoT
Threats to IoT
Performance checks for
various cryptographic
algorithms
The practical applicability of
blockchain
Secured strategies
✔ ✔
Also, the file size for execution is an important
factor while deciding the secured hash algorithm.
For a file of size 1KB, 5Kb, and 10 KB, the time
taken for execution is depicted in figure 6 below.
So, for large-size files, SHA1 is taking less time as
compared to SHA2 and SHA3.
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Fig. 6. Comparative Analysis of Hash algorithms
based on File Size
Also, hash algorithms can be compared based on
their speed, and accordingly, a particular hash is
selected. In this 6 iteration were taken for the two
major cases and that includes a small sequence
having immutable universally unique identifier
string, immutable universally unique identifier
including system current time, and random
immutable universally unique identifier with
system current time and large sequence that will
include two immutable universally unique
identifiers, two immutable universally unique
identifier with current system time, and three
random immutables universally unique identifier
with current system time. The setup is implemented
in java with these six iterations and outcomes from
several samples are collated and evaluated. There
are 6 primary instances and are mentioned in table
3.
Table 3. Six Iterations Execution Time for Small and Large
Sequence
SMALL SEQUENCE
(ms)
LARGE SEQUENCE
(ms)
HASH
ALG
ORIT
HM
ITER
ATIO
N 1
ITER
ATIO
N 2
ITER
ATIO
N 3
ITER
ATIO
N 4
ITER
ATIO
N 5
ITER
ATIO
N
6
MD5 542 715 1425 798 892 1606
SHA-
1458 466 1146 601 716 1319
SHA-
256 513 492 1120 639 750 1339
SHA-
512 379 469 1172 593 750 1349
0 500 1000 1500 2000
,!$,!??4
MD5
SHA-1
SHA-256
SHA-512
4;.<
Fig. 7: Comparative Analysis of Speed
Performance for Hash Algorithms
From the above cases, it is being concluded and
shown in figure 7 that MD5 is faster in speed
response than SHA-1 with 29.57% for small
sequences and fasters 25.04% for large sequences.
Also, SHA-1 is slow as compared to SHA-256 with
2.59% for small sequences and a 3.37% slower use
level when selecting secured hash algorithm. MD5
is faster in speed response than SHA-1 with
29.57% for small sequences and fasters 25.04% for
large sequences. Also, SHA-1 is slow as compared
to SHA-256 with 2.59% for large sequences. SHA-
256 is 5.2% faster than SHA-512 for small and
faster than SHA-512 with 1.34% for large
sequences. Also, out of all, SHA-1 is the fastest
with 708.3 ms for small sequences and 909.3 ms
for long sequences. For future work the Hybrid
Cryptographic Hash Function could be suggested
for a security evolved approach which would
increase network consensus, however, the ledger
node's confidence in current IoT devices cannot be
guaranteed, and reaching a consensus would
consume a large number of wireless
communications.
VII Conclusion
Blockchain systems can supply IoT through a
distributed ledger system to exchange data in a
secure nature intimidating the centralized power
model that remains presently on IoT. In
cryptographic currencies, the Internet of Things,
chain management, financing, information
exchange, and other areas, Blockchain is broadly
adopted. In blockchain systems, although, there
seem to be safety issues of different extents. A
cryptographic hash is used to validate the
authenticity and validation of transmissions in a
variety of ways. MD5, SHA-1, SHA-2, and SHA-3
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have all become the industry norms. The majority
of them were discovered to be either usable or
inefficient in terms of time. This study investigated
certain hashes methods that have been submitted by
academics, but the majority of them have not been
checked against blockchain and IoT threats.
Therefore, hash performance plays a crucial role in
blockchain as well as in IoT. So, this paper focuses
on the different cryptographic hash algorithms and
it is conferred that it is indeed safe to limit MD5
and SHA-1 because they have been vulnerable and
not secured. However, if the performance
is considerably better than stable SHA-2 family for
a specific scenario and protection is not so
necessary, they can be selected. It is dependent on
the use level when selecting a secured hash
algorithm. SHA-1 is the fastest with 708.3 ms for
small sequences and 909.3 ms for long sequences.
References
[1] H. Suo, J. Wan, C. Zou, and J. Liu,
“Security in the internet of things: A
review,” Proc. - 2012 Int. Conf. Comput.
Sci. Electron. Eng. ICCSEE 2012, vol. 3,
no. March, pp. 648–651, 2012, doi:
10.1109/ICCSEE.2012.373.
[2] F. Lin et al., “Survey on blockchain for
internet of things,” J. Internet Serv. Inf.
Secur., vol. 9, no. 2, pp. 1–30, 2019, doi:
10.22667/JISIS.2019.05.31.001.
[3] T. M. Fernández-Caramés and P. Fraga-
Lamas, “A Review on the Use of
Blockchain for the Internet of Things,”
IEEE Access, vol. 6, no. c, pp. 32979–
33001, 2018, doi:
10.1109/ACCESS.2018.2842685.
[4] K. Biswas and A. B. Technology, “Securing
Smart Cities Using Blockchain
Technology,” 2016 IEEE 18th Int. Conf.
High Perform. Comput. Commun. IEEE
14th Int. Conf. Smart City; IEEE 2nd Int.
Conf. Data Sci. Syst., pp. 1392–1393, 2016,
doi: 10.1109/HPCC-SmartCity-
DSS.2016.0198.
[5] B. Liu, X. L. Yu, S. Chen, X. Xu, and L.
Zhu, “Blockchain Based Data Integrity
Service Framework for IoT Data,” 2017,
doi: 10.1109/ICWS.2017.54.
[6] M. Abu-elkheir, M. Hayajneh, and N. A.
Ali, “Data Management for the Internet of
Things: Design Primitives and Solution,”
pp. 15582–15612, 2013, doi:
10.3390/s131115582.
[7] Zeyad A. Al-Odat, Mazhar Ali, Assad
Abbas, and Samee U. Khan. 2020. Secure
Hash Algorithms and the Corresponding
FPGA Optimization Techniques. ACM
Comput. Surv. 53, 5, Article 97 (October
2020), 36 pages.
doi:https://doi.org/10.1145/3311724
[8] B.P Kosta, and P.S. Naidu " Design and
Implementation of a Strong and Secure
Lightweight Cryptographic Hash Algorithm
using Elliptic Curve Concept: SSLHA-160
",(IJACSA) International Journal of
Advanced Computer Science and
Applications, Vol. 12, No. 2, 2021.
[9] Pfautsch, Fr., Schubert, N., Orglmeister, C.,
Gebhart, M., Habermann, P. & Juurlink,
B., (2020). The Evolution of Secure Hash
Algorithms. PARS-Mitteilungen: Vol. 35,
Nr. 1. Berlin: Gesellschaft für Informatik
e.V., Fachgruppe PARS. (S. 5-15)
[10] N. Khan, N. Sakib, I. Jerin, S. Quader and
A. Chakrabarty, "Performance analysis of
security algorithms for IoT devices," 2017
IEEE Region 10 Humanitarian Technology
Conference (R10-HTC), Dhaka,
Bangladesh, 2017, pp. 130-133, doi:
10.1109/R10-HTC.2017.8288923.
[11] White, C., Paul, M. and Chakraborty, S.,
2020. A Practical Blockchain
Framework using Image Hashing for
Image Authentication. arXiv e-prints,
pp.arXiv-2004.
[12] L. Wan, D. Eyers, and H. Zhang,
“Evaluating the impact of network latency
on the safety of blockchain transactions,”
Proc. - 2019 2nd IEEE Int. Conf.
Blockchain, Blockchain 2019, pp. 194–201,
2019, doi:
10.1109/Blockchain.2019.00033.
[13] J. Li, J. Wu, and L. Chen, “Block-secure:
Blockchain based scheme for secure P2P
cloud storage,” Inf. Sci. (Ny)., vol. 465, pp.
219–231, 2018, doi:
10.1016/j.ins.2018.06.071.
[14] S. Huh, S. Cho, and S. Kim, “Managing
IoT devices using blockchain platform,”
10 | P a g e
www.ijacsa.thesai.org
(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 12, No. 3, 2021
Int. Conf. Adv. Commun. Technol. ICACT,
pp. 464–467, 2017, doi:
10.23919/ICACT.2017.7890132.
[15] A. Dorri, S. S. Kanhere, R. Jurdak, and P.
Gauravaram, “Blockchain for IoT security
and privacy: The case study of a smart
home,” 2017 IEEE Int. Conf. Pervasive
Comput. Commun. Work. PerCom Work.
2017, pp. 618–623, 2017, doi:
10.1109/PERCOMW.2017.7917634.
[16] K. Hossain and S. Roy, “A Data
Compression and Storage Optimization
Framework for IoT Sensor Data in Cloud
Storage,” 2018 21st Int. Conf. Comput. Inf.
Technol., pp. 1–6, 2018.
[17] W. Gao, W. G. Hatcher, and W. Yu, “A
survey of blockchain: Techniques,
applications, and challenges,” Proc. - Int.
Conf. Comput. Commun. Networks,
ICCCN, vol. 2018-July, no. i, 2018, doi:
10.1109/ICCCN.2018.8487348.
[18] T. Alam, “Blockchain and its Role in the
Internet of Things (IoT),” Int. J. Sci. Res.
Comput. Sci. Eng. Inf. Technol., no.
January 2019, pp. 151–157, 2019, doi:
10.32628/cseit195137.
[19] J. Li, Y. Liu, Z. Zhang, J. Ren, and N.
Zhao, “Towards Green IoT Networking:
Performance Optimization of Network
Coding Based Communication and
Reliable Storage,” IEEE Access, vol. 5, pp.
8780–8791, 2017, doi:
10.1109/ACCESS.2017.2706328.
[20] M. Wang and Q. Zhang, “Optimized data
storage algorithm of IoT based on cloud
computing in distributed system,” Comput.
