Clustering man in the middle attack on chain and graph-based blockchain in internet of things network using k-means

Computer Science and Information Technologies
Vol. 5, No. 2, July 2024, pp. 176~185
ISSN: 2722-3221, DOI: 10.11591/csit.v5i2.pp176-185  176
Journal homepage: http://iaesprime.com/index.php/csit
Clustering man in the middle attack on chain and graph-based
blockchain in internet of things network using k-means
Sari Nuzulastri1
, Deris Stiawan1
, Hadipurnawan Satria2
, Rahmat Budiarto3
1
Department of Computer Engineering, University of Sriwijaya, Palembang, Indonesia
2
Department of Informatics Engineering, University of Sriwijaya, Palembang, Indonesia
3
Department of Computer Science, College of Computing and Information, Al-Baha University, Al Bahah, Saudi Arabia
Article Info ABSTRACT
Article history:
Received Dec 6, 2023
Revised May 27, 2024
Accepted Jun 4, 2024
Network security on internet of things (IoT) devices in the IoT development
process may open rooms for hackers and other problems if not properly
protected, particularly in the addition of internet connectivity to computing
device systems that are interrelated in transferring data automatically over
the network. This study implements network detection on IoT network
security resembles security systems from man in the middle (MITM) attacks
on blockchains. Security systems that exist on blockchains are decentralized
and have peer to peer characteristics which are categorized into several parts
based on the type of architecture that suits their use cases such as blockchain
chain based and graph based. This study uses the principal component
analysis (PCA) to extract features from the transaction data processing on
the blockchain process and produces 9 features before the k-means algorithm
with the elbow technique was used for classifying the types of MITM attacks
on IoT networks and comparing the types of blockchain chain-based and
graph-based architectures in the form of visualizations as well. Experimental
results show 97.16% of normal data and 2.84% of MITM attack data were
observed.
Keywords:
Blockchain
Internet of things
K-means
Man in the middle
Network security
This is an open access article under the CC BY-SA license.
Corresponding Author:
Deris Stiawan
Department of Computer Engineering, University of Sriwijaya
Indralaya, Ogan Ilir-30662, Palembang, Indonesia
Email: deris@unsri.ac.id
1. INTRODUCTION
The development of internet of things (IoT) as a smart device in several technologies [1]–[4] that
changes the world with the development of internet networks [5]–[8] are seen in collecting data, and controlling
tools to do certain things through the internet network. Self organization and communication using the cloud as
a data storage medium are vulnerable to attacks because many devices are connected to the internet [9], [10].
Network security in IoT devices is used to protect data during the data transmission process to keep them safe
because devices connected to IoT devices can open gaps for hackers and other problems [11]. Mallik et al. [12]
and Nayak and Samaddar [13] explain about the type of man in the middle (MITM) attack that aims to retrieve
information in a network protocol or secure sockets layer and transport layer security (SSL/TLS) MITM attack
and the domain name system (DNS) spoofing attack that provides different data (data falsification) [14].
Choi, et al. [15] explain the blockchain-based MITM security system that detects MITM attacks by filtering,
detecting, and comparing networks implemented on a network security system on the blockchain in the IoT.
Singh et al. [16], Li and Kassem [17] describe the distributed ledger technology (DLT) which is part
of the blockchain that provides a decentralized data management system in storing and sharing data on every
network transaction. Ferraro et al. [18] explain the directed acyclic graphs (DAGs) in the blockchain

