IoT security using deep learning algorithm: intrusion detection model using LSTM
Abitha V. K. Lija, R. Shobana, J. Caroline Misbha, S. Chandrakala · International Journal of Electronic Security and Digital Forensics · 2024
Internet of things (IoT) and the integration of many gadgets is rapidly becoming a reality.IoT devices, particularly edge devices, are particularly vulnerable to cyberattacks as a result of the proliferation of device-to-device (D2D) connectivity Advanced network security measures are required to do real-time traffic analysis and to mitigate malicious traffic.These mechanisms must also be able to detect malicious traffic.We describe a game-changing approach to detect and classify new malware in record time.This will allow us to handle the difficulty that has been presented (zero-day malware).This article puts out the idea of a hybrid deep learning (DL) model for the detection of cyber attacks.Long short-term memory (LSTM) and gated recurrent unit are the foundations of the model that has been suggested (GRU).The results of the experiments are quite encouraging, revealing an accuracy rate of 94.50% for the identification of malware traffic.