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Digital Object Identifier 10.1109/ACCESS.2017.DOI
Machine Learning adoption in
Blockchain-based Smart Applications:
The challenges, and a way forward
SUDEEP TANWAR1, QASIM BHATIA1, PRUTHVI PATEL1, APARNA KUMARI1, PRADEEP
KUMAR SINGH2, WEI-CHIANG HONG3, SENIOR MEMBER, IEEE
1Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India (e-mails:
sudeep.tanwar@nirmauni.ac.in, 16bce059@nirmauni.ac.in, 16bce055@nirmauni.ac.in, 17ftphde22@nirmauni.ac.in)
2Department of Computer Science & Engineering, Jaypee University of Information Technology, Waknaghat, Solan (H.P.) India (e-mail:
pradeep_84cs@yahoo.com)
3Department of Information Management, Oriental Institute of Technology, Panchiao, New Taipei, Taiwan. (email: samuelsonhong@gmail.com)
Corresponding author: Wei-Chiang Hong (e-mail: samuelsonhong@gmail.com).
ABSTRACT In recent years, the emergence of blockchain technology (BT) has become a unique, most
disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy.
Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new
security issues such as majority attack and double-spending. To handle the aforementioned issues, data
analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen
technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions.
Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination
of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a
detailed study on ML adoption for making BT-based smart applications more resilient against attacks.
There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering,
bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long
short-term memory (LSTM) can be used to analyses the attacks on a blockchain-based network. Further, we
include how both the technologies can be combined in several smart applications such as Unmanned Aerial
Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges
are explored. At last, a case study is presented with a detailed conclusion.
INDEX TERMS Blockchain, Machine Learning, Smart Grid, Data Security and Privacy, Data Analytics,
Smart Applications
I. INTRODUCTION
FROM the past few decades, data has become an essential
source of intelligence and carries new opportunities
to the real-life problems such as wireless communications,
bioinformatics [1], agriculture [2], and finance [3] through
smart applications. These applications are data-driven and
incorporate actionable insights into user experience, which
enables individuals to complete the desired task more effi-
ciently. It operationalizes insights, personalizes the customer
experience, optimizes customer interactions, improves op-
erational efficiency, and enable new business model. There
are various smart applications such as SG, UAV, Smart
Cities, which makes the life of an individual easier. These
applications generate a huge amount of data, and storage
of this ever-evolving data in databases is a problem, and
its communication also raises security concerns. To handle
these issues, BT can be used, which has a distributed database
network. It was coined by Satoshi Nakamoto in the year 2008
and contained a time-stamped series of tamper-proof records,
which are managed by a cluster of distributed computers.
It comprises of a chain of blocks that are connected using
cryptographic primitives. The three mainstays of BT are
immutability, decentralization, and transparency. These three
characteristics opened its door for a wide variety of applica-
tions, for example, digital currency existence (currency with
no physical existence) and analysis on the suitability of it
in smart applications [4]. Although BT ensures security and
privacy issues, some vulnerabilities also started appearing
after its implementation. For instance, the nature of attacks
began to be increasingly sophisticated such as majority
VOLUME 4, 2016 1
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Tanwar et al.: Machine Learning adoption in Blockchain-based Smart Applications: The challenges, and a way forward
attacks (51% attack) that control voting, Sybil attacks for fake
identity generation to control the consensus [5]. To handle the
aforementioned issue, a robust Intrusion Detection System
(IDS) is required in place because the traditional methods use
a signature-based approach to detect specific patterns. But,
to detect intrusions and attack patterns, one of the emerging
technology known as ML can be used to analyze the data
traffic. Thus, designing efficient and effective algorithms
to analyze this massive amount of data is in dire need of
handling the blockchain-based smart applications. Hence,
ML is highly prevalent today and uses a dozen times a
day without even realizing it. ML encompasses computers
to study, think, and act without intervention of humans.
It is considered to be one of the applications of Artificial
Intelligence (AI). ML provides computers the competency to
learn without being programmed it explicitly. Its basic idea
is to build an efficient algorithm that can accept input data
and, with the help of statistical analysis, make a prediction,
and update the outputs. A substantial amount of data can be
analyzed by ML to create data-driven decisions.
In a communication network of blockchain-based smart
applications, there is layer-wise handling of security issues.
Some security issues are handled at the network layer, such as
malicious packets and some at the application layer such as
malware [6]. At the network layer, malicious packets can be
used to impose the network to establish fraudulent consensus.
A naive solution to this problem can be to use a firewall
to ensure that packets meet pre-defined security criteria [7].
Though, the attacks are becoming more sophisticated with
unseen patterns to bypass a firewall. To prevent this issue,
packets header data can be analyzed using ML models [8] in
real-time using historical data. This analysis helps to detect
new and changing patterns. Similarly, ML techniques can
be used to classify malware to end-point such as servers,
mobile, or workstations. Further, several blockchain-based
smart applications such as UAV [9], Data Trading [10], SG
build trust between data exchangers [11]. It is very crucial
in any smart application at the same time; data should be
secure. BT ensures data security but to build confidence,
and ML techniques are used to predict untrustworthy nodes
based on past patterns. Similarly, UAVs have significantly
different network topology compared to the conventional
blockchain network topology [9]. It includes communication
using satellites and various ground stations. For UAV, BT is
used to securely store coordinates and other relevant data to
maintaining graph integrity for the vehicles. In subsequent
sections, we explore the recent research work on ML adop-
tion in the blockchain-based smart application.
A. SCOPE OF THIS SURVEY
Several survey work has been published till date by the
different researchers on the various aspects of the adoption
of ML in blockchain-based smart applications [5]. As per
our knowledge, most of the surveys have focused on specific
areas, fields, or applications where it requires both ML and
blockchain. The proposed survey covers all the fundamental
aspects of ML to be applied BT based applications, for
instance, intrusion detection. A review conducted by Meng
et al. [12] describes how blockchain help to meet intrusion
detection. The authors mitigate trust issues by establishing a
collaborative IDS using smart contracts [17]. Further, Conti
et al. [13] surveyed on security and privacy issues of bitcoin.
They discuss various categories of attacks, such as double-
spending attacks, client-side security threats, and mining
pool attacks. To handle the aforementioned issue, Rahouti et
al. [5] discuss specific ML-based solutions and discuss the
handling of specific social issues such as human trafficking
and drug sales through cryptocurrency. Then, Ucci et al [14]
explored malware analysis using ML techniques. Features of
malware were thoroughly discussed, and a detailed taxonomy
has been proposed. Salah et al. [15] and Casino et al. [16]
conducted a review on blockchain-based applications. To
clarify the main difference between other surveys paper and
this survey paper, a comprehensive comparison has been
shown in Table 1. It includes objectives, merits, and demerits
of peer surveys concerning numerous parameters such as
architecture, applications, open issues, taxonomy, and merits,
demerits of the existing approaches. A master taxonomy of
ML for BT is summarized in Figure 3.
B. RESEARCH CONTRIBUTIONS
Though several research works exist to address the various
ML usages for the blockchain-based system but It has not
been exploited to its full potential. In this paper, we investi-
gated the ML usages for BT. The following are the research
contribution of this paper.
•A brief discussion on how ML and blockchain can be
used together with a proposed architecture diagram.
•To develop taxonomy covering ML techniques required
for BT based environment. In each part of the taxonomy,
existing work has been discussed in detailed to handle
several issues, such as preventing and predicting attacks
on the blockchain network.
•A case study is presented to demonstrate the ML tech-
niques for blockchain-based smart applications, for ex-
ample, SG.
C. ORGANIZATION
The rest of the paper organized as follows: Section 2 provides
a background of Blockchain, ML, and list all benefits while
applying ML for BT based application. Then, we proposed an
architecture. Survey procedures and taxonomy presented in
Section 3. Then, in Section 4-7, we present existing advance-
ments in ML approaches for BT-based smart applications.
A discussion on future research issues and challenges is
presented in Section 8. Further, section 9 covers a case study
on the SG system, and finally, we concluded the paper.
II. BACKGROUND
2VOLUME 4, 2016
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Tanwar et al.: Machine Learning adoption in Blockchain-based Smart Applications: The challenges, and a way forward
TABLE 1: Comparison of the existing surveys with the proposed survey.
Authors Year Objectives of Survey Merits Demerits 1 2 3 4 5
Meng et al. [12] 2018 To present use of blockchain in in-
trusion detection
Scope of application of blockchain
was discussed
Discusses only data sharing and
trust management issues of collab-
orative intrusion detection
XXXXX
Conti et al. [13] 2018 To discuss various security and pri-
vacy issues in bitcoin
A Comprehensive review of possi-
ble attacks on bitcoin and provided
countermeasures
Blockchain issues are not high-
lighted
XXXXX
Rahouti et al. [5] 2018 Survey on ML security solutions for
bitcoin
In-depth and wide classification of
major threats and extensive expla-
nation of the role of ML
Other applications of blockchain
are missing
XXXXX
Ucci et al. [14] 2018 To study ML techniques for mal-
ware analysis
Time and space complexity for var-
ious methodologies has been de-
scribed in detail
Lacks discussion on uses of these
techniques in a blockchain environ-
ment
XXXXX
Salah et al. [15] 2019 Discuss applications, platforms,
and protocols in blockchain
specifically for AI
The decentralization feature of
blockchain is explained with a spe-
cific view of AI
Discussion on privacy is not cov-
ered in detail
XXXXX
Casino et al. [16] 2019 Review blockchain-based applica-
tions and identify open issues
Prerequisites for blockchain appli-
cations are thoroughly discussed
Focused on applications, not the
open issues
XXXXX
Proposed Survey - To survey how ML can be used
in blockchain-based smart applica-
tions
Discusses architecture and technol-
ogy at a fundamental level and
bridges the gap between two tech-
nology
-XXXXX
Parameters- 1:Architecture, 2:Application, 3:Open Issues and Challenges, 4:Taxonomy, 5:Security
Notations- X: considered, and X: not considered.
A. MACHINE LEARNING
ML is the field of study that focuses on building applications
that learn through experience. It is the ability to teach a
computer without programming it explicitly [18]. ML en-
compasses its work from a diverse set of disciplines, includ-
ing philosophy, information theory, probability and statistics,
control theory, psychology and neurobiology, computational
complexity, and artificial intelligence [19]. ML algorithms
are used in many application and benefited it as listed below:
•In Data mining, large databases contain different pat-
terns that can be discovered automatically by using ML
techniques to analyze outcomes, for instance, medical
treatments of a patient from health record databases
or to identify the creditworthiness of a person from
financial databases.
•ML applies in areas where a deterministic algorithm
is not promising, such as human face recognition from
images.
•Application domains where the adaptable programming
is required, for instance, controlling manufacturing pro-
cesses as per the demand of the customer and adapting
to the varying reading interests of readers.
ML algorithm eare application specific and depends on the
output required by the system. There are several ML algo-
rithms, such as Supervised ML, Semi-Supervised ML, and
Unsupervised ML. (i) Supervised ML uses statistical models
to predict output in numerical data and classify the correct la-
bel [20]. Here, the most commonly known algorithms include
the regression approach and decision trees. (ii) Unsupervised
ML does not have label data. Here, data samples are grouped
into clusters depending on their similarity or dissimilarity
[21] using a different approach. For example, K means clus-
tering and association rules algorithms. (iii) Semi-Supervised
ML is also a category of interest [22]. It involves a mixture of
supervised and unsupervised ML techniques. Unsupervised
learning may be applied to discover the structure of input
variables, following which it is used to make best guess
predictions for the unlabeled data. It feeds that predicted data
back into the supervised ML algorithm as training data and
use the model to make predictions on unseen data.
B. BLOCKCHAIN
Blockchains are an immutable set of records that are cryp-
tographically linked together for audit [23]. It is similar to
an accounting ledger. Here, previous records in accounting
ledger cannot be changed, and new records need to be veri-
fied by a trusted party. The only difference between these two
is that new blocks (set of records) checked by a decentralized
structure of nodes that have a copy of the ledger. There
is no centralized party to verify the records. Blockchain is
formed by linking valid blocks together; the current block
contains the hash of the previous block, and so on, as shown
in FIGURE 1. This makes blockchain traceable and resistant
to change [24]. Older blocks cannot be modified, in case
they are changed in any way; their hash would change. This
emphasis to link hash in all subsequent blocks to make the
blockchain network valid again. A copy of the blockchain is
available with every individual within the network; hence-
forth, any changes can be cross verified by the other users.
These copies of the blockchain are updated with the addition
of a new block. Then, everyone can see the block, depending
on the permissions assigned by the administrator. BT uses
a cryptographic secure hash algorithm (SHA) such as SHA-
256 and SHA-512 to maintain the data integrity within the
block. Each block has a unique hash value. For instance,
Ethereum uses Keccak-256 and Keccak-512, while Bitcoin
uses double SHA-256. This SHA is a collision-resistant al-
gorithm, where no two different input data could produce the
same output (hash value). Henceforward SHA can be used to
check if the data is the same or not. There are various SHA
algorithms developed by the NSA and NIST and belongs
to the SHA-2 family [25]. SHA was initially envisioned
VOLUME 4, 2016 3
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Tanwar et al.: Machine Learning adoption in Blockchain-based Smart Applications: The challenges, and a way forward
Block Hash
Difficulty
Previous Block Hash
Block Header
Nonce
Timestamp
Merkle Root
Transaction Hashes
Block Hash
Difficulty
Previous Block Hash
Block Header
Nonce
Timestamp
Merkle Root
Transaction Hashes
Block Hash
Difficulty
Previous Block Hash
Block Header
Nonce
Timestamp
Merkle Root
Transaction Hashes
- - - - - - - - - - - - - -
- - - - - - - - - - - - -
Block-N
Block-2
Block-1
FIGURE 1: Blockchain Structure
as a fragment of the Digital Signature Standard (DSS) to
produce the signature. Further, consensus algorithms are
used to determine block validity. The selection of algorithms
depends on the type of blockchain, such as public, private,
and consortium blockchain. The selected algorithm should
ensure the consensus among nodes [26]. It must be able to
use resources efficiently and tolerate a degree of safety during
the event of attacks.
Further, a smart contract is a program (set of codes) that
runs on the blockchain and adds blocks whenever certain
conditions are met. It is defined by translating actual legal
contracts into programs to enforce the legal contract onto
the blockchain [27]. It is similar to stored-procedures in
relational databases. It is stored as scripts in the blockchain
and executed according to the data fed to them to produce
outputs that are expected from the original contract [28]. It
governs transactions either executed fully or partially based
on the current input. The primary purpose of a smart contract
is to provide superior security with a reduction in cost and
delays associated with traditional contracts.
C. INTEGRATION OF MACHINE LEARNING IN
BLOCKCHAIN-BASED APPLICATIONS
The learning capabilities of ML can be applied to blockchains
based applications to make them smarter. By using ML
security of the distributed ledger may be improved. ML may
also be used to enhance the time taken to reach consensus
by building better data sharing routes. Further, it creates an
opportunity to build better models by taking advantage of the
decentralized architecture of BT. We proposed architecture
for ML adoption in BT-based smart application, as shown in
Figure 2. Here, the smart application collects data from dif-
ferent data sources such as sensors, smart devices, and Intenet
of Things (IoT) devices. Data collected from these devices
get processed as part of smart applications. The blockchain
work as an integral part of these smart applications. Then,
ML can be applied to these application’s data for analysis
(Data analytics and real-time analytics) and prediction. The
data sets used by ML models could be stored on a blockchain
network. This reduces errors in the data such as duplica-
tion, missing data value, errors, and noise. Blockchains are
focused on the data, and hence data-related issues in ML
FIGURE 2: Proposed architecture for Machine Learning
adoption in Blockchain-based Smart applications
models may be eliminated. ML-models can be based on
specific segments of the chain rather than the entire data-
set. This could give custom models for different applications,
such as fraud detection and identity theft detection. Few of
the benefit are listed beneath when ML is applied:
•User authentication as a legitimate user for requesting or
performing any transaction in the blockchain network.
•BT provides a high level of security and trust.
•Blockchain integrates public ML models into smart
contracts to ensure that the conditions and terms which
were previously agreed are sustained.
•BT helps in the reliable implementation of an incentive-
based system; thus, it encourages users/customers to
contribute data. This huge data will help to improve ML
model performance.
•ML models can be updated on-chain environment of BT
with a small fee and off-chain, locally on an individu-
alâ ˘
A´
Zs device without any costs
•Good data contributions can happen from users/customers,
these data consistently computed, and rewards can be
given to the users.
•Tamper-proof smart contracts can be evaluated by dif-
ferent machines (having different hardware configura-
tion), ML models will not diverge from their potential
and produce results exactly as it is supposed to do.
•Payments processed in real-time with trust on a
blockchain environment.
•Blockchains tools, for instance, Ethereum, deal with
thousands of decentralized machines all over the world.
This guaranteed users that it is never completely un-
reachable or offline.
4VOLUME 4, 2016
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Tanwar et al.: Machine Learning adoption in Blockchain-based Smart Applications: The challenges, and a way forward
III. SURVEY PROCEDURES AND TAXONOMY
In this section, we refer to the procedures used to perform this
study; for example, our search approach and inclusion criteria
for the final set of papers. Likewise, we present a detailed
taxonomy based on our literature review.
A. SYSTEMATIC LITERATURE REVIEW
We used standard databases (for example IEEEXplore, ACM
Digital Library, ScienceDirect, and Springerlink) and Google
Scholar to search existing research work, using keywords
such as â ˘
AIJMachine Learning for Blockchain-based Smart
Application,â ˘
A˙
I (â ˘
AIJBlockchain for Smart Applicationsâ ˘
A˙
I
AND â ˘
AIJMachine Learning for Smart applicationsâ ˘
A˙
I ).
In the initial phase, publications emphasis essentially on
blockchain approaches for smart applications. Then, in the
next phase of the search, we concentrated on an ML-based
solution for blockchain-based smart applications. Based on
the results of these searches, we removed duplicate articles
and obtained our first set of more than 350 publications.
Further, we steered the search procedure with several maga-
zines, journals, and conferences dedicated to the parent field,
ML, smart applications, and Blockchain. Based on that, we
found 130 articles. Then, we studied the different sections
of the articles like abstract, conclusion, and introduction.
Then we categorized these articles as â ˘
AIJrelevantâ ˘
A˙
I or
non-relevantâ ˘
A˙
I to ML for blockchain-based smart applica-
tions. Lastly, we only selected 60 publications to present the
taxonomy, as shown in Figure 3. Here, each layer is color-
coded to tailor each level of the taxonomy. For example, the
root represents the level-0 of the classified taxonomy and
presented in blue color. In a similar way, the four major
dimensions are representing as level-1 in light-green color
and so onwards.
B. TAXONOMY
In this paper, we studied the papers that are predominantly
based on either ML, blockchain, or both. The presented
survey has been divided into four dimensions: Goal Oriented,
Layer Oriented, Countermeasures, and Smart Applications.
Figure 3shows the taxonomy of ML adoption for blockchain-
based smart applications.
IV. GOAL ORIENTED
In this dimension, research work has been included from the
following aspects: (i) preventing, (ii) predicting, (iii) mon-
itoring, (iv) detection, and (v) response of blockchain. For
instance, [29] discussed the detection of intrusion in a collab-
orative manner, [30] predicts bitcoin prices using Bayesian
regression method and [31] monitors the blockchain network
in a distributed manner.
A. DETECTION
This subsection includes the detection of the attacks to
handle data security issues. Meng et al. [12] has proposed
a Collaborative IDS (CIDS) with a data-sharing agreement
on a blockchain-based environment. Data privacy issues of
CIDS can be addressed with the use of ML classifiers on
BT. These classifiers run locally by the data owner, and
the results shared with other users within the network. To
handle the trust computation issue in CIDS, Alexopoulos
et al. [29] proposed an approach to handle the alerts and
data distribution among the participants. Then, it verifies the
data and executes a suitable consensus algorithm to add a
new data block within the blockchain network. These data
alerts could be encrypted with keys distributed to selected
parties. Another approach was to keep them on a separate
blockchain while still being part of the network. Further, an
anomaly detection system (ADS) was implemented based on
the assumption that a similar kind of attack may occur, but on
different nodes in the blockchain network [32]. This system
does not discard information about orphans and forks that
are usually done by other ADS. The attacked nodes shared
this information with other neighborâ ˘
A´
Zs node within the
network. In the experimental analysis, the system success-
fully prevented the same kind of attack (on other nodes) with
negligible overhead.
B. MONITORING
A blockchain monitoring system has been prototyped using
Self Organising Maps (SOMs) [33]. It has modeled the
dataset without external control. Here, large vectors map
to smaller and lower dimension vectors using SOMs. The
system analyzed blockchain data using Kohonen and SOM-
brero libraries (in the R programming environment). The
result shows the effective key attributes monitoring of the
blockchain nodes and finds the emerging patterns. More,
another approach included the distributed pattern recognition
system with the concept of Graph Neurons (GNs) [31] to
monitor the blockchain system. GNs are scalable and could
recognize patterns from similar or incomplete patterns. The
GN communicated with adjacent nodes to detect events
within the network by using input data. Preliminary results
showed object detection by the GN was accurate; still, further
work is needed in the area.
