Enhancing Cybersecurity in IoT Networks: A Deep Learning Approach to Anomaly Detection

TL;DR

This paper presents a deep learning model using LSTM and attention mechanisms to improve anomaly detection and cybersecurity in IoT networks, demonstrating superior performance on multiple datasets.

Contribution

The paper introduces a novel deep learning approach combining LSTM and attention mechanisms specifically designed for IoT network anomaly detection.

Findings

Outperforms existing baseline methods on IoT security datasets

Effective in detecting various cyber threats in IoT environments

Enhances real-time monitoring and threat mitigation in IoT networks

Abstract

With the proliferation of the Internet and smart devices, IoT technology has seen significant advancements and has become an integral component of smart homes, urban security, smart logistics, and other sectors. IoT facilitates real-time monitoring of critical production indicators, enabling businesses to detect potential quality issues, anticipate equipment malfunctions, and refine processes, thereby minimizing losses and reducing costs. Furthermore, IoT enhances real-time asset tracking, optimizing asset utilization and management. However, the expansion of IoT has also led to a rise in cybercrimes, with devices increasingly serving as vectors for malicious attacks. As the number of IoT devices grows, there is an urgent need for robust network security measures to counter these escalating threats. This paper introduces a deep learning model incorporating LSTM and attention mechanisms, a pivotal strategy in combating cybercrime in IoT networks. Our experiments, conducted on datasets including IoT-23, BoT-IoT, IoT network intrusion, MQTT, and MQTTset, demonstrate that our proposed method outperforms existing baselines.