Anomaly based intrusion detection using ensemble machine learning and block-chain

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 13, No. 3, September 2024, pp. 2754~2762
ISSN: 2252-8938, DOI: 10.11591/ijai.v13.i3.pp2754-2762  2754
Journal homepage: http://ijai.iaescore.com
Anomaly based intrusion detection using ensemble machine
learning and block-chain
Mekala Srinivasa Rao, Shaik Nazma, Kumbhagiri Nava Chaitanya, Thota Ambica
Department of Computer Science and Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram, India
Article Info ABSTRACT
Article history:
Received Jan 10, 2024
Revised Feb 20, 2024
Accepted Mar 4, 2024
A major issue facing the quickly evolving technological world is the surge in
security concerns, particularly for critical internet of things (IoT)
applications like health care and the military. Early security attack detection
is crucial for safeguarding important resources. Our research focuses on
developing an anomaly-based intrusion detection system (IDS) using
machine learning (ML) models. With the use of voting strategies, bagging
ensemble, boosting ensemble, and random forest, we created a robust and
long-lasting IDS. The F1 score is a crucial metric for measuring accurate
predictions at the class level and serves as the focus of these ML systems.
Maintaining a high F1 score in critical applications highlights the constant
need for development. Make use of the latest Canadian Institute for
cybersecurity internet of things (CICIoT) 2023 dataset employ hyperledger
fabric to create a private channel in order to bolster the security of our IDS
through the usage of block-chain technology. We use block-chain's
immutable record and cryptographic techniques to establish a decentralized,
tamper-proof environment. Consequently, our proposed approach provides
an efficient IDS that significantly enhances resource protection and alerting
the user in prior with intruder information incritical regions for IoT security
applications.
Keywords:
Block-chain technology
Hyper-ledger fabric
Intrusion detection system
Machine learning
Random forest
This is an open access article under the CC BY-SA license.
Corresponding Author:
Shaik Nazma
Department of Computer Science and Engineering, Lakireddy Bali Reddy College of Engineering
Mylavaram, Andhra Pradesh, India
Email: sknazma2003@gmail.com
1. INTRODUCTION
Rapid technological innovation brings with it a serious challenge: a rising threat posed by increased
security vulnerabilities. This is especially true in the internet of things (IoT), where networked gadgets play
important roles in critical areas such as national security and healthcare. Cyber assaults in this setting might
have serious consequences, emphasizing the fundamental need of early intrusion detection in securing
important resources. In response to this requirement, our study focuses on the creation of a robust
anomaly-based intrusion detection system (IDS) using machine learning (ML) [1]. Our focus is on
developing an adaptive IDS using ensemble methodologies such as bagging, boosting, and random forest
ensemble [2]. The F1 score, a critical indicator for exact class-level predictions, especially in high-stakes
scenarios, guides the evaluation of these models. Recognizing the importance of the IoT security
environment, we investigate the use of blockchain technology to strengthen our IDS. Our objective is to
create a decentralized, tamper-proof ecosystem by exploiting the secure private channel provided by
hyperledger fabric. The intrinsic characteristics of blockchain, such as cryptographic security and immutable
record-keeping, improve the dependability of our suggested method.