C. PREDICTION
The ML models are mostly used for prediction. A good
prediction model helps to make the right decision making
and analytics. Added to this, an ML model to predict the
bitcoin prices has been proposed by Valenkar et al. [30].
This model uses bayesian regression and random forest with
several features such as block size, total bitcoins, day high,
number of transactions, and trade volume. The trained dataset
was normalized using log, z-score, and box-cox normaliza-
tion techniques. Further, a price prediction study has been
done for several cryptocurrencies such as Ripple, Litecoin,
Dash, Bitcoin, and Ethereum cryptocurrencies [34]. Here,
correlation matrices for feature selections used and reported
the general trends within the network. The proposed model
used multiple regression techniques on bitcoin. The system
showed the 0.9944 accuracies for price prediction.
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Tanwar et al.: Machine Learning adoption in Blockchain-based Smart Applications: The challenges, and a way forward
Machine Learning Adoption for Blockchain-
based Smart Applications
Smart
Applications
Device
Personalization
Data Trading
Product
Manufacturing
Customer
Services
Counter-
measures
Real-Time
Analytical
Layers
Oriented
User
Process
Endpoint
Application
Layer
Goal Oriented
Prediction
Response
Monitoring
Detection
Prevention UAV
Smart Cities
Energy and
Utilities
FIGURE 3: Proposed Taxonomy for Machine Learning adoption in Blockchain-based Smart Applications
D. RESPONSE
Tsolakis et al. [35] presented an approach for the secure
exchange of energy data between a cluster of end-users and
a Virtual Node. This approach uses a blockchain-based ar-
chitecture to give a demand response solution. This solution
included Fog-Enabled Intelligent Devices (FEID) [36] at the
user side (act as a blockchain node) and managed smart
contracts with the energy producer [36] in cloud computing
platform [37] [38]. Then, a group of energy users combined
into a centralized Virtual Node. All predictions and forecast-
ing about the energy consumption have done on the Virtual
Nodes. The end-user learned itself from each experience that
helps to improve the accuracy of data passed as the next
iteration to the virtual node.
E. PREVENTION
One of the based uses of ML in BT based application is the
prevention of an issue; for instance, employee verification by
a future employer. A mechanism proposed to reduce the time
taken to verify work history details provided by a prospective
employee [39]. The previously worked organization has to
compile details of an employee (date of joining, leaving,
post, etc.), and a public key is generated to encrypt the
data. Subsequently, a new smart contract is created for the
employee. Then, the address of the smart contract is entered
into the organizationâ ˘
A´
Zs database, and later it verified by
the future employer. In a future organization, work history is
fetched from the smart contract of employees and decrypted
using the key. Then, each work history entry compared
with the database of the previous organization. This ensures
consistency and integrity of data as well as the authenticity
of the sender.
A similar idea to tracking studentâ ˘
A´
Zs learning history
has been proposed [40]. The main aim was to maintain the
learning histories of the students and ensure access control,
privacy, and security. To ensure privacy and access con-
trol, three contracts were suggested: Learner â ˘
A¸S Learning
Provider Contract (LLPC), Registrar â ˘
A¸S Learning Provider
Contract (RLPC), and Index Contract (IC). The role of RLPC
is to specify all conditions that describe request access by
the provider within the network. LLPC shows that a learner’s
data exists on the providerâ ˘
A´
Zs database. The IC map the
learners and providers and the corresponding learning his-
tory. Here, specialized Learning Blockchain Application Pro-
gram Interfaces (LB APIs) has been proposed to encourage
the adoption of this technology. Initially, the system is started
with a boot node in the network. A new provider is added
to the network as per the conditions described in RLPC. A
new learner account is created and noted LLPC. In case, the
new provider wants access to learners’ data, it has to request
the learner using the LLPC, and upon granting, data will be
accessible.
6VOLUME 4, 2016
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Tanwar et al.: Machine Learning adoption in Blockchain-based Smart Applications: The challenges, and a way forward
TABLE 2: Comparative Analysis of Goal Oriented Approaches
Authors Year Objective Merits Demerits 1 2 3 4 5
Meng et al. [12] 2018 To present use of blockchain in In-
trusion Detection
Scope of application of blockchain
was discussed
Discusses only data sharing and
trust management issues of col-
labrative intrusion detection
XXXXX
Alexopoulos et al.
[29]
2018 Improvement in trust and account-
ability in Collaborative Intrusion
System using blockchain
Design considerations thoroughly
explained
Use of ML not covered XXXXX
Signorini et al. [32] 2018 to provide a blockchain based
anomaly detection system
extremely less bandwidth overhead
of about 0.248%
Only basic testing carried out con-
sisting of only 3 node
XXXX X
Chawathe et al. [33] 2018 self-organizing maps to monitor
blockchain data in real-time
Running time for the number of
iterations, attributes and instances
shows a linear relationship
Security issues are not highlighted
in storing bitcoin data in a database
XXXXX
Hudaya et al. [31] 2018 distributed pattern recognition
for event monitoring within IoT-
blockchain network
use of graph neuron approach en-
ables distributed pattern recognition
integration of IoT-blockchain not
covered in depth
XXXXX
Saad et al. [34] 2018 predicting bitcoin prices with high
accuracy using ML
user activity dynamics and its ef-
fects discussed extensively
not enough focus on other factors
other than supply demand
XXXXX
Tsolakis et al. [35] 2018 demand response system using
blockchain
security concerns addressed appro-
priately
ML aspects not covered XXXXX
Sarda et al. [39] 2018 To prevent work-history related
frauds using blockchain
Extensive use of blockchain to
share and verify work history
No intersection of blockchain and
ML was found, discussion on secu-
rity issues was not carried out
XXXXX
Liang et al. [41] 2019 Present micro-blockchain based dy-
namic intrusion detection
Scope of application of blockchain
was discussed
Discusses attacks and trust manage-
ment issues only
XXXXX
Sgantzos et al. [42] 2019 Present genetic algorithm imple-
mentation on blockchain
Discussed framework and future
applications
Natural Language interaction with
blockchian based system need to be
explored
XXXXX
Parameters- 1:ML, 2:Blockchain, 3:Detection, 4:Algorithm, 5:Trust Management
Notations- X: Considered, and X: Not Considered.
F. COMPARISON OF EXISTING APPROACHES FOR
GOAL ORIENTED DIMENSION
A detailed comparison of the approaches discussed above
is shown in Table 2. This comparison of works included
several parameters such as trust management, ML, algorithm,
blockchain, detection, merits, and demerits.
V. LAYERS ORIENTED
At this moment, BT is very young, and understanding of
layer division is not enough. Considering the BT as a solitary
layer is similar to enduring everything among the physical
layers and transport layers into a single layer. While separat-
ing the BT into multiple layers, we can understand various
properties of BT which are needed to be implemented. Some
of the properties are: (i) Security: Nodes that do not control
rare resources (commonly computing power) majorly cannot
convince others for a different version of the ledger. (ii) Live-
ness: Here, new blocks can be added to the blockchain with
suitable latency. (iii) Stability: Nodes within the blockchain
network should not amend their belief of the consensus
ledger except rare cases. (iv) Accuracy: Blocks added to the
ledger must signify valid transactions such as they imitate to
a description of how new blocks relate to previous blocks.
