DL-ADS: Improved Grey Wolf Optimization Enabled AE-LSTM Technique for Efficient Network Anomaly Detection in Internet of Thing Edge Computing

Abstract

The Internet of Things (IoT) technology has begun to proliferate in recent years, which simultaneously increases the number of attacks. Owing to the massive volume and multi-dimensional data in IoT, anomaly detection leads to low prediction accuracy and a high false alarm rate. Further, there is a deficit of real-world test datasets for anomaly detection. This work aims to generate a novel real-time anomaly detection dataset and proposes an efficient anomaly detection model using an Improved Grey Wolf Optimization (IGWO)-enabled Long Short-Term Memory (LSTM) network in IoT edge scenarios. Dataset generation is carried out using a testbed setup containing Raspberry Pi 4 and sensors connected by a lightweight Message Queuing Telemetry Transport (MQTT) protocol. An autoencoder is used for feature reduction as it can investigate the input characteristics without sacrificing vital information. The LSTM classifier parameters, such as learning rate, optimizer, and batch size, are tuned precisely using IGWO techniques. The experimental results disclose that the proposed model achieves an accuracy of 99.11% for the testbed dataset, which is better than recent models. To confirm the generalizability of our model, the CICIDS 2017, DS2OS, and MQTTset standard datasets are applied explicitly. The developed model outcomes are statistically verified using the Wilcoxon signed rank test.

Keywords

• Anomaly detection
• autoencoder
• improved grey wolf optimization
• Internet of Things security
• LSTM networks
• MQTT