Botnet Attack Detection the Internet of Things Using Selected Learning Algorithms: A Research Study on Securing IoT Against Cyber Threats Machine in Bloomington, MN

A Must-Read for IoT Security Researchers and Machine Learning Experts
As IoT networks continue to expand, so do the complexities of securing them against
botnet attacks
. The diversity of devices, varying computational capabilities, and different communication protocols make developing a
universal botnet detection system
a significant research challenge. This book provides a
rigorous, data-driven approach
to tackling this issue using
supervised machine learning algorithms
.
Based on the
NB-IoT-23 dataset
, this research evaluates multiple classification techniques, including
Logistic Regression, Linear Regression, Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Bagging
. The findings reveal that the
Bagging ensemble model
outperforms others, achieving an exceptional
99.96% accuracy
with minimal computational overhead, making it a strong candidate for
real-world IoT botnet detection systems
Key Features for Academic Researchers:
Comprehensive IoT Security Analysis
- Explore the unique challenges of botnet detection across diverse IoT devices.
Advanced Machine Learning Techniques
- Compare different learning algorithms and their effectiveness in botnet detection.
High-Quality Dataset & Empirical Evaluation
- Gain insights from
real-world NB-IoT-23 datasets
featuring data from multiple IoT devices.
Research-Backed Findings
- The book presents reproducible results, making it a
valuable reference for Master's and Ph.D. students
exploring IoT security, cybersecurity, and machine learning.
Future Research Directions
- Identify gaps and opportunities for further exploration in
IoT security and anomaly detection
This book serves as a
practical and theoretical resource
for graduate students, cybersecurity professionals, and researchers interested in
IoT security, network intrusion detection, and applied machine learning
Enhance your research and contribute to securing IoT networks-get your copy today!