Int J Artif Intell ISSN: 2252-8938 
Anomaly based intrusion detection using ensemble machine learning and … (Mekala Srinivasa Rao)
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Essentially, our study tackles the critical need for improved security in the linked world of IoT. We
foresee an IDS that is not only more resilient but also more effective, providing enhanced protection for
essential resources in crucial industries by combining robust ML models with the unprecedented security of
blockchain technology. To back up our findings, we used the Canadian Institute for cybersecurity internet of
things (CICIoT) 2023 dataset [3], which has a complete depiction of real-world IoT assaults. This dataset
contains a variety of attack methods aimed at 105 real-world IoT devices divided into seven categories. Its
inclusion of attack types [4] not often seen in other datasets, along with the collection of benign IoT traffic in
both idle and active stages, makes it important for testing the efficacy of ML algorithms in constructing
strong IDS. In order to put our findings into practice, we used the CICIOT-2023 dataset and thorough data
preparation. Handling null values, environmentally responsible data training, and a complete set of methods
spanning data purification, integration, feature selection, transformation, normalization/scaling, categorical
value handling, and computational overhead reduction were all part of the process. Following that, the data is
trained using ML algorithms [5], [6] and ensemble techniques such as random forest, adaptive boosting
(AdaBoost), bootstrap aggregating (Bagging), and voting ensemble. The examination of performance
measures comprises target classes, accuracy, recall, F1 score, and support.
The combination of ML methods and blockchain technologies to address cyber threats [7], [8] in the
IoT area is a novel path worth further investigation. While blockchain has many advantages, its implementation
is fraught with difficulties such as storage restrictions, scalability limitations, and vulnerabilities. Our solution
offers heightened situational awareness and supports proactive efforts to neutralize and resolve any security
breaches by rapidly alerting end-users of detected dangers via email notifications. This integrated alerting
system is crucial in improving the overall efficacy of the IDS [9], resulting in a more responsive and adaptable
security framework for protecting vital resources in IoT applications. In conclusion, our research provides a
complete method to resolving the ever-changing difficulties of IoT security. We want to contribute to the
creation of a more robust and effective IDS by integrating sophisticated ML models with blockchain
technology, therefore bolstering the security of important resources in crucial industries.
This research expands on earlier investigations into the difficulties and weaknesses present in IoT
systems. According to Narayan et al. [10], strong security solutions designed for IoT applications are
essential. The goal of their work was to improve current IDS by integrating ML techniques and potentially
ensemble methodologies to adapt to the dynamic nature of IoT landscapes. Specifically, they worked on
developing an intelligent intrusion detection system (IIDS) tailored for IoT environments. Similar to this,
Alsharif et al. [11] suggested a novel strategy for resolving IoT security issues by utilizing blockchain
technology and ML techniques. Their work aims to support IDS in IoT environments by leveraging ML to
improve flexibility and accuracy. Additionally, blockchain is introduced to offer a decentralized, tamper-
proof framework for validating intrusion-related data. Further, Khonde and Ulagamuthalvi [12] combined
blockchain technology with a hybrid IDS to present a revolutionary cybersecurity strategy. Their hybrid
approach used a blockchain architecture to guarantee the integrity and immutability of data related to
intrusions with several detection techniques to increase overall accuracy.
The research presented here adds to the rapidly developing subject by providing a thorough
approach to deal with the constantly changing problems associated with IoT security. We want to build a
more reliable and efficient IDS by fusing advanced ML models with blockchain technology [13]. This will
improve the security of vital resources in important businesses. In addition to improving situational
awareness, our method facilitates proactive steps to reduce security breaches, guaranteeing a more flexible
and responsive security framework for IoT applications.
The CICIoT2023 dataset is a comprehensive resource for IoT security research and development. It
provides real-time network traffic [10] data collected from a large network of 105 different IoT devices,
simulating 33 assaults [14], [15] over seven primary categories. The categories are i) distributed denial-of-
service (DDoS): flooding target systems with traffic to cause service disruption, ii) denial-of-service (DoS):
depleting certain device resources to make them unavailable, iii) reconnaissance: information gathering about
the network and its devices in preparation for future assaults, iv) online based: taking advantage of flaws in
online apps or services operating on IoT devices, v) brute-force: repeated trial-and-error login attempts to
acquire illegal access, vi) spoofing: the practice of forging source or destination addresses in order to trick
other devices or systems, and vii) mirai Botnet: using vulnerabilities to infect devices and establish a botnet
for coordinated attacks.
2. METHOD
2.1. A voting ensemble approach for enhancing intrusion detection
In order to improve the effectiveness of our IDS, we used a voting ensemble approach that combines
the predictive capability of three well-known ML algorithms: random forest, Bagging, and AdaBoost
(Figure 1). A voting ensemble is used to aggregate the individual predictions from these several algorithms

