An Edge-Based LSTM Approach for Predictive Intrusion Detection in Massive IoT Networks

Abstract

The Internet of Things (IoT) has become increasingly integrated into our daily lives, offering a wide range of services through the proliferation of connected devices. While this connectivity enhances convenience and functionality, it also introduces significant security challenges, exposing IoT systems to various forms of cyberattacks. In this paper, we propose a lightweight edge-based intrusion detection approach for massive IoT networks, leveraging a Long Short-Term Memory (LSTM) model to achieve high accuracy with minimal resource consumption. Unlike centralized solutions, the proposed system is fully implemented and deployed at the edge level, enabling local traffic analysis directly on edge devices. This design reduces latency, minimizes bandwidth consumption, enhances data privacy, and ensures real-time detection capabilities in large-scale IoT environments. The approach incorporates an efficient data pre-processing methodology applied to the wellknown Avast IoT-23 dataset, resulting in a detection accuracy of 99.9% with a compact model size of only 1767 KB. To further optimize performance, the system decomposes the data into clusters before applying a tailored LSTM model for each subset. Experimental evaluation using real malicious traffic demonstrates that the proposed model achieves up to 90% specificity and 88% precision under real-world conditions. These results confirm the effectiveness of our edge-level LSTM framework in providing secure, scalable, and resource-efficient intrusion detection for large-scale IoT environments.