Commun., vol. 157, no. February, pp. 124–
131, 2020, doi:
10.1016/j.comcom.2020.04.023.
[21] A. Dorri, S. S. Kanhere, and R. Jurdak,
“Towards an optimized blockchain for
IoT,” Proc. - 2017 IEEE/ACM 2nd Int.
Conf. Internet-of-Things Des.
Implementation, IoTDI 2017 (part CPS
Week), pp. 173–178, 2017, doi:
10.1145/3054977.3055003.
[22] A. R. Shahid, N. Pissinou, C. Staier, and R.
Kwan, “Sensor-Chain : A Lightweight
Scalable Blockchain Framework for
Internet of Things,” 2019 Int. Conf.
Internet Things IEEE Green Comput.
Commun. IEEE Cyber, Phys. Soc. Comput.
IEEE Smart Data, pp. 1154–1161, 2019,
doi:
10.1109/iThings/GreenCom/CPSCom/Sma
rtData.2019.00195.
[23] S. Sicari, A. Rizzardi, L. A. Grieco, and A.
Coen-Porisini, “Security, privacy and trust
in Internet of things: The road ahead,
Comput. Networks, vol. 76, pp. 146–164,
2015, doi: 10.1016/j.comnet.2014.11.008.
[24] J. Wu, M. Dong, K. Ota, J. Li, and W.
Yang, “Application-Aware Consensus
Management for Software-Defined
Intelligent Blockchain in IoT,” IEEE Netw.,
vol. 34, no. 1, pp. 69–75, 2020, doi:
10.1109/MNET.001.1900179.
[25] A. Gajbhiye and D. Sen, “Attacks and
Security Issues in IoT Communication : A
Survey,” pp. 1688–1693, 2020.
[26] F. Buccafurri, G. Lax, L. Musarella, and A.
Russo, “Ethereum transactions and smart
contracts among secure identities,” CEUR
Workshop Proc., vol. 2334, pp. 5–16, 2019.
[27] M. Sigwart, M. Borkowski, M. Peise, S.
Schulte, and S. Tai, “Blockchain-based data
provenance for the internet of things,”
ACM Int. Conf. Proceeding Ser., 2019, doi:
10.1145/3365871.3365886.
[28] G. Ayoade, V. Karande, L. Khan, and K.
Hamlen, “Decentralized IoT Data
Management Using BlockChain and
Trusted Execution Environment,” 2018
IEEE Int. Conf. Inf. Reuse Integr., pp. 15–
22, 2018, doi: 10.1109/IRI.2018.00011.
[29] D. Liu, J. Ni, C. Huang, X. Lin, and X.
Shen, “Secure and Efficient Distributed
Network Provenance for IoT: A
Blockchain-based Approach,” IEEE
Internet Things J., vol. 4662, no. c, pp. 1–1,
2020, doi: 10.1109/jiot.2020.2988481.
[30] K. Kumar, S. Kumar, O. Kaiwartya, Y. Cao,
J. Lloret, and N. Aslam, “Cross-layer
energy optimization for IoT environments:
Technical advances and opportunities,”
Energies, vol. 10, no. 12, 2017, doi:
10.3390/en10122073.
[31] H. Wang, Y. Wang, Z. Cao, Z. Li, and G.
Xiong, An Overview of Blockchain
Security, vol. 2. Springer Singapore, 2019.
[32] Y. Qian et al., “Towards decentralized IoT
security enhancement: A blockchain
approach,” Comput. Electr. Eng., vol. 72,
11 | P a g e
www.ijacsa.thesai.org
(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 12, No. 3, 2021
pp. 266–273, 2018, doi:
10.1016/j.compeleceng.2018.08.021.
[33] H. Kim, S. H. Kim, J. Y. Hwang, and C.
Seo, “Efficient privacy-preserving machine
learning for blockchain network,” IEEE
Access, vol. 7, no. September, pp. 136481–
136495, 2019, doi:
10.1109/ACCESS.2019.2940052.
[34] R. Yasaweerasinghelage, M. Staples, and I.
Weber, “Predicting Latency of Blockchain-
Based Systems Using Architectural
Modelling and Simulation,” Proc. - 2017
IEEE Int. Conf. Softw. Archit. ICSA 2017,
no. October, pp. 253–256, 2017, doi:
10.1109/ICSA.2017.22.
[35] Y. Xu and Y. Huang, “Segment blockchain:
A size reduced storage mechanism for
blockchain,” IEEE Access, vol. 8, pp.
17434–17441, 2020, doi:
10.1109/ACCESS.2020.2966464.
[36] J. Sengupta, S. Ruj, and S. Das Bit, “A
Comprehensive Survey on Attacks,
Security Issues and Blockchain Solutions
for IoT and IIoT,” J. Netw. Comput. Appl.,
vol. 149, p. 102481, 2020, doi:
10.1016/j.jnca.2019.102481.
12 | P a g e
www.ijacsa.thesai.org
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