Comput Sci Inf Technol ISSN: 2722-3221 
Clustering man in the middle attack on chain and graph-based blockchain in … (Sari Nuzulastri)
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architecture for the DLT can make transactions easier and more linear because the network is peer to peer
[19]–[21]. It provides a detailed analysis of security attack patterns applied to IoT devices. The security
system that exists on a decentralized blockchain that stores and shares data is a decentralized data
management system [22] and peer to peer characteristics can hinder the improvement of blockchain
technology in several aspects of life.
Blockchain technology is categorized into several parts based on the type of architecture that suits
its use case. In the context of blockchain chain based and graph based, there are two types of data structures
used by blockchain to store transaction data and build evidence of consensus [23]. The chain-based
blockchain has a data structure in each block forming a chain and it will continue to grow. In contrast, graph-
based uses a random graph-shaped data structure and each transaction can be directly connected to several
other transactions in the network whose use depends on the purpose of the blockchain being used [24].
The use of the k-means algorithm in the IoT network for grouping data according to their
characteristics has been implemented such as in [25], [26] and show the accuracy in the clustering process of
99.94% with confusion matrix accuracy in the true negative section of 98.62%, true positive of 100%, false
negative of 0.00% and false positive of 1.38%. Related research on DLT in IoT networks that had been
carried out previously discussed the benefits of the data transmission transaction process [27], [28]. These
studies explain the stochastic mechanism in the transaction process that existed in the blockchain architecture
for DLT to make transactions faster and more stable using the Markov chain Monte Carlo (MCMC)
algorithm, which was proven by a numerical balance of 25% on each transaction sent through the protocol.
In general, in security system processes of the IoT networks, it is very important to have an
immutable transaction record to analyze a parasitic chain attack, which aims to see the resilience and security
by using the MCMC algorithm in reducing parasitic chain attacks [28]. As for some research, it was found
that the improvement process that focuses on the number of transactions called Tangle [29], [30] have proven
that by using the tip selection algorithm (TSA) method, the level of confidence and sustainability were
getting better along with the increase in the number of transactions. On the other hand, research in 2021
[31]–[33] explains that attacks on IoT networks have increased by up to 20% for the security level of the
identification process in IoT networks integrated with blockchain technology. The use of the elliptic curve
cryptography (ECC)-based algorithm was needed because of the privacy of the security protocol [10]. The
development of IoT aims to connect data through the internet network in the issue of identity security (data
privacy) [34] from various attacks such as MITM attacks that steal passwords, and personal identification
numbers [35]. It generally estimates the theoretical complexity of attacks that allow for multiple
combinations of increased MITM attacks [15], [36].
Therefore, it is necessary to analyze the improvement in the detection of attacks in producing a lower
rate of misclassification of attacks so that the process of sending data in transmission is safe and integrated using
the k-means method. This research discusses the comparison of the performance of blockchain chain-based and
graph-based transactions on data of MITM attack on IoT networks where the traffic features are extracted using
principal component analysis (PCA) and clustered using the k-means method. The results then were displayed
in the form of visualizations. The discussion in this research was as follows: section 2 discusses the proposed
method in determining the data to be clustered. Section 3 provides the results of clustering data of the MITM
attacks and section 4 provides conclusions and hopes for future research.
2. METHOD
In general, the steps in the research methodology used to assist in the preparation of this research
required a clear framework in its stages. The research framework is shown in Figure 1, which consists of a
literature review by reviewing research in recent years, followed by data preparation using a dataset of
550,000 data samples. Next is data preprocessing by performing feature extraction followed by testing,
analyzing the results and drawing conclusions.
Figure 1. Research methodology

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Comput Sci Inf Technol, Vol. 5, No. 2, July 2024: 176-185
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2.1. Feature extraction
Feature extraction is an important part of this process. In this study, datasets were taken from a journal
data [29], which were then preprocessed using the PCA method to reduce the dimensions of the data without
significantly reducing the characteristics of the data. The flow of data preprocessing was depicted in Figure 2.
This preprocessing stage can be divided into two, i.e.: feature extraction process and feature selection process
using PCA method that can reduce the dimensionality of data without significantly reducing the
characteristics of the data [37], [38]. In this process the data was made using simpler features so that it could
be analyzed and interpreted properly in order to produce accurate and reliable data using several techniques
including data cleaning, data transformation and data reduction. The processed data was saved in csv format.
Figure 3 shows an example of a dataset that had been saved in csv format.
Figure 2. Dataset preprocessing flow
Figure 3. Research dataset in CSV format
2.2. Clustering with k-means
Stages of clustering with the k-means method is a grouping with a specified number of clusters
using different cluster shapes [39], [40]. The MITM types are grouped in the form of sample data that has a
lot in common with each other. The flow chart of the working system can be seen in Figure 4.
Figure 4. K-Means clustering flowchart

