Securing the IoT: A Machine Learning Approach to Cyber Threat Analysis

DOI

10.2991/978-94-6463-471-6_123How to use a DOI?

Keywords

— IoT (Internet of Things); botnets; machine learning; Classification; Decision Tree; Logistic Regression

Abstract

The rise of Internet of Things (IoT) devices [1] has brought about numerous conveniences, but it has also introduced security challenges, notably the alarming threat of botnets. These malicious networks can compromise a large number of devices, posing significant risks to privacy, data integrity, and network availability. Recent years have seen the effectiveness of Machine Learning (ML) techniques in addressing this concern by identifying and mitigating IoT botnet attacks. The proposed system utilizes both supervised and unsupervised ML algorithms, such as classification, decision trees, and logistic regression, to identify compromised IoT devices. Trained on carefully labelled datasets, the system learns distinctive features and patterns associated with malicious activities, employing feature engineering to enhance accuracy. Real-time monitoring and anomaly detection are integrated to promptly respond to botnet-related activities. Ensemble learning methods further strengthen the system, resulting in high accuracy and minimized false alarms, contributing significantly to the security of IoT networks and paving the way for future advancements in IoT security.

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  - Jani Shaik
AU  - Mogasala Bhanu
AU  - Doddi Bhoomika
AU  - Burri Divya
AU  - Kornu Himasagar
AU  - Prof Ashok Patel
PY  - 2024
DA  - 2024/07/30
TI  - Securing the IoT: A Machine Learning Approach to Cyber Threat Analysis
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1285
EP  - 1293
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_123
DO  - 10.2991/978-94-6463-471-6_123
ID  - Shaik2024
ER  -