This dimension discusses the adoption of ML techniques in
various layers of a blockchain network, such as endpoints,
application layer, process, network (intrusion), and finally,
user level. The proposed categorization into layers recognizes
each property. Security is accomplished at each layer, Live-
ness is realized at the user layer, Stability is achieved at the
network and application layer, and accuracy is achieved at the
endpoints, wherever blocks have significance.
A. APPLICATION LAYER
The application layer helps to accomplish the security, liveli-
ness within the network. An approach to detect malware (in
the form of a portable executable file) has been proposed
using DL methods [43]. The portable executable file was
converted to a grayscale image to feed to the Deep belief
network. In this solution, the receiving node broadcast the
file to other nodes within the network. The file executed
locally in the local detection model of each node. The prob-
ability value of being malware is appended with the block.
A weighted average for the trust of the node is applied to
the appended probabilities. The weighted average calculated
on the condition that the file is malware or not. The model
used Restricted Boltzmann Machines (RBM) techniques with
3000 hidden units. The results showed good accuracy for
malware detection.
B. ENDPOINT
Endpoints included the small computing devices which are
participating in the BT based application network. To handle
the issue of fast computing and reduction in computation
power, emergent technology Edge Computing comes into the
picture. The Edge Computing Service Provider (ECSP) can
meet the requirements, and an approach has been proposed
to maximize the revenue of ECSP [44]. The ECSP allocates
the resource unit to the highest bidder who participated in the
bidding process.
Similarly, Kim et al. [45] proposed an architecture to
offload resources computations in Deep Neural Networks to
edge computing. Within this architecture, the embedded de-
vice (the computation device at the edge server) and the edge
server had to make initial deposits. After that, the embedded
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Tanwar et al.: Machine Learning adoption in Blockchain-based Smart Applications: The challenges, and a way forward
TABLE 3: Comparative analysis for Layer-Oriented approaches
Author Year Objective 1 2 3 4 5 Pros Cons
Raje et al. [43] 2017 To design a decentralized firewall
using blockchain and deep learning
XXXXXDiscussed Deep Belief Networks
and training procedure in detail and
explored various architectures
Number of devices in experimen-
tal setup was small and working of
blockchain not discussed
Portnoff et al. [46] 2017 To identify human traffickers using
classified ads and bitcoin transac-
tions
XXXXXA detailed discussion on data anal-
ysis
Bitcoin transaction linking was not
perfect, false positives were high,
blockchain aspect not covered
Wasim et al. [47] 2017 Proposed a Law as a service
architecture to monitor Contract
Breaches and issue injunctions
XXXXXResults and system model defined
clearly, proposed unsupervised ML
algorithm
Blockchain not included in the
model description
Luong et al. [44] 2018 Edge computing usage for mining
applications and ML for resource
allocation
XXXXXML algorithms thoroughly dis-
cussed, well-documented results
Focus on optimizing revenue but no
optimal allocation of resources
Kim et al. [45] 2018 Proposed deep neural networks ar-
chitecture for blokchain based edge
computing application
XXXXXExecutes complex programs on
ethereum blockchain using virtual
machines
Experimental results not discussed
in detail, Deep Neural Network ap-
plications not covered
Parameters- 1:Blockchain, 2:Edge Computing, 3:Neural Network, 4:Supervised Learning, 5:Unsupervised Learning
Notations-X: considered, and X: not considered.
device sends the computations to edge servers. Then, the
edge server returns the results, which are verified by other
nodes within the network. The server is rewarded for its
work after validation. The typical Ethereum architecture had
been modified in this implementation by replacing Ethereum
virtual machine by V8 javascript virtual machine. The blocks
generation part had been separated from the virtual machine
to ensure that smart contract execution cannot affect the
rate of generation of blocks. The system performance shows
better results compare to the state-of-art approaches.
C. PROCESS
This subsection comprises ML techniques to the process in-
volves in the blockchain-based applications. An ML classifier
has been used to detect the human traffickers using adult
classified ads [46]. It included ads dataset from Backpage (a
website for online classifieds). It used a supervised learning
model and used logistic regression for classification. The
labeled dataset with identifiers like email and phone number
has been used for testing. This approach takes two ads at a
time as an input to the model. If the ads are from the same
author, the output will be same. The trained model showed an
89.54% true positive rate. Further, this approach used to build
a graph and found links between ads and their related bitcoin
transactions. This approach showed a high false-positive rate.
D. USER
Users are the individual or system that uses the system
functionality seating at one end of the application. Wasim
et. al [47] proposed law as a service ML-based architecture
to monitor contract breaches. Previously, contract breaches
were dealt with or without the use of courts. The pro-
posed system used an unsupervised ML algorithm called
Probability-based Factor Model to issue injunctions. This
model simulated using three service providers Redis [48],
MongoDB [49], and Memcached Servers [50]. Services
monitor the contracts for breaches. The results showed that
services that perform complex operations are more likely to
breach contracts.
E. COMPARISON OF EXISTING APPROACHES FOR
LAYER ORIENTED USE CASES
A detailed comparison of the approaches is discussed in
Table 3. This comparison is made based on several param-
eters such as edge computing, blockchain, neural network,
supervised learning, pros, and cons of the proposed approach.
VI. COUNTERMEASURES
The ML approached in response to a threat or monitoring
a system can be classified as real-time analytics or analysis
based on historical data [33]
A. ANALYTICAL
In todayâ ˘
A´
Zs world, every enterprise generates huge
amounts of data from different sources such as social me-
dia, smartphones, IoT, and other computing devices. These
data are tremendously valuable to organizations. The overall
technique to find a meaningful pattern from these data is
called data analytics. It is a process to convert data from
foresight to insight. It describes what happened in the past,
draw awareness about the present, and make predictions
(with some ML techniques) about the future. ML techniques
can be categories as supervised, unsupervised, and reinforce-
ment learning to analyses the data. The supervised learn-
ing included classification and regression originated on the
idea of example-based learning. Next, unsupervised learning
techniques perform clustering, dimensionality reduction, and
recommendation of a system based on the dataset. This used
to recognize hidden patterns or focus on the well-educated
behavior of the machine. The reinforcement learning ap-
proach helps to reward maximization. In the next decades,
our society will be driven by new technological developments
in ML and BT. Further, a traceability algorithm has been pro-
posed for bitcoin mining by using the ANN approach [51].
It aimed to remove irrelevant data in mining and introduce
traceability to the system. This was helpful in a distributed
architecture and decrease traceability time.
DL is a subset of ML that is originated on definitive
algorithms influenced by the overall structure and work of
the neural network in the human brain. DL rationalizes
tasks to perform speech recognition, image recognition, make
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Tanwar et al.: Machine Learning adoption in Blockchain-based Smart Applications: The challenges, and a way forward
insightful decisions on natural language processing. DL net-
work consisted of input, output, and hidden multi-layer. It
accepted a new block and a history of the previous block as
an input in BT based applications. It used a state-transition
algorithm on the features such as hash value, nonce, address,
and transaction data. An ML-based classification approach is
used to identify the cyber-crime in Bitcoin [52]. The results
provided a glimpse of the size of cybercrime in the Bitcoin
ecosystem.
