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.

翻译：随着互联网和智能设备的普及，物联网技术取得了显著进展，并已成为智能家居、城市安防、智能物流等领域不可或缺的组成部分。物联网能够实现对关键生产指标的实时监控，使企业能够及时发现潜在质量问题、预测设备故障并优化工艺流程，从而减少损失并降低成本。此外，物联网还提升了资产实时追踪能力，优化了资产利用率与管理效率。然而，物联网的扩张也导致网络犯罪活动日益增多，设备正逐渐成为恶意攻击的载体。随着物联网设备数量的持续增长，迫切需要采取有效的网络安全措施以应对不断升级的威胁。本文提出一种融合LSTM与注意力机制的深度学习模型，作为应对物联网网络犯罪的关键策略。我们在IoT-23、BoT-IoT、物联网网络入侵、MQTT及MQTTset等数据集上进行的实验表明，所提方法在性能上优于现有基线模型。