用混合深度学习模型精准识别物联网网络中的已知与未知攻击
Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models
- 结合自组织映射、深度信念网络与自编码器，构建多层检测架构
- 在多个数据集上达到99.99%准确率，MCC超99.50%
- 适合关注物联网安全与新型攻击防御的研究者与工程师
物联网设备的快速扩展增加了网络遭受网络攻击的风险，因此有效检测对于保障物联网网络安全至关重要。本文提出一种新方法，结合自组织映射（SOM）、深度信念网络（DBN）和自编码器，以检测已知及此前未见的攻击模式。通过粒子群优化（PSO）对模型进行优化，并利用模拟与真实流量数据进行全面评估。实验在NSL-KDD、UNSW-NB15和CICIoT2023数据集上进行，结果显示系统最高准确率达99.99%，马修相关系数（MCC）超过99.50%。结果表明，该方法能有效识别新兴威胁并适应不断演变的攻击策略，显著提升物联网安全性。
原文摘要 · Abstract (English)
The rapid expansion of Internet of Things (IoT) devices has increased the risk of cyber-attacks, making effective detection essential for securing IoT networks. This work introduces a novel approach combining Self-Organizing Maps (SOMs), Deep Belief Networks (DBNs), and Autoencoders to detect known and previously unseen attack patterns. A comprehensive evaluation using simulated and real-world traffic data is conducted, with models optimized via Particle Swarm Optimization (PSO). The system achieves an accuracy of up to 99.99% and Matthews Correlation Coefficient (MCC) values exceeding 99.50%. Experiments on NSL-KDD, UNSW-NB15, and CICIoT2023 confirm the model's strong performance across diverse attack types. These findings suggest that the proposed method enhances IoT security by identifying emerging threats and adapting to evolving attack strategies.
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