B. REAL-TIME
ML enables real-time analytics for all types of data, such as
social â ˘
A¸S accessible, transactional, and operational. It uses
in-memory computing though leaving unremittingly updated
data securely. It improves analytics accuracy and accelerates
the predictive behavior of ML models. It has been noticed
that BT makes real-time cross edge transactions in monetary
and payment frameworks. Several fintech innovators and
banks are currently investigating blockchain due to fast and
real-time settlement of massive amounts independent of geo-
graphic hindrances. Similarly, associations that require real-
time analytics of information on a huge scale can approach
an ML and blockchain-empowered framework to accomplish
the goal. With ML and BT, financial institutions and other
associated organizations across the globe can trace the data
changes to make quick and fast business decisions regard-
less of irregular activities or suspicious transactions. More-
over, the performance of the blockchain-based Software-
Defined Vehicular Network (SDVN) has been improved us-
ing deep Q-learning methodology [53] [54]. This approach
used a permissioned blockchain with Byzantine fault tol-
erance as a consensus protocol. Results showed that this
scheme managed network and computing resources better
and gave the best throughput in the SDVN [55]. Further,
Liu et al. [22] recommended a data collection framework for
Industrial-IoT (IIoT) applications. This framework combines
the use of deep reinforcement learning (DRL) and Ethereum
blockchain. To store and share data, Ethereum nodes were
categories into two categories: mining nodes and nonmining
nodes. The proposed DRL algorithm used three plain compo-
nents, actions (moving path and remoteness of mobile termi-
nals), states (environment description), and rewards (amount
achieved). The proposed algorithm shows the 34.5% increase
in geographical fairness compared to a random solution.
VII. SMART APPLICATIONS
The last dimension covers the adoption of ML techniques
to BT-based smart applications such as data trading, UAV,
product manufacturing, Medical, and Healthcare [56], Smart
Cities, automated Customer Service, and Device Personaliza-
tion as shown in Figure 4. ML and BT are revolutionizing up-
to-date technologies by transforming customer experiences,
behaviors, and business models [57]. Both are making strides
in the major smart application:
FIGURE 4: A list of Smart Applications
A. CUSTOMER SERVICE
With the upsurge of the customer, the Customer service has
to be more efficient and automated to meet rising customer
needs. One of the best ways is to automate the process to
increase a companyâ ˘
A´
Zs capabilities. Wang et al. [58] pro-
posed an AutoML framework for the blockchain-based appli-
cation. It consists six layers: (i) Organization Layer (Includes
entities such as shops, malls, online stores), (ii) Customer
Layer, (iii) Application Layer (applications provide services
to the consumers), (iv) Data conversion layer(Data masking
and unmasking), (v) AutoML layer (Consists various ML
models such as Linear regression, SVM and logistic regres-
sion for predictions), (vi) Blockchain Layer (Secure storage
of data and ensures the safe data exchange) This framework
helps organizations to keep their data safe, automate their
processes and share data with other organizations in a mu-
tually beneficial and safe way.
B. DEVICE PERSONALIZATION
Device Personalization (DP) is a component that uses pre-
dictions across smart devices to improves the quality of
service (QoS) such as actions in the launcher, smart text
selection during writing on text pad. DP Services uses system
permissions to provide smart predictions. In a smart home
environment, and ML model-based single DP framework has
been implemented [59]. Here, smart devices connected to a
smart hub. Every time a user uses a device, a log is generated
with user data, device data, and other parameters for that
device. For example, uses of air conditioner at home. This
log data help to adjust the operation of the device.
C. PRODUCT MANUFACTURING
As a feature of the manufacturing process, organizations
have started trusting blockchain-based procedures to em-
power production, security, transparency, and compliance
checks. Instead of following fixed schedules of machine
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maintenance, ML algorithms are being used to make flexible
plans at specific periods. Product testing and quality control
have also automated increasingly, with versatile algorithms.
It successfully detects faulty and good products, particularly
in profoundly delicate situations. For example, Porsche (a car
manufacturing company) is one of those early adopters of
ML and BT technologies; to improve automobile safety and
increase capabilities. The organization utilizes blockchain
innovation to transfer data more safely and rapidly, offering
its clients peace of mind, regardless of parking, charging, and
third-party access to their car.
D. DATA TRADING
DataTrading is an innovative platform that makes advanced
trading possible for retail traders from all over the world. An
Ethereum based data trading framework has presented that
succeeded in preventing single-point failure and preserving
privacy at the same time [10]. The framework consists of
three entities, data provider, a data consumer, and a market
manager. Once the network is set up, the data consumer and
data provider registered themselves with the market manager.
The data provider needed to deposit an amount with the
manager greater than the amount to be paid by the consumer.
A list indicating topics of data available with the provider is
published. The client referred it and requests some encrypted
data blocks for content validation using a distance metric
learning technique. After successful validation, the client
responded, and the provider sent a signature, and the client
published a smart contract to the network. The provider then
sent another signature, and these two signatures, the client,
decrypted the data. There are different protocols such as setup
protocol, register protocol, payment and acknowledgment
protocol, and query protocol that facilitate the communica-
tion between provider, consumer, and the manager.
E. UNMANNED AERIAL VEHICLE
A UAV or drone is an aircraft that runs without a pilot
(human) aboard. Kuzmin et al. [9] has proposed blockchain-
based UAVNet model includes different devices such as
a network of satellites, cellular base stations, and ground
control stations. Here, BT served multiple purposes, such
as preserving the integrity of the data and for distributed
graphs computation. The communication between satellites
and base stations is prone to electromagnetic jamming, hence
a blockchain-based system enables UAVs to store relevant
coordinateâ ˘
A´
Zs data and operate autonomously within the
jamming zone. This solution used a proof-of-graph consen-
sus algorithm with a simplified memory bounded algorithm
of any existing shortest path to validating a new transaction.
F. SMART CITIES
Smart cities improve the living experiences of individuals.
ML and blockchain emergent technologies play a crucial role
in the innovation of smart cities to provide critical services
and infrastructure components such as healthcare [60], city
administration, smart homes, education, transportation, real
estate, and utilities [61]. (i) Smart Homes: Smart homes
can be monitored, and DP can help to improve the quality
of livelihood. (ii) Smart Parking System: Here, arrival and
departure of vehicles can be tracked for different parking
lots available in a decentralized manner within the smart city
[61]. Consequently, smart parking lots should be designed by
seeing the number of cars in each region. Furthermore, new
parking lots must be recognized spontaneously to benefits in
dealers and vehicle ownerâ ˘
A´
Zs daily life. (iii) Smart Weather
and Water Systems: Here, the system can use some sensors
to generate appropriate data such as temperature, wind speed,
rain, and pressure. The analysis of these data through ML
techniques can contribute to improving the density of smart
cities. (iv) Smart Vehicular Traffic: Vehicular traffic data with
a suitable analysis will benefit the government and citizens to
a great extent [62]. Everyone can decide the arrival time to
a destination by using these data. (v) Surveillance Systems:
Physical security is an utmost important concern for citizens
anywhere they live. To address this issue, smart technology
such as ML and BT can be configured for it. Consequently,
collecting and analyzing data and identifying crimes is one of
the challenging tasks. (vi) Smart Healthcare: It includes the
accessibility of the caregivers and doctors, identification of
nearest medical stores, and clinic. Hence, ML and BT play
a vital role while data is getting generated [63] [64]. (vii)
Smart Governance and Smart Education: Smart governance
can maintain a city smartly. A smart city includes a differ-
ent way of education. It captures the data of the students
and employees in educational and government institutions.
