An Intelligent Approach to Increase the Performance of Threat Detection in IoT

DOI

10.2991/978-94-6463-471-6_137How to use a DOI?

Keywords

Internet of Things; Machine Learning Models; Threat Detection

Abstract

The ubiquitous use of IoT (Internet of Things) devices is on the increase. In order for an Internet of Things system to function, it includes all of the necessary hardware, software, networks, sensors, and other parts. The developers of these sensors and devices, however, often omitted details about their minimal resource needs and a slew of security vulnerabilities. In addition, there are a lot of risks associated with the placement of edge networks for IoT devices. The system's performance might be severely compromised by denial-of-service assaults or unlawful sensor hijacking on sites inside the edge network. Our paper presents a model for training and forecasting DDoS attacks using principal component analysis and machine learning methods. The data's dimensionality was reduced using principal component analysis techniques. Metrics for evaluation included precision, accuracy, F1score, and recall. Important parts of the evaluation metrics mentioned earlier are Metrics such as True- Positive, False- Positive, True -Negative, and False -Negative are utilized to assess the impact of the Fourth Industrial Revolution. We used the Training Time to compare each model's training time, which differs from past research. With the use of the CICIDS 2017 and CICIDS 2018 datasets, we assess the performance of our suggested model. In comparison to similar models, the suggested models outperformed them while requiring much less time to train.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. Tamilkodi
AU  - V. Bala Sankar
AU  - Nersu Pavankumar
AU  - Potnuru Hemanth Kumar
AU  - Uggu Veera Gani Durga
AU  - Bokka Durga Pravallika
PY  - 2024
DA  - 2024/07/30
TI  - An Intelligent Approach to Increase the Performance of Threat Detection in IoT
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1414
EP  - 1422
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_137
DO  - 10.2991/978-94-6463-471-6_137
ID  - Tamilkodi2024
ER  -