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Determining the number of clusters at each center point (centroid) by presenting the cluster, the
centroid value can be found using the formula in (1).
𝑐 = ∑
𝑥𝑖
𝑛
𝑛
𝑖=1 (1)
Where, c is centroid value, 𝑥𝑖 is point value/the i-th object, n is number of objects. The formula
in (1) can be rewritten as (2).
µ𝑘 =
1
𝑁𝑘
∑ 𝑥𝑞
𝑁𝑘
𝑞=1 (2)
where, 𝜇𝑘 is centroid of the k-th cluster, 𝑥𝑞 is the q-th object from the k-th cluster, and 𝑁𝑘 is number
of data (samples) from the k-th cluster.
2.3. Confusion matrix calculation
The proposed method’s performances are measured, in terms of accuracy, sensitivity, precision, and
F1 score using a confusion matrix. Confusion matrix has four values, i.e.: True positive (TP), false positive
(FP), true negative (TN), and false negative (FN). Accuracy describes how accurate the model is in
classifying correctly. it can be calculated by dividing the number of correct predictions by the total number of
predictions made, the accuracy calculation uses (3).
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑇𝑃+𝑇𝑁
𝑇𝑃+𝐹𝑃+𝑇𝑁+𝐹𝑁
(3)
The correctly predicted precision can be calculated by dividing the number of positive prediction
results by the number of positive predictions using (4).
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑇𝑃
𝑇𝑃+𝐹𝑃
(4)
Sensitivity measures how good the model is at identifying positive classes by dividing the number
of positive predictions by the total number of positive cases as in (5).
𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 =
𝑇𝑃
𝑇𝑃+𝐹𝑁
(5)
F1 score provides a balanced average value between sensitivity and precision and expressed as (6).
𝐹1 𝑠𝑐𝑜𝑟𝑒 =
2∗(𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛∗𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦)
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦
(6)
3. RESULTS AND DISCUSSION
This section presents the results of the experiments of the MITM attack on the blockchain of the IoT
network. Results of the feature data extraction process used in the clustering process is discussed first,
followed by the clustering result itself. Since the clustering is done with k-means, it is silhouette score is also
analyzed to determine the quality of the clusters. After that, the evaluation result is discussed.
3.1. Feature extraction
The PCA was used to extract and compress the dataset. This stage was carried out to select features
that were used for clustering. Initially the raw data consists of 16 blockchain features. Some of the features
were dropped because they are deemed unsuitable to be used with k-means. Also, some of the blockchain
features must be first transformed into numerical forms. This leaves the number of features down to seven.
Then all the data are normalized before being fed to the PCA which reduces the number of features to three.
With the dimension reduced to three, the dataset can be easily visualized with 3D graphs. Figure 5 shows the
features that were used before and after the PCA process. PCA generates new features that are a linear
combination of actual features, as such the resulting features have no associated meaning with the actual
blockchain.

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Figure 5. Blockchain features used for clustering
3.2. Clustering results with k-means
To measure the quality of the similarities within clusters and the differences between clusters as the
result of clustering using k-means method its silhouette score is calculated. This score uses a measurement
range of [-1,1] which means the higher the score of the silhouette, the more optimal number of clusters. The
result of the quality test measurement with silhouette score with six clusters was 0.417. This score indicates
that the K- Means was able to create distinct enough clusters while perhaps not the best possible.
Figure 6 shows the silhouettes of each cluster with the vertical line marking the average silhouette
score. The cluster heights in the figure denote the variation of the nodes within each cluster. Most clusters
have consistent height, except cluster 4. The maximum scores of each cluster are in fact close to 0.7, which is
considered strong. But some clusters have negative scores, notably cluster 1, 2 4 and 5. These negative scores
indicate outliers but their existences are still minimal. Nevertheless, they are responsible for reducing the
average down to 0.4 even though all clusters have maximum scores above 0.6.
Figure 6. Silhouette score of the clustering
The clustering with k-means method produced a total of six clusters, shown in Figure 7 where nodes
belonging to cluster 0, cluster 1, cluster 2, cluster 3, cluster 4 and cluster 5 are marked with color blue,
yellow, green, brown and red respectively. As can be seen in the figure, most clusters have notably clear
boundaries and contain nodes that are all close together with only a few outlier nodes. Cluster 4 (purple)
though, has more spread-out nodes. Compared to other clusters, cluster 4 also has the least amount of nodes.
This is consistent with the previously discussed silhouette plot where it was the only cluster with low height
or score variations.
To help understanding the clustering result, Figure 8 shows the parallel coordinates plot of each
cluster against all of the features. Using this plot, the relation of each feature of data nodes and the cluster
they belong to can be analyzed. Note that the values in y axis are normalized, hence only their relative values
are meaningful for the analysis. Also note that all six subfigures are scaled differently, their maximum values
in the y axis are different and must be considered when comparing one cluster to the others.
In the Figure 8, cluster 0 (blue) and cluster 2 (green) appear to be very similar, only differing at
sender, where cluster 0 has values around 0 and cluster has values around 1. Furthermore, values of feature
gas_price on both clusters gather in two groups, one group near zero and another group near six. These two
clusters are the only one exhibiting this trait. Compared to other clusters, the other distinct traits are the very
low values of timestamp, height and gas_consumed.

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Figure 7. Clustering result
Figure 8. Mapping clusters to each feature