Predictions and analytics required to keep the stuff up to
standards.
G. ENERGY AND UTILITIES
In the Energy industry, BT is assisting to simplify energy
exchanges. For example, IOTA enables smart transformation
across the entire energy industry by implementing BT [65].
It uses the concept of peer-to-peer (P2P) energy production
and consumption. Smart energy microgrids are progressively
developing fame as a technique of making ecological energy
resources. LO3 Energy (a Newyork-based organization) is
also using a blockchain-based development to develop en-
ergy generation, transmission, conservation, and exchanging
within neighborhood networks. The technology uses micro-
grid and smart meters, together with smart contracts, to
manage and track energy transactions. GE Digital, together
with Evolution Energie (a startup), has created an application
that encourages the tracking of renewable energy in SG and
uses blockchain to give energy sources certificate. The idea
is allowing organizations and individuals to trade renewable
energy sources without the involvement of third-party [36].
Globally, most of the industries are dogged by third-party,
who increase the business cost. BT has already interrupted
that model by facilitating the P2P model for customers.
The revolution works even better when combined with the
ML and BT together. We have been witnessed the financial
revolution by using the cryptocurrency world; similarly, we
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Tanwar et al.: Machine Learning adoption in Blockchain-based Smart Applications: The challenges, and a way forward
are going to observe more innovation disruptions as a result
of combining ML and BT.
VIII. FUTURE RESEARCH ISSUES AND CHALLENGES
Researchers are looking forward to these technologies across
the globe, but still, various obstacles resist the integration of
BT and ML [66]. Their integration is still in its infancy. Many
open issues and challenges are yet to be addressed. Here,
we discuss the futuristic open issues and challenges of ML
adoption in BT for secure communication, as shown in Figure
5. We highlighted the challenges as Suitability, Infrastructure,
Privacy, security, Memory, Implementation, and Quantum
resilience.
A. SUITABILITY
Blockchain is a viable solution if the source of data cannot be
trusted, and several entities are high in the distributed envi-
ronment. If performance is required, then a simple database
is a better option. Therefore, the architecture of blockchain
must be understood before its use in any application [67].
B. INFRASTRUCTURE
Blockchain specific hardware and network infrastructure im-
prove the performance of many blockchain-based applica-
tions. These could include network administration, mining
hardware, decentralized storage, and communication proto-
cols [30]. However, products tailored to use in blockchain
are still under investigation (involving major tech companies
and financial institutions).
C. PRIVACY
Data generated by devices to be stored on the blockchain is
available to the entire blockchain nodes [33]. This leads to a
potential privacy concern for data that needs to be kept either
private or confidential. Such issues could be resolved by the
use of private blockchains, controlled access, and encryption.
However, the ML models adoption on these limited data
imposed barriers for predictions and analytics.
D. MEMORY
The size of blockchain keeps growing as new blocks are
added to it. Consequently, the entire chain must be stored
by all nodes, which creates a significant memory constraint
on these devices. The performance is also affected by an
increase in chain size. Besides that, the storage of irrelevant
data also wastes computational resources. Blockchains are
immutable, and hence storage management is a major issue
in most implementations.
E. IMPLEMENTATION
A large- scale blockchain network will require an equiva-
lently large number of transactions. Implementation of this
size of blockchain invites potential issues; for instance, high
demand for internet bandwidth, which not easy to reduce,
transactions will be a burden on the network. Hence, the
Research
Challenges
Infrastruct
ure
Memory
Implement
ation
Security
Quantum
resilience
Privacy
Suitability
FIGURE 5: Future Research Issues and Challenges
addition of blocks and transactions needs to be decreased to
meet the inevitable demand.
F. SECURITY
Blockchains are decentralized, and they are prone to security
issues [68]. The most common concern is that due to attacks,
the consensus protocol may be compromised, such that the
mining power of a few farms will control which blocks are
added to the network. This particular concern is present in
public blockchains. Private versions are unaffected by this
attack as they have each node identified, and an appropriate
consensus protocol is in place.
G. QUANTUM RESILIENCE
The hashing algorithms used by blockchains could soon be
broken with quantum computers. A blockchain is predom-
inantly at risk from this because it uses one-way functions
for encryption (only protection in digital signature). This
would cause all the features that make blockchains a viable
storage structure (obsolete). Luckily, quantum computing
offers opportunities to boost the performance and security of
blockchains. Quantum cryptography can reinforce the secu-
rity of the blockchain network as quantum communication is
authenticated inherently (users cannot mimic another user). It
can encrypt entire P2P communications and replace classical
digital signatures in the blockchain network. Research is
underway to design blockchain with quantum computing
[69].
IX. CASE STUDY
In order to demonstrate the proposed architecture for ML
adoption in blockchain-based smart applications, we present
a case study, as shown in Figure 6. In this case study, we study
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Energy Trading Platform
Smart
Grid
Energy Storage
Device
Entities participating in energy trading
Blockchain
Accounts
Industries Buildings
Individual Houses
FIGURE 6: An Energy Trading system
a blockchain-based SG system for energy transactions using
cryptocurrency [70] [71]. This framework uses a blockchain
and DL approach to complete an energy transaction. It is
a reliable P2P energy system. It is based on the Byzantine
fault tolerance algorithm to produce high system throughput.
This approach consists of five phases (i)setup phase, (ii)
agreement phase, (iii) consensus-making phase, (iv) block
creation phase, and (v) a change view phase. Here, blocks
are generated using hash functions and a short signature. It
is an IDS that works on recurrent neural networks to detect
fraudulent transactions and network attacks in blockchain-
based energy applications. The performance of this IDS has
been studied on different energy datasets. It consists of four
entities for communications: (a) Energy Buyer, (b) Energy
Seller, (c) Blockchain, and (d) IDS [41]. Energy buyer trade
energy with the energy seller. An energy buyer shows that
he has sufficient energy money (cryptocurrency) that fulfills
the minimum asset requirement of energy sellers. An energy
buyer will be a person or commercial building or industry.
Energy buyers can be located in a Home Area Network
(HAN), Building Area Network (BAN), or Neighborhood
area Network (NAN). Energy seller entities demonstrate that
it has sufficient energy to sell. Energy sellers can be located
in HAN, BAN, NAN, or energy companies. Energy seller
(entities) produces energy from renewable sources such as
wind energy, solar energy, and biomass. The seller can be
a neighbor, local society with renewable energy resources,
utility provider, or SG. Blockchain entity is a distributed
digital ledger, which is encompassing all energy transactions
in the SG system.
Once an energy seller produces energy from renewable
energy sources and uses that energy for his own use. After
that, if the seller left with energy, then he publishes his per-
unit prices per kilowatt energy on the blockchain network.
