Abstract
To classify sensor data correctly and quickly has a very sound impact on areas such as performance monitoring, user behavior analysis, and user accounting and intrusion detection in IoT (Internet of things). This work is an approach to reorganize the features in a dataset of 114 features depending on the relevancy and non-redundancy of an attribute or feature. Thus having a minimal set of 30 features can create a linear SVM classifier model to detect TCP attack in IoT botnet attack traffic.
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Notes
- 1.
https://arstechnica.com/, accessed on 21 November 2019.
- 2.
https://arstechnica.com/, accessed on 21 November 2019.
- 3.
https://archive.ics.uci.edu/ml/index.php, accessed on 21 November 2019.
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Deka, R.K., Kalita, K.P., Bhattacharyya, D.K., Boro, D. (2021). A Smart Feature Reduction Approach to Detect Botnet Attack in IoT. In: Bora, P.K., Nandi, S., Laskar, S. (eds) Emerging Technologies for Smart Cities. Lecture Notes in Electrical Engineering, vol 765. Springer, Singapore. https://doi.org/10.1007/978-981-16-1550-4_3
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