Distributed anomaly detection models for industrial wireless sensor networks

Wireless Sensor Networks (WSNs) are firmly established as an integral technology that enables automation and control through pervasive monitoring for many industrial applications. These range from environmental applications and healthcare applications to major industrial monitoring applications such as infrastructure and structural monitoring. The key features that are common to such applications can be noted as involving large amounts of data, consisting of dynamic observation environments, non-homogeneous data distributions with evolving patterns and sensing functionality leading to data-driven control. Also in most industrial applications a major requirement is to have near real-time decision support. Accordingly there is a vital need to have a secure continuous and reliable sensing mechanism in integrated WSNs where integrity of the data is assured. However, in practice WSNs are vulnerable to different security attacks, faults and malfunction due to inherent resource constraints, openly commoditised wireless technologies employed and naive modes of implementation. Misbehaviour resulting from such threats manifest as anomalies in the sensed data streams in critically compromising the systems. Therefore, it is vital that effective techniques are introduced in accurately detecting anomalies and assuring the integrity of the data. This research focuses on investigating such models for large scale industrial wireless sensor networks. Focusing on achieving an anomaly detection framework that is adaptable and scalable, a hierarchical data partitioning approach with fuzzy data modelling is introduced first. In this model unsupervised data partitioning is performed in a distributed manner by adapting fuzzy c-means clustering in an incremental model over a hierarchical node topology. It is found that non-parametric and non-probabilistic determination of anomalies can be done by evaluating the fuzzy membership scores and inter-cluster distances adaptively over the node hierarchy. Considering heterogeneous data distributions with evolving patterns, a granular anomaly detection model that uses an entropy criterion to dynamically partition the data is proposed next. This successfully overcomes the issue of determining the proper number of expected clusters in a dynamic manner. In this approach the data is partitioned on to different cohesive regions using cumulative point-wise entropy directly. The effect of differential density distributions when relying on an entropy criterion is mitigated by introducing an average relative density measure to segregate isolated outliers prior to the partitioning. The combination of these two factors is shown to be significantly successful in determining anomalies adaptively in a fully dynamic manner. The need for near real-time anomaly evaluation is focused next on this thesis. Building upon the entropy based data partitioning model that is also proposed, a Point-of-View (PoV) entropy evaluation model is developed next. This employs an incremental data processing model as opposed to batch-wise data processing. Three unique points-of-view are introduced as the reference points over which point-wise entