基于Kmeans聚类和朴素贝叶斯的IoT异常检测方法
A Hybrid Approach: Utilising Kmeans Clustering and Naive Bayes for IoT Anomaly Detection
摘要：
物联网设备的激增和多样性意味着它们越来越成为恶意用户的可行目标。这就产生了对可以跨多个设备工作的异常检测算法的需求。本论文提出了一种潜在的替代方案，可以在物联网系统中实现当前的异常检测算法，这些算法可以应用于不同类型的设备。该算法由机器学习的非监督和监督机器区域组成，结合了每个区域的最强方面。该算法涉及初始的k-means聚类的攻击，并将它们分配到集群。接下来，AdaBoosted Naive Bayes监督学习算法使用这些聚类，以便自学哪段数据应该聚类到哪种特定攻击。这通过在最终分类步骤之前添加聚类数据来提高所提出算法的准确性，从而确保更准确的算法。该算法的正确识别百分比分数范围从90%到100%，以及对所提出的算法的准确性，精确度和召回率进行评级。这些高分实现了准确，灵活，可扩展，优化的算法，可能在不同的物联网设备，确保强大的数据完整性和隐私。
英文摘要：
The proliferation and variety of Internet of Things devices means that they have increasingly become a viable target for malicious users. This has created a need for anomaly detection algorithms that can work across multiple devices. This thesis suggests a potential alternative to the current anomaly detection algorithms to be implemented within IoT systems that can be applied across different types of devices. This algorithm is comprised of both unsupverised and supervised machine areas of machine learning combining the strongest facet of each. The algorithm involves the initial k-means clustering of attacks and assigns them to clusters. Next, the clusters are then used by the AdaBoosted Naive Bayes supervised learning algorithm in order to teach itself which piece of data should be clustered to which specific attack. This increases the accuracy of the proposed algorithm by adding clustered data before the final classification step, ensuring a more accurate algorithm. The correct indentification percentage scores for this proposed algorithm range anywhere from 90% to 100%, as well as rating the proposed algorithms accuracy, precision and recall. These high scores achieve an accurate, flexible, scalable, optimised algorithm that could potentially be in different IoT devices, ensuring strong data integrity and privacy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
