Deep Learning Based Intrusion Detection Framework in Industrial IoT

A Deep Learning Based Intrusion Detection Framework in Industrial IoT is a sophisticated system designed to safeguard critical infrastructure from unauthorized access and malicious activities. Leveraging the power of deep learning algorithms, this framework utilizes advanced neural networks to analyze vast amounts of data collected from Industrial Internet of Things (IoT) devices. By training the deep learning models on labeled datasets consisting of normal and anomalous behavior patterns, the framework can accurately identify and classify various types of intrusions in real-time. The framework's ability to adapt and learn from evolving threats makes it an effective defense mechanism, providing a robust security layer for industrial IoT environments, ensuring the integrity, availability, and confidentiality of critical assets and systems.

Autorentext

Dr. Raghavender K V ist außerordentlicher Professor in der Abteilung für CSE am G. Narayanamma Institute of Technology & Science, Hyderabad. Er hat einen Doktortitel in Informatik und Ingenieurwesen mit den Schwerpunkten Netzwerksicherheit und maschinelles Lernen. Er hat seinen M.Tech und B.Tech in CSE an der JNTUH, Hyderabad, Telangana, Indien abgeschlossen.