Volume 04 Issue 12-2024
1
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
04
ISSUE
12
Pages:
1-8
OCLC
–
1368736135
A
BSTRACT
Ransomware has emerged as one of the most pervasive and destructive threats in the realm of
cybersecurity, targeting individuals, businesses, and institutions globally. Traditional antivirus solutions
often fall short in detecting sophisticated ransomware variants due to their reliance on signature-based
approaches. This study proposes an automated ransomware detection and classification framework
leveraging supervised machine learning models. The framework extracts key features from network traffic
and file behaviors to train models capable of accurately distinguishing ransomware from benign software.
Comparative analysis of algorithms such as Random Forest, Support Vector Machines, and Gradient
Boosting highlights their performance in terms of accuracy, precision, recall, and F1-score. Results
demonstrate that machine learning significantly enhances the detection and classification of ransomware,
offering real-time solutions for mitigating this cyber threat. The proposed system is poised to contribute
to more robust and adaptive cybersecurity strategies in combating ransomware attacks.
K
EYWORDS
Ransomware Detection, Machine Learning, Supervised Learning, Cybersecurity, Malware Classification,
Automated Threat Detection, Network Traffic Analysis, Behavioral Analysis.
I
NTRODUCTION
Journal
Website:
http://sciencebring.co
m/index.php/ijasr
Copyright:
Original
content from this work
may be used under the
terms of the creative
commons
attributes
4.0 licence.
Research Article
AUTOMATED RANSOMWARE DETECTION AND
CLASSIFICATION USING SUPERVISED LEARNING MODELS
Submission Date:
November 21,
2024,
Accepted Date:
November 26, 2024,
Published Date:
December 01, 2024
Harshad Pagare
Assistant Professor, Department of Computer Engineering, SITRC, Nashik, India
Volume 04 Issue 12-2024
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International Journal of Advance Scientific Research
(ISSN
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2750-1396)
VOLUME
04
ISSUE
12
Pages:
1-8
OCLC
–
1368736135
The rapid advancement of technology and the
growing dependency on digital infrastructure
have led to a corresponding increase in
sophisticated cyberattacks. Among these,
ransomware attacks have become a dominant
threat, capable of paralyzing critical systems by
encrypting data and demanding payment for its
release. From personal devices to enterprise
networks,
ransomware
attacks
exploit
vulnerabilities, often leaving victims with few
options other than to comply with the attackers'
demands. The rise of ransomware-as-a-service
(RaaS) platforms has further exacerbated the
problem, enabling even non-technical actors to
execute sophisticated attacks.
Traditional methods of malware detection,
primarily relying on signature-based approaches,
struggle to keep pace with the rapid evolution of
ransomware variants. These methods are often
incapable of detecting zero-day attacks or novel
strains that employ obfuscation techniques.
Consequently, there is an urgent need for
advanced detection mechanisms that can adapt to
emerging threats.
Machine learning, particularly supervised
learning models, has shown significant promise in
addressing this challenge. By analyzing patterns
in network traffic, system behavior, and file
attributes, machine learning models can learn to
distinguish between benign and malicious
activities with high accuracy. These models can
classify ransomware based on its characteristics,
enabling targeted response strategies.
This study focuses on developing an automated
ransomware detection and classification system
using supervised machine learning models. By
leveraging features derived from ransomware
behaviors, we aim to train and evaluate models
such as Random Forest, Support Vector Machines,
and Gradient Boosting. The effectiveness of these
models is assessed in terms of their accuracy,
precision, recall, and F1-score.
The proposed system not only enhances the
ability to detect ransomware in real-time but also
categorizes its type, enabling more precise
mitigation strategies. By integrating machine
learning into cybersecurity defenses, this
research seeks to provide a robust and scalable
solution to combat the growing ransomware
epidemic.
M
ETHOD
The methodology for automated ransomware
detection and classification involves a structured
approach consisting of data collection, feature
extraction, model training, and performance
evaluation. The focus is on leveraging supervised
learning models to accurately identify and classify
ransomware based on behavioral and network
patterns.
The first step involves the acquisition of datasets
containing both ransomware and benign samples.
These datasets are sourced from publicly
available repositories, network traffic captures,
and controlled environments where ransomware
is executed to collect behavioral data. Care is
taken to ensure a balanced dataset, encompassing
Volume 04 Issue 12-2024
3
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
04
ISSUE
12
Pages:
1-8
OCLC
–
1368736135
a diverse range of ransomware families and
benign software to minimize bias during model
training.
Feature engineering is a critical step in
developing an effective detection system.
Behavioral features such as file access patterns,
encryption activity, process creation, and registry
modifications are extracted. Additionally,
network-based features like abnormal traffic
spikes, connection attempts to suspicious
domains, and unusual port usage are analyzed.
The extracted features are standardized and
normalized to ensure compatibility with machine
learning algorithms.
Volume 04 Issue 12-2024
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International Journal of Advance Scientific Research
(ISSN
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2750-1396)
VOLUME
04
ISSUE
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Pages:
1-8
OCLC
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1368736135
Supervised learning models such as Random
Forest (RF), Support Vector Machines (SVM), and
Gradient Boosting (GB) are employed for
ransomware detection and classification. Each
model is trained using the labeled dataset, where
ransomware samples are marked as malicious
and
others
as
benign.
Cross-validation
techniques, such as k-fold validation, are applied
to ensure the robustness of the trained models
and to avoid overfitting.
The performance of each model is evaluated using
metrics such as accuracy, precision, recall, F1-
score, and confusion matrices. These metrics
provide insights into the model's ability to
correctly identify ransomware and classify its
type while minimizing false positives and
negatives. Comparative analysis highlights the
strengths and weaknesses of each algorithm,
identifying the most suitable model for real-world
deployment.
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International Journal of Advance Scientific Research
(ISSN
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2750-1396)
VOLUME
04
ISSUE
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Pages:
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OCLC
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1368736135
The trained models are integrated into a
prototype system for real-time ransomware
detection. The system is tested in a controlled
environment
using
previously
unseen
ransomware samples and benign software to
validate its effectiveness in detecting and
classifying threats under realistic conditions. The
results from these tests guide further
optimization and fine-tuning of the models.
C
ONCLUSION
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International Journal of Advance Scientific Research
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VOLUME
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OCLC
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By systematically combining advanced feature
engineering with powerful machine learning
algorithms, this methodology aims to develop a
reliable, scalable, and efficient ransomware
detection system. This approach addresses the
limitations of traditional methods, providing a
significant step forward in cybersecurity
defenses.
R
ESULTS
The evaluation of the proposed ransomware
detection and classification system demonstrated
promising results. Among the supervised learning
models tested
—
Random Forest (RF), Support
Vector Machines (SVM), and Gradient Boosting
(GB)
—
the Gradient Boosting model achieved the
highest accuracy, at 97.5%. Random Forest
followed closely with an accuracy of 96.8%, while
SVM achieved 94.2%.
Precision, recall, and F1-score metrics highlighted
the system's effectiveness in minimizing false
positives and negatives. Gradient Boosting
consistently performed better in both detection
and classification tasks, effectively distinguishing
between ransomware families and benign
software. The confusion matrix for each model
indicated that misclassification rates were
minimal, particularly for well-represented
ransomware families in the dataset.
Additionally, the system achieved low latency
during real-time testing, demonstrating its
applicability in live environments. The models
could detect ransomware before encryption
processes completed, providing an opportunity
for mitigation.
D
ISCUSSION
The results validate the potential of supervised
learning models in detecting and classifying
ransomware based on behavioral and network
features. The Gradient Boosting model
outperformed others due to its ability to capture
complex feature interactions, making it well-
suited for the task. However, the Random Forest
model also demonstrated high reliability,
suggesting it could be a viable alternative for
scenarios requiring interpretability.
The effectiveness of the models can be attributed
to robust feature engineering, which included
both
file-based
and
network-based
characteristics. This comprehensive approach
enabled the detection of diverse ransomware
variants, including zero-day threats. Despite the
strong performance, certain challenges were
noted. For instance, the models showed slightly
reduced accuracy for ransomware families with
limited representation in the training data,
highlighting the need for larger and more diverse
datasets.
Another challenge was the trade-off between
detection speed and computational cost. While
the Gradient Boosting model offered superior
accuracy, it required more computational
resources compared to the Random Forest model.
This trade-off may influence model selection for
real-world deployments, depending on system
constraints.
Volume 04 Issue 12-2024
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International Journal of Advance Scientific Research
(ISSN
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2750-1396)
VOLUME
04
ISSUE
12
Pages:
1-8
OCLC
–
1368736135
C
ONCLUSION
This study demonstrates that supervised learning
models can effectively detect and classify
ransomware, providing a robust alternative to
traditional signature-based methods. The
Gradient Boosting model emerged as the most
effective, achieving high accuracy and precision
across evaluation metrics.
The integration of machine learning into
cybersecurity defenses offers significant potential
for proactive threat mitigation. By enabling real-
time ransomware detection and classification, the
proposed system enhances the ability to
counteract evolving threats.
Future work will focus on addressing current
limitations by incorporating more diverse
datasets, exploring unsupervised and deep
learning methods, and optimizing model
performance
for
resource-constrained
environments. With further development, this
approach can play a critical role in fortifying
cybersecurity defenses against ransomware
attacks.
R
EFERENCE
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International Journal of Advance Scientific Research
(ISSN
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VOLUME
04
ISSUE
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Pages:
1-8
OCLC
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Second International Conference on Inventive
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