Enhancing Cybersecurity in IoT Networks: A Deep Learning Approach to Anomaly Detection
This addresses security threats in IoT networks, which is critical for sectors like smart homes and urban security, but it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of increasing cybercrimes in IoT networks by proposing a deep learning model with LSTM and attention mechanisms for anomaly detection, achieving results that outperform existing baselines on multiple datasets.
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.