Paper Key : IRJ************796
Author: Gaurav Kumar
Date Published: 10 Jul 2024
Abstract
The paper presents a series of case studies and experimental results to demonstrate the effectiveness of machine learning-driven NIDS in real-world IoT scenarios. These case studies highlight the improvements in detection accuracy, response time, and overall network security achieved through the integration of machine learning algorithms. Overall, the dissertation underscores the importance of continuous innovation and adaptation in the field of IoT security. It advocates for the ongoing development of more sophisticated machine learning models and security protocols to keep pace with the evolving threat landscape. The final remarks emphasize the potential of IoT to transform industries and improve quality of life, provided that robust security measures are in place to safeguard these systems. This paper provides an in-depth examination of NIDS, focusing on the application of three machine learning algorithms KNeighborsClassifier, LogisticRegression, and RandomForestClassifier.
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