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Title: :  Prediction of Cyber Security Attacks Using Data Science Techniques: A Comparative Study of Machine Learning Algorithms
PaperId: :  27136
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 4 2025
DUI:    16.0415/IJARIIE-27136
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Gowtham S CMR university

Abstract

computer engineering
Cybersecurity, Machine Learning, Intrusion Detection, Attack Prediction, Random Forest, Classification Models
In today’s interconnected digital world, cybersecurity threats are becoming more frequent, complex, and adaptive—often outpacing traditional security measures. Static, rule-based systems, such as signature-based intrusion detection, are increasingly unable to detect novel or evolving attack patterns. This research addresses the need for more intelligent, data-driven solutions by investigating how machine learning algorithms can be used to anticipate and classify different forms of cyber attacks. The study centers on four common and impactful attack types: Denial of Service (DoS), Remote to Local (R2L), User to Root (U2R), and Malware. Using a carefully curated dataset of 40,000 network activity records, each with 25 distinct features, we evaluate the predictive performance of four widely used machine learning models—Logistic Regression, Decision Tree, Random Forest, and Support Vector Classifier (SVC). Our methodology includes robust data preprocessing, thoughtful feature engineering, and comprehensive model evaluation using performance metrics such as accuracy, precision, recall, F1-score, sensitivity, and specificity. Among the evaluated models, Random Forest emerged as the most reliable performer overall, although each algorithm demonstrated strengths depending on the specific attack type. These insights can guide the development of more effective and responsive intrusion detection systems, ultimately contributing to a stronger cybersecurity posture across digital infrastructures.

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IJARIIE Gowtham S . "Prediction of Cyber Security Attacks Using Data Science Techniques: A Comparative Study of Machine Learning Algorithms" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 4 2025 Page 3778-3783
MLA Gowtham S . "Prediction of Cyber Security Attacks Using Data Science Techniques: A Comparative Study of Machine Learning Algorithms." International Journal Of Advance Research And Innovative Ideas In Education 11.4(2025) : 3778-3783.
APA Gowtham S . (2025). Prediction of Cyber Security Attacks Using Data Science Techniques: A Comparative Study of Machine Learning Algorithms. International Journal Of Advance Research And Innovative Ideas In Education, 11(4), 3778-3783.
Chicago Gowtham S . "Prediction of Cyber Security Attacks Using Data Science Techniques: A Comparative Study of Machine Learning Algorithms." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 4 (2025) : 3778-3783.
Oxford Gowtham S . 'Prediction of Cyber Security Attacks Using Data Science Techniques: A Comparative Study of Machine Learning Algorithms', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 4, 2025, p. 3778-3783. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Prediction_of_Cyber_Security_Attacks_Using_Data_Science_Techniques__A_Comparative_Study_of_Machine_Learning_Algorithms_ijariie27136.pdf (Accessed : ).
Harvard Gowtham S . (2025) 'Prediction of Cyber Security Attacks Using Data Science Techniques: A Comparative Study of Machine Learning Algorithms', International Journal Of Advance Research And Innovative Ideas In Education, 11(4), pp. 3778-3783IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Prediction_of_Cyber_Security_Attacks_Using_Data_Science_Techniques__A_Comparative_Study_of_Machine_Learning_Algorithms_ijariie27136.pdf (Accessed : )
IEEE Gowtham S , "Prediction of Cyber Security Attacks Using Data Science Techniques: A Comparative Study of Machine Learning Algorithms," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 4, pp. 3778-3783, Jul-Aug 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/Prediction_of_Cyber_Security_Attacks_Using_Data_Science_Techniques__A_Comparative_Study_of_Machine_Learning_Algorithms_ijariie27136.pdf [Accessed : ].
Turabian Gowtham S . "Prediction of Cyber Security Attacks Using Data Science Techniques: A Comparative Study of Machine Learning Algorithms." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 4 ().
Vancouver Gowtham S . Prediction of Cyber Security Attacks Using Data Science Techniques: A Comparative Study of Machine Learning Algorithms. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(4) : 3778-3783. Available from: https://ijariie.com/AdminUploadPdf/Prediction_of_Cyber_Security_Attacks_Using_Data_Science_Techniques__A_Comparative_Study_of_Machine_Learning_Algorithms_ijariie27136.pdf
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