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Int J Artif Intell, Vol. 13, No. 3, September 2024: 2754-2762
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and determine the final classification based on a majority vote. This strategy tries to leverage on each
algorithm's strengths while limiting its particular flaws, ultimately improving the overall robustness and
accuracy of the IDS. Each algorithm, random forest, Bagging, and AdaBoost, contributes distinct benefits to
the ensemble, resulting in a more complete and resilient detection method. Our ensemble technique aims to
achieve superior performance in recognizing and mitigating security vulnerabilities inside IoT systems by
leveraging the CICIoT-2023 dataset, which contains a broad range of real-world IoT assaults.
Figure 1. Voting ensemble of the input dataset
2.1.1. The voting ensemble's mechanics
The proposed voting ensemble uses the three algorithms that are random forest, bagging ensemble,
and AdaBoost ensemble to determine the final classification.
‒ Random forest: known for its capacity to handle complicated datasets and minimize overfitting, random
forest adds a wide variety of decision trees to the ensemble, increasing its resistance to changing data
trends and possible outliers.
‒ Bagging: with an emphasis on variance reduction and better stability, Bagging delivers an ensemble of
models trained on distinct subsets of the dataset, adding to the IDS overall generalization performance.
‒ AdaBoost: AdaBoost iteratively modifies the weights of misclassified examples to increase the model's
capacity to recognize subtle patterns and improve detection accuracy.
Our ensemble's synergy of random forest, Bagging, and AdaBoost attempts to develop a more
comprehensive and resilient detection approach. The distinct qualities of each algorithm contribute to the
ensemble's combined competency, delivering a robust and diverse intrusion detection capacity. We use the
CICIoT-2023 dataset to thoroughly test and optimize our proposed model. This dataset, which includes a
wide range of real-world IoT attacks, provides a solid testing ground for evaluating the ensemble's
effectiveness in detecting and mitigating security vulnerabilities in IoT devices. The core of the voting
ensemble is its capacity to combine the many views provided by random forest, Bagging, and AdaBoost. The
ensemble strategy harnesses the aggregate wisdom of these algorithms by allowing each algorithm to cast its
"vote" based on its own forecast. The majority voting process enables a definite and strong final
categorization in times of uncertainty or opposing forecasts. Our voting ensemble technique is a complex
integration mechanism that maximizes the predictive capacity of various algorithms to strengthen the IDS's
overall efficacy. This method not only highlights the virtues of random forest, Bagging, and AdaBoost, but it
also highlights the promise of ensemble approaches in enhancing intrusion detection capabilities within the
complex environment of IoT security.
2.1.2. Improving IoT security with blockchain integration
We use blockchain technology, specifically hyperledger fabric [16], to improve network security.
Within this fabric network, we construct a channel between two organizations that are permitted to interact
[17]. It is important to note that only users from these organizations can create transactions and add them to
the blockchain [18]. If an intrusion occurs, the peer who detects the attack with help of ML model, is
responsible for creating a transaction that includes the intruder's signature [19]. The immutability of this
transaction throughout the blockchain means that all network users may quickly view the intruder's signature,
essentially advertising the threat [20], [21]. An automatic alert email with the intruder's data is delivered to
every network user for added awareness, boosting the security response. This multi-layered method, which
Dataset (CICIoT-2023)
Random Forest AdaBoost Ensemble
Bagging Ensemble
Voting Ensemble

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combines real-time blockchain updates with proactive email notifications, dramatically improves network
security by quickly notifying all users of possible attacks and the accompanying consequences [22], [23].
2.1.3. Project workflow
The procedure begins with the ML model inspecting incoming data and discriminating between
normal and abnormal network behavior. When an intrusion is detected, the system creates a transaction that
contains essential facts about the assault. These transactions are arranged into a block on the hyperledger
fabric blockchain, maintaining the consolidated record's integrity and transparency. Authorized entities safely
propagate the blocks over the network via a private channel for transaction creation capabilities, providing
real-time insights into possible risks to all network users [24] as shown in Figure 2.
Figure 2. Workflow of proposed system
3. RESULTS AND DISCUSSION
As a key component of our IDS, the given dataset is normalized using standardization as shown in
(1) then we used a hard voting ensemble technique in our project. To reach a final conclusion on the
categorization of a particular instance, this approach aggregates predictions from separate models within the
ensemble, namely random forest, Bagging, and AdaBoost. In hard voting, the class with the most votes from
the individual models is chosen as the ultimate forecast [25]. If M represents the collection of individual
models, ci represents the projected class by model i, and y represents the final prediction, the hard voting
process may be stated mathematically as shown in (2). After the hard voting process the result output class
performance is evaluated using precision, recall and F1 score as shown in (3)-(5). The resultant values are
depicted in Figure 3.
‒ Data normalization (standardization):
Z =
𝑋−µ
𝜎
(1)
Where z is standardized value, x is original value, μ is mean of the data, and σ is standard deviation of the
data.
‒ Final prediction (y)
y = arg maxc(∑i ∈ M1(ci = c)) (2)
where c is each class in classification problem and M is number of individual models.
No
attack
Yes
Attack
Create a
block
Build a transaction
Identify the type of
attack
Block is distributed
across the network
Data
Transaction:-
(IP address,
Type of attack)
Is attack occured

