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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: March 2026
Volume 11 | Issue 3
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21574
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Total Authors : 22430
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| Paper Title: | Performance Analysis of Machine Learning Algorithms for Cyber Attack Prediction |
| Authors Name: | Sagar Makude , Ram Suryawanshi |
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IJRTI_207788
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| Published Paper Id: | IJRTI2511157 |
| Published In: | Volume 10 Issue 11, November-2025 |
| DOI: | |
| Abstract: | Cyber attacks pose an escalating threat to digital infrastructure, with global economic losses exceeding $8 trillion annually. This paper presents a comprehensive comparative analysis of machine learning algorithms for predicting cyber attacks using network traffic data. We evaluate supervised algorithms (Logistic Regression, Decision Tree, and Random Forest) and unsupervised approaches (Isolation Forest and Autoencoders) on a dataset of 500,000 network logs from diverse attack scenarios, incorporating 15 feature variables including packet size, connection frequency, protocol type, and behavioral indicators. Experimental results demonstrate that Random Forest achieves the highest prediction accuracy of 92.5%, with precision of 91.8%, recall of 93.2%, and F1-score of 92.5%. Decision Tree achieves 87.3% accuracy, while Logistic Regression attains 84.1%. Feature importance analysis reveals that packet size, connection frequency, and protocol type are the most significant predictors of attack likelihood. The proposed hybrid framework provides actionable insights for cybersecurity systems to enable proactive threat detection and mitigation, reducing response times and enhancing network resilience through early intervention strategies. |
| Keywords: | Cyber attack prediction, machine learning, classification algorithms, network security, intrusion detection, Random Forest, Decision Tree, Logistic Regression, unsupervised learning |
| Cite Article: | "Performance Analysis of Machine Learning Algorithms for Cyber Attack Prediction", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.b495-b502, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511157.pdf |
| Downloads: | 000213 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2511157
Registration ID:207788
Published In: Volume 10 Issue 11, November-2025
DOI (Digital Object Identifier):
Page No: b495-b502 Country: latur, Maharashtra, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2511157 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2511157 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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