Anomaly Detection in IoT Network by using Multi-class Adaptive Boosting Classifier

Pandit Byomakesha Dash; K. Srinivasa Rao

Anomaly Detection in IoT Network by using Multi-class Adaptive Boosting Classifier

Abstract

Detection of anomaly and attack identification is some of the major concern in IoT domain in recent days. With the exponential use of IoT based infrastructure in every domain, threats and anomalies are amplifying adequately. Attacks such as malicious operations, spying, service denial etc. are the main cause for failure in IoT system. So, developing an efficient model to identify and decipher such complex problem is always been a challenging task. Rather some of the machine learning based models is developed to solve such problem, but due to highly nonlinear nature of the data, such methods seem to be failed to prove the efficacy. With the combination of several models, ensemble learning helps to enhance the performance of machine learning methods. As compared to any single method, the ensemble learning based models are highly predictable for large dimensional data. In this paper, an adaptive boosting based model has been proposed to identify the anomaly in IoT based environment. The performance of the proposed method is compared with several other competitive machine learning based methods and found to be superior with all the considered metrics.

Keywords

IoT Security, Anomaly Detection, Adaptive Boosting, Ensemble Learning

References

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Details

Primary Language

English

Subjects

-

Journal Section

-

Authors

Pandit Byomakesha Dash This is me

K. Srinivasa Rao This is me

Publication Date

September 1, 2020

Submission Date

-

Acceptance Date

-

Published in Issue

Year 2020 Volume: 9 Number: 3

IZ

https://izlik.org/JA82LP45FU

Cite

RIS / Bibtex

APA

Dash, P. B., & Rao, K. S. (2020). Anomaly Detection in IoT Network by using Multi-class Adaptive Boosting Classifier. International Journal of Information Security Science, 9(3), 164-171. https://izlik.org/JA82LP45FU

AMA

1.Dash PB, Rao KS. Anomaly Detection in IoT Network by using Multi-class Adaptive Boosting Classifier. IJISS. 2020;9(3):164-171. https://izlik.org/JA82LP45FU

Chicago

Dash, Pandit Byomakesha, and K. Srinivasa Rao. 2020. “Anomaly Detection in IoT Network by Using Multi-Class Adaptive Boosting Classifier”. International Journal of Information Security Science 9 (3): 164-71. https://izlik.org/JA82LP45FU.

EndNote

Dash PB, Rao KS (September 1, 2020) Anomaly Detection in IoT Network by using Multi-class Adaptive Boosting Classifier. International Journal of Information Security Science 9 3 164–171.

IEEE

[1]P. B. Dash and K. S. Rao, “Anomaly Detection in IoT Network by using Multi-class Adaptive Boosting Classifier”, IJISS, vol. 9, no. 3, pp. 164–171, Sept. 2020, [Online]. Available: https://izlik.org/JA82LP45FU

ISNAD

Dash, Pandit Byomakesha - Rao, K. Srinivasa. “Anomaly Detection in IoT Network by Using Multi-Class Adaptive Boosting Classifier”. International Journal of Information Security Science 9/3 (September 1, 2020): 164-171. https://izlik.org/JA82LP45FU.

JAMA

1.Dash PB, Rao KS. Anomaly Detection in IoT Network by using Multi-class Adaptive Boosting Classifier. IJISS. 2020;9:164–171.

MLA

Dash, Pandit Byomakesha, and K. Srinivasa Rao. “Anomaly Detection in IoT Network by Using Multi-Class Adaptive Boosting Classifier”. International Journal of Information Security Science, vol. 9, no. 3, Sept. 2020, pp. 164-71, https://izlik.org/JA82LP45FU.

Vancouver

1.Pandit Byomakesha Dash, K. Srinivasa Rao. Anomaly Detection in IoT Network by using Multi-class Adaptive Boosting Classifier. IJISS [Internet]. 2020 Sep. 1;9(3):164-71. Available from: https://izlik.org/JA82LP45FU