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‒ Precision, sensitivity or true positive rate (recall), and F1 score
Precision =
TP
TP+FP
(3)
Recall =
TP
TP+FN
(4)
F1 − Score =
2∗Precision∗Recall
Precision+Recall
(5)
The analysis of performanse metrics of proposed model is shown in Table 1. Here we can see about
the evaluation of performance of a proposed model against an existing one using standard metrics such as
accuracy, precision, recall, and F1 score. The proposed model consistently outperforms the existing one
across all metrics as shown in Figure 3, indicating its potential for enhancing classification accuracy and
reliability. The performance metrics of used ML for each attack when intruders are present is shown in
Figure 4. The generated F1 score of proposed trained ML model and existing model is shown in Figure 5.
Figure 3. Comparision between existing system and proposed system
Table 1. Analysis of performance metrics
Model Accuracy Precision Recall F1 score
Random forest 98.5 91 76 79
Bagging ensemble 97 93 72 81
AdaBoost 96 80 73 75
Voting ensemble 99 95 78 89

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Figure 4. Performance metrics of each attack of used ML model
Figure 5. F1 score comparison for existing and proposed method
We created a static web page that extracts data from a dataset using important parameters from the
supplied dataset as shown in Figure 6. The proposed trained ML model will predict the class of the attack
when the intruders present and shows the intruders data as shown in Figure 7. The intrusion data is stored in
the blockchain network using CouchDB as shown in Figure 8. An email will be send to the user to help the
user discover suspicious behavior in the data as shown in Figure 9.

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Figure 6. Extraction of data from input dataset Figure 7. Prediction of intruder data
Figure 8. Storage of data in blockchain using couch database

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Figure 9. Email alert send to user
4. CONCLUSION
Finally, this study addresses the important security challenges in IoT applications, particularly in
crucial industries such as health care and military. Using advanced ML models, we created an effective IDS
and produced encouraging results, setting the framework for future advancements. The use of block-chain
technology improves system security by creating a decentralized and tamper-proof environment. Overall, our
method represents an important advancement in IoT security, with a priority on resource protection and
delivering a robust solution for a growing technological context. Further more,we aim to enhance detection
accuracy by integrating deep learning with ensemble ML, refine hyperparameters, and implement real-time
monitoring for proactive threat response. We'll also optimize scalability, explore privacy techniques, enhance
resilience against attacks, deploy IDS in edge computing, and collaborate for standardization to advance IoT
security.
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BIOGRAPHIES OF AUTHORS
Mekala Srinivasa Rao working as professor in Department of Computer Science
and Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh,
India. He received his B.Tech. Computer Science and Engineering from Nagarjuna University
in 1998. He completed M.Tech. in Software Engineering from JNT University, Hyderabad in
2001. He received Ph.D. degree from JNTUK Kakinada in 2018. He is having nearly 21 years
of teaching and industrial experience. He published 25 papers in various conferences and
journals. His current research areas are IoT, blockchain, AI, and data science. He can be
contacted at email: srinu.mekala@gmail.com.
Shaik Nazma currently pursuing her bachelor’s degree in the field of Computer
Science and Engineering, at Lakireddy Bali Reddy College of Engineering, Mylavaram,
Andhra Pradesh, India. She has done many projects as a part of her B.Tech. curriculum. She
has contributed her best in her academics. She can be contacted at email:
sknazma2003@gmail.com.
Kumbhagiri Nava Chaitanya currently pursuing his bachelor’s degree in the
field of Computer Science and Engineering, at Lakireddy Bali Reddy College of Engineering,
Mylavaram, Andhra Pradesh, India. He has done many projects as a part of his B.Tech.
curriculum. He had contributed his best in his academics. He can be contacted at email:
navachaitanya.kumbhagiri@gmail.com.
Thota Ambica currently pursuing her bachelor’s degree in the field of Computer
Science and Engineering, at Lakireddy Bali Reddy College of Engineering, Mylavaram,
Andhra Pradesh, India. She has done many projects as a part of her B.Tech. curriculum. She
has contributed her best in her academics. She can be contacted at email:
ambicat2003@gmail.com.

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