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Machine Learning DDoS Detection Using Stochastic Gradient Boosting

M Devendra Prasad1 , Prasanta Babu V2 , C Amarnath3

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-4 , Page no. 157-166, Apr-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i4.157166

Online published on Apr 30, 2019

Copyright © M Devendra Prasad, Prasanta Babu V, C Amarnath . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: M Devendra Prasad, Prasanta Babu V, C Amarnath, “Machine Learning DDoS Detection Using Stochastic Gradient Boosting,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.157-166, 2019.

MLA Style Citation: M Devendra Prasad, Prasanta Babu V, C Amarnath "Machine Learning DDoS Detection Using Stochastic Gradient Boosting." International Journal of Computer Sciences and Engineering 7.4 (2019): 157-166.

APA Style Citation: M Devendra Prasad, Prasanta Babu V, C Amarnath, (2019). Machine Learning DDoS Detection Using Stochastic Gradient Boosting. International Journal of Computer Sciences and Engineering, 7(4), 157-166.

BibTex Style Citation:
@article{Prasad_2019,
author = {M Devendra Prasad, Prasanta Babu V, C Amarnath},
title = {Machine Learning DDoS Detection Using Stochastic Gradient Boosting},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2019},
volume = {7},
Issue = {4},
month = {4},
year = {2019},
issn = {2347-2693},
pages = {157-166},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4011},
doi = {https://doi.org/10.26438/ijcse/v7i4.157166}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i4.157166}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4011
TI - Machine Learning DDoS Detection Using Stochastic Gradient Boosting
T2 - International Journal of Computer Sciences and Engineering
AU - M Devendra Prasad, Prasanta Babu V, C Amarnath
PY - 2019
DA - 2019/04/30
PB - IJCSE, Indore, INDIA
SP - 157-166
IS - 4
VL - 7
SN - 2347-2693
ER -

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Abstract

DDoS (Distributed Denial of service) attacks emerge as the most devastating attacks of all time for organizations and ISPs of all sizes. The increasing availability of DDoS-for-hire services and the proliferation of billions of Unsecured IoT devices and botnets contributed to a significant increase in DDoS attacks. These attacks continue to grow in magnitude, frequency, and sophistication. The legacy methods like signature-based detection and scrubbing are challenged, as attacks are growing smarter day by day and evading IDS. The next-generation security technologies also cannot keep pace with the scale of attacks targeting organizations. Even anomaly-based detection is suffering from many limitations with accuracy and false positives by demanding human intervention. This is our attempt to obviate manual analysis in anomaly-based DDoS detection by achieving perfect accuracy with zero misclassifications. In this paper, we demonstrated DDoS anomaly detection on the open CIC datasets using Stochastic Gradient Boosting (SGB) machine learning (ML) model. Using this ML model and by meticulously tuning hyperparameters, we achieved maximum accuracy and compared the results with other machine learning algorithms.

Key-Words / Index Term

DDOS attacks, anomaly detection, machine learning, stochastic gradient boosting, scikit-learn, XGBOOST

References

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