The pervasive deployment of the Internet of Things (IoT) has significantly facilitated manufacturing and living. The diversity and continual updates of IoT systems make their security a crucial challenge, among which the detection of malicious network traffic turns out to be the most common yet destructive threat. Despite the efforts on feature engineering and classification backend designing, established intrusion detection systems sometimes lack robustness and are inflexible against the shift of the traffic distribution. To deal with these disadvantages, we design a fuzzy system for the online defense of IoT. Our framework incorporates a full Bayesian possibilistic clustering module for feature processing and an ensemble module motivated by reinforcement learning and adaptive boosting that dynamically fits the streaming data. The proposed clustering module overcomes the issue of determining the number of clusters and can dynamically identify new patterns. The classifier backend combines a collection of fuzzy decision trees that provide readable decision boundaries. The ensembled classifiers can accommodate the drift of data distribution to optimize the long-time performance. Our proposal is tested on settings including one dataset collected from real IoT systems and is compared to numerous competitors. Experimental results verified the advantage of our system regarding accuracy and stability.

中文翻译：

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具有全贝叶斯可能性聚类和集成模糊分类器的物联网系统在线入侵检测

物联网 (IoT) 的普遍部署极大地促进了制造和生活。物联网系统的多样性和持续更新使其安全成为一项关键挑战，其中恶意网络流量的检测被证明是最常见但最具破坏性的威胁。尽管在特征工程和分类后端设计方面做出了努力，但已建立的入侵检测系统有时缺乏鲁棒性，并且对流量分布的变化不灵活。针对这些缺点，我们设计了一个物联网在线防御的模糊系统。我们的框架包含一个用于特征处理的完整贝叶斯可能性聚类模块和一个由强化学习和自适应提升驱动的集成模块，可动态拟合流数据。所提出的聚类模块克服了确定聚类数量的问题，并且可以动态识别新模式。分类器后端结合了提供可读决策边界的模糊决策树集合。集成分类器可以适应数据分布的漂移以优化长期性能。我们的提案在包括从真实物联网系统收集的一个数据集在内的设置上进行了测试，并与众多竞争对手进行了比较。实验结果验证了我们系统在准确性和稳定性方面的优势。集成分类器可以适应数据分布的漂移以优化长期性能。我们的提案在包括从真实物联网系统收集的一个数据集在内的设置上进行了测试，并与众多竞争对手进行了比较。实验结果验证了我们系统在准确性和稳定性方面的优势。集成分类器可以适应数据分布的漂移以优化长期性能。我们的提案在包括从真实物联网系统收集的一个数据集在内的设置上进行了测试，并与众多竞争对手进行了比较。实验结果验证了我们系统在准确性和稳定性方面的优势。