In this P2P energy trading system, the needed energy buyer
will look upon the published price and unit of energy. First of
all, the energy buyer checks his account that he has sufficient
cryptocurrency balance for energy trading. Then, if the en-
ergy demand of energy buyers and per-unit price matched his
requirement of energy at that moment, he sends a purchase
request to the energy seller through the blockchain network.
After validation of the buyer on the blockchain network seller
sells the energy to the buyer and receives cryptocurrency in
his account. This P2P energy trading system required further
data analysis to identify the frequent buyer and seller for
the recognition of malicious transactions within the network.
A malicious block or node can impact the P2P system and
compete for energy trading. IDS can help to figure out the
deceitful transactions and network attacks on energy trading
applications [72]. The result shows the good performance of
the system compares to the state-of-art approaches.
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Tanwar et al.: Machine Learning adoption in Blockchain-based Smart Applications: The challenges, and a way forward
X. CONCLUSION
The recent advancements in Blockchain and ML have made
them path-breaking technologies. The distributed ledger has
the possibility to work as the backbone of various smart
applications such as smart cities, UAV, SG, data trading. In
this paper, we have presented detailed information on BT
and ML, along with their usages in smart applications and
proposed an ML-BT based architecture. This architecture
can be used to design and deploy an ML-BT based data
analysis system. A discussion and comparison of various
existing surveys are presented. Then, we presented ML-BT
solution taxonomy, focusing on goal oriented, layer oriented,
countermeasures, and smart application dimensions. A com-
parative analysis of available methodologies and approaches
is presented in each dimension. Then, we have listed several
research challenges being faced during ML adoption in BT-
based systems, which require solutions. We also emphasized
a number of research prospects such as infrastructure avail-
ability, quantum resilience, and privacy issues that can serve
as future research directions in this field. Then, we presented
a case study on the energy trading system to verify the
effectiveness of the proposed architecture and concluded the
paper at last.
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14 VOLUME 4, 2016
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2019.2961372, IEEE Access
Tanwar et al.: Machine Learning adoption in Blockchain-based Smart Applications: The challenges, and a way forward
SUDEEP TANWAR is an Associate Professor
in the Computer Science and Engineering De-
partment at Institute of Technology, Nirma Uni-
versity, Ahmedabad, Gujarat, India. He is visit-
ing Professor in Jan Wyzykowski University in
Polkowice, Poland and University of Pitesti in
Pitesti, Romania. He received B.Tech in 2002 from
Kurukshetra University, India, M.Tech (Honor’s)
in 2009 from Guru Gobind Singh Indraprastha
University, Delhi, India and Ph.D. in 2016 with
specialization in Wireless Sensor Network. He has authored or coauthored
more than 100 technical research papers published in leading journals
and conferences from the IEEE, Elsevier, Springer, Wiley, etc. Some of
his research findings are published in top cited journals such as IEEE
Transactions on TVT, IEEE Transactions on Industrial Informatics, Applied
Soft Computing, Journal of Network and Computer Application, Pervasive
and Mobile Computing, International Journal of Communication System,
Telecommunication System, Computer and Electrical Engineering and IEEE
Systems Journal. He has also published three edited/authored books with
International/National Publishers. He has guided many students leading to
M.E./M.Tech and guiding students leading to Ph.D. He is Associate Editor
of IJCS, Wiley and Security and Privacy Journal, Wiley. His current interest
includes Wireless Sensor Networks, Fog Computing, Smart Grid, IoT, and
Blockchain Technology. He was invited as Guest Editors/Editorial Board
Members of many International Journals, invited for keynote Speaker in
many International Conferences held in Asia and invited as Program Chair,
Publications Chair, Publicity Chair, and Session Chair in many International
Conferences held in North America, Europe, Asia and Africa. He has been
awarded best research paper awards from IEEE GLOBECOM 2018, IEEE
ICC 2019, and Springer ICRIC-2019.
QASIM BHATIA is a graduate student of Nirma
University, Ahmedabad, India. His research inter-
est includes machine learning, network security,
fog computing, and cloud computing.
PRUTHVI PATEL is a graduate student of Nirma
University, Ahmedabad, India. His research inter-
est includes big data analytics, fog computing, and
cloud computing.
APARNA KUMARI pursuing Ph.D. from Depart-
ment of Computer Science and Engineering, In-
stitute of Technology, Nirma University, Ahmed-
abad, Gujarat, India. She received M.Tech from
Jawaharlal Nehru University, Delhi, India in 2012.
Her research interest includes big data analytics,
smart grid, blockchain technology and cloud com-
puting.
PRADEEP KUMAR SINGH is currently work-
ing as Assistant Professor (Senior Grade) in De-
partment of CSE at Jaypee University of Informa-
tion Technology (JUIT), Waknaghat, H.P. He has
completed his Ph.D. in Computer Science & En-
gineering from Gautam Buddha University (State
Government University), Greater Noida, UP, In-
dia. He received his M.Tech. (CSE) with Distinc-
tion from GGSIPU , New Delhi, India. Dr. Singh
is having life membership of Computer Society of
India (CSI), Life Member of IEI and promoted to Senior Member Grade
from CSI and ACM. He is Associate Editor of International Journal of
Information Security and Cybercrime (IJISC) a scientific peer reviewed
journal from Romania. He has published nearly 85 research papers in various
International Journals and Conferences of repute. He has received three
sponsored research projects grant from Govt. of India and Govt. of HP worth
Rs 25 Lakhs. He has edited total 8 books from Springer and Elsevier and
also edited several special issues for SCI and SCIE Journals from Elsevier
and IGI Global. He has Google scholar citations 401, H-index 12 and i-10
Index 15 in his account.
WEI-CHIANG HONG (M’04-SM’10) is a Pro-
fessor in the Department of Information Manage-
ment, Oriental Institute of Technology, Taiwan.
His research interests mainly include computa-
tional intelligence (neural networks and evolution-
ary computation), and application of forecasting
technology (ARIMA, support vector regression,
and chaos theory) and machine learning algo-
rithms. Professor Hong serves as the program
committee of various international conferences
including premium ones such as IEEE CEC, IEEE CIS, IEEE ICNSC,
IEEE SMC, IEEE CASE, and IEEE SMCia, etc. In May 2012, his paper
had been evaluated as Top Cited Article 2007-2011 by Elsevier Publisher
(Netherlands). In Sep. 2012, once again, his paper had been indexed in
ISI Essential Science Indicator database as Highly Cited Papers, in the
meanwhile, he also had been awarded as the Model Teacher Award by
Taiwan Private Education Association. Professor Hong is a senior member of
IIE and IEEE. He is indexed in the list of Who’s Who in the World (25th-30th
Editions), Who’s Who in Asia (2nd Edition), and Who’s Who in Science and
Engineering (10th and 11th Editions). Professor Hong is currently appointed
as the Editor-in-Chief of the International Journal of Applied Evolutionary
Computation, in addition, he serves as a guest editor of the Energies, and
is appointed as an Associate Editor of the Neurocomputing, Forecasting,
and International Journal of System Dynamics Applications. He has Google
scholar citations 5424, H-index 38 and i-10 Index 64 in his account..
VOLUME 4, 2016 15