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Cluster 1 (green) in particular is more compressed than other clusters, as signified by it is figure’s y
axis max value of 0.5, while other clusters max values are as high as 12. Generally speaking, all features of
this cluster are near zero. Feature nonce, gas_limit, gas_price and gas_consumed are the most compressed,
with all nodes having values nearing 0. feature sender has the most spread-out values, ranging from -1.5 to
0.5 while timestamp and height are somewhere in between.
Cluster 3 (brown) and cluster 5 (red) are actually quite similar even though their general shapes
appear different. Just like most clusters the nodes in these clusters have nonce, gas_limit, gas_price and
gas_consumed values near zero. The rest of the features though are notably different. The sender values of
cluster 3 are more compressed than those of cluster 5. On the contrary, the values of timestamp and height of
cluster 5 are more compressed and on the higher side in comparison to cluster 3.
Cluster 4 is the most distinctive among the six clusters. It is sender, timestamp and height values are
quite similar to other clusters, in that all the values are near zero. But it has multiple groups of values for
gas_price, gas_consumed and gas_limit. Also, its nonce values are the most spread out, ranging from zero to
around 11. Another notable distinction is the multiple appearance of solitary values of the gas_limit feature
which may indicate outlier nodes within the cluster. Determination of the cluster class based on the similarity
of features in the clustering process on the blockchain includes several aspects such as transaction time which
is a significant feature because it can identify at a certain time, transaction size where data grouping is based
on the size of the data to be transferred, transaction security in identifying groups based on security
characteristics (transaction security attributes such as digital signatures).
3.3. Validation result
The following are the results of simulation experiments from scenarios focused on MITM attacks, as
for attacks carried out by changing the value in packet data. Based on the number of validation results in the
training and testing phases of the dataset with 550,000 data samples. The data visualization in Figure 9 shows
that the normal data (represented by blue) is 97.16% and MITM attacks are 2.84% (represented by orange),
which means that normal data amounted to 534,380 data and as much as 15,620 data were MITM attacks.
Confusion matrix usually uses training data to train the proposed model and measure the
performance of the clustering algorithm on the testing data. The following parameters were used to measure
the performance, namely TP, FP, TN and FN. Then, the results of the Confusion Matrix calculation can
measure how accurate the results of the Man in the Middle attack detection. Figure 10 displays the confusion
matrix observations.
For validation purposes, training data of 80% and the testing data of 20% are used, and obtained an
accuracy value of 99.78%. Table 1 shows the confusion matrix using 80% off the testing data. The use
confusion matrix in the use of K-Mean’s method is to show the level of accuracy of the prediction results that
have been done in seeing the accuracy value of the data labeling that has been done.
Figure 9. Visualization data transaction

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Figure 10. Confusion matrix display
Table 1. Confusion matrix using 80% testing data
Measurement Value
True Positive (TP) 8,562
True Negative (TN) 234
False Positive (FP) 1
False Negative (FN) 18
Based on the results obtained using the k-means method which shows the advantages in identifying
patterns and finding data for those tested. This is in accordance with the advantages of k-means, namely
simplicity and efficiency. In addition, k-means is easily applied to large data and has better data computation
time efficiency than other methods, while the disadvantage is that it must determine the initial number of
clusters (k value). In this study, the determination of the initial cluster value (k) uses the silhouette score
technique in clustering.
4. CONCLUSION
The PCA method used in feature extraction from incoming transaction data on the IoT network,
reduces the number of features from 16 to 3 features to build a classification model in the clustering process.
The clustering process with the k-means method implemented on the IoT network was carried out by
performing an extraction process on the MITM attack data types. The results of the clustering analysis using
the k-means method with 6 clusters in the transaction process with a silhouette score were 0.417. The
detected Normal data was 97.16%, while the MITM attacks data was 2.84%. In the future, it is hoped that
newly available datasets on the blockchain can be applied to get different features and characteristics using
the implementation of the GMM clustering method and spherical k-means clustering to see better results and
visualization. Other clustering methods can also be explored, especially methods that are derived from
k-means but with more suitable characteristics to be used with the blockchain dataset.
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BIOGRAPHIES OF AUTHORS
Sari Nuzulastri currently a Master student in Universitas Sriwijaya. She received
her undergraduate degree in the same university, majoring in informatics. Her areas of interest
include internet of things, machine learning, and cyber security. She can be contacted at email:
sari.anhar88@gmail.com.
Deris Stiawan received his Ph.D. degree in Computer Engineering from
Universiti Teknologi Malaysia, Malaysia. He is currently a Professor at Department of
Computer Engineering, Faculty of Computer Science, Universitas Sriwijaya. His research
interests include computer networks, intrusion detection/prevention system, and heterogeneous
networks. He can be contacted at email: deris@unsri.ac.id.
Hadipurnawan Satria received his Ph.D. degree in Computer Science from Sun
Moon University, South Korea. He is currently a Lecturer at the Department of Informatics
Engineering, Faculty of Computer Science, Universitas Sriwijaya. His research interests
include platform-based development, embedded systems, and software engineering. He can be
contacted at email: hadi@ilkom.unsri.ac.id.
Rahmat Budiarto received his Doctor of Engineering in Computer Science from
Nagoya Institute of Technology, Japan in 1998. Currently, he is a full professor at Department
of Computer Science, College of Computing and Information, Albaha University, Saudi
Arabia. His research interests include intelligent systems, brain modeling, IPv6, network
security, wireless sensor networks, and MANETs. He can be contacted at email:
rahmat@bu.edu.sa.

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