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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Scalable Hypergrid k-NN-Based Online Anomaly Detection in Wireless Sensor Networks
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Scalable Hypergrid k-NN-Based Online Anomaly Detection in Wireless Sensor Networks

机译：无线传感器网络中基于可扩展超网格k-NN的在线异常检测

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摘要

Online anomaly detection (AD) is an important technique for monitoring wireless sensor networks (WSNs), which protects WSNs from cyberattacks and random faults. As a scalable and parameter-free unsupervised AD technique, $(k)$-nearest neighbor (kNN) algorithm has attracted a lot of attention for its applications in computer networks and WSNs. However, the nature of lazy-learning makes the kNN-based AD schemes difficult to be used in an online manner, especially when communication cost is constrained. In this paper, a new kNN-based AD scheme based on hypergrid intuition is proposed for WSN applications to overcome the lazy-learning problem. Through redefining anomaly from a hypersphere detection region (DR) to a hypercube DR, the computational complexity is reduced significantly. At the same time, an attached coefficient is used to convert a hypergrid structure into a positive coordinate space in order to retain the redundancy for online update and tailor for bit operation. In addition, distributed computing is taken into account, and position of the hypercube is encoded by a few bits only using the bit operation. As a result, the new scheme is able to work successfully in any environment without human interventions. Finally, the experiments with a real WSN data set demonstrate that the proposed scheme is effective and robust.
机译：在线异常检测（AD）是监视无线传感器网络（WSN）的一项重要技术，可以保护WSN免受网络攻击和随机故障。作为一种可扩展且无参数的无监督AD技术，$（k）$最近邻居（kNN）算法在计算机网络和WSN中的应用引起了很多关注。然而，懒惰学习的本质使得基于kNN的AD方案难以在线使用，尤其是在通信成本受到限制的情况下。本文提出了一种基于超网格直觉的基于kNN的AD方案，以解决WSN应用中的懒惰学习问题。通过将超球检测区域（DR）的异常重新定义为超立方体DR，可以大大降低计算复杂度。同时，使用附加系数将超网格结构转换为正坐标空间，以保留用于在线更新的冗余度和调整位操作。另外，考虑了分布式计算，并且仅使用位操作通过几个位对超立方体的位置进行编码。结果，新方案能够在任何环境下成功运行而无需人工干预。最后，在真实的WSN数据集上进行的实验表明，该方案是有效且可靠的。

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