UGC Approved Journal no 63975(19)
New UGC Peer-Reviewed Rules

ISSN: 2349-5162 | ESTD Year : 2014
Volume 12 | Issue 10 | October 2025

JETIREXPLORE- Search Thousands of research papers



WhatsApp Contact
Click Here

Published in:

Volume 12 Issue 5
May-2025
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

Unique Identifier

Published Paper ID:
JETIR2505706


Registration ID:
562821

Page Number

g73-g83

Share This Article


Jetir RMS

Title

Cyber Threats Prediction And Analysis Using Machine Learning Algorithms

Abstract

In the ever-shifting terrain of cybersecurity, threat prediction and analysis became central to creating a lateral defense mechanism. The work presents a strong comparison of five machine learning algorithms, viz. Random Forest, Support Vector Machine (SVM), Naive Bayes, XGBoost, and Decision Trees for cyber-threat prediction and classification. The past few years have seen the development and maintenance of a large-scale dataset containing various cyber threat indicators and network behavior patterns. After several iterations and optimizations, the Random Forest seemed to do best with the threat predictive classification of 80.88% accuracy and an F1-score rating of 0.84. The model performed extraordinarily well in classifying high-severity threats because, for some threat categories, the precision increases went up to 95%. On top of that, hyperparameter tuning on the Random Forest model was applied in the study, increasing prediction accuracy and reducing false positives even further. The comparative study also offered some insights regarding training time for Random Forest, XGBoost, and SVM models with Naive Bayes being the fastest at 9.8 seconds with decent accuracy. The study contributes to the incidents by establishing a systematic approach to cyber threat prediction and by giving some hints for practical applications for machine learning-based security solutions.

Key Words

Machine Learning, Cyber Security, Random Forest, Support Vector Machine, XGBoost, Naive Bayes, Decision Trees, Predictive Analytics, Network Security, Feature Importance, Model Optimization, Classification Algorithms, Threat Detection, Cyber Threat Intelligence, Performance Analysis

Cite This Article

"Cyber Threats Prediction And Analysis Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.g73-g83, May-2025, Available :http://www.jetir.org/papers/JETIR2505706.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Cyber Threats Prediction And Analysis Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppg73-g83, May-2025, Available at : http://www.jetir.org/papers/JETIR2505706.pdf

Publication Details

Published Paper ID: JETIR2505706
Registration ID: 562821
Published In: Volume 12 | Issue 5 | Year May-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/jetir.v12i5.562821
Page No: g73-g83
Country: Durgapur, West Bengal, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


Preview This Article


Downlaod

Click here for Article Preview

Download PDF

Downloads

000252

Print This Page

Current Call For Paper

Jetir RMS