Internet-of-Thing (IoT) faces various security attacks. Different solutions exist to mitigate the intrusion problems. However, the existing solutions lack behind in dealing with heterogeneity of attack sources and features. The future anticipated demand of devices’ connections also urge the need of new solutions addressing the concerns of time consumption and complexity. In this article, we show a novel solution for the intrusion detection in IoT framework. We configure the intrusion detection in the edge computing layer so that the effect of the attack is not propagated to the clouds. Our solution uses cerebellar model articulation controller with kernel map. This combination is very new in the direction of intrusion detection; hence, it emphasizes the novelty of our proposed intrusion detection solution. We name our solution as Cerebellar Model Articulation Controller based Intrusion Detection System (CMACIDS). Additionally, we use spline fitting to the kernel mapping for the model fit; this adds on another novel contribution to CMACIDS. The results obtained with our detection system are compared with the state-of-the-art solutions in terms of complexity, false alarms, and precision of detection. The analysis of the comparative study proves the efficiency of the solution and makes CMACIDS suitable for IoT paradigm.

中文翻译：

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使用小脑模型关节控制器和样条拟合的边缘计算中的自适应入侵检测


物联网（IoT）面临各种安全攻击。存在不同的解决方案来减轻入侵问题。然而，现有的解决方案在处理攻击源和特征的异构性方面存在不足。未来设备连接的预期需求也迫切需要新的解决方案来解决时间消耗和复杂性问题。在本文中，我们展示了物联网框架中入侵检测的新颖解决方案。我们在边缘计算层配置入侵检测，以便攻击的影响不会传播到云端。我们的解决方案使用带有内核映射的小脑模型关节控制器。这种组合在入侵检测方向上是非常新颖的；因此，它强调了我们提出的入侵检测解决方案的新颖性。我们将我们的解决方案命名为基于小脑模型关节控制器的入侵检测系统（CMACIDS）。此外，我们使用样条拟合核映射来进行模型拟合；这为 CMACIDS 增添了另一项新颖的贡献。我们的检测系统获得的结果与最先进的解决方案在复杂性、误报和检测精度方面进行了比较。比较研究的分析证明了该解决方案的效率，并使 CMACIDS 适用于物联网范式。