An AI-Driven Cybersecurity Framework for IoT: Integrating LSTM-Based Anomaly Detection, Reinforcement Learning, and Post-Quantum Encryption

An AI-Driven Cybersecurity Framework for IoT: Integrating LSTM-Based Anomaly Detection, Reinforcement Learning, and Post-Quantum Encryption

In an increasingly digital world, cybersecurity threats have grown in frequency, complexity, and scale, challenging the adequacy of traditional rule-based defense systems. This study proposes a unified AI-driven cybersecurity framework to detect anomalies, verify data integrity, automate incident re...

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Bibliographic Details
Main Author: Mozamel M. Saeed
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
AI-driven cybersecurity
anomaly detection
LSTM networks
homomorphic hashing
data integrity
reinforcement learning
Online Access:https://ieeexplore.ieee.org/document/11023579/
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https://ieeexplore.ieee.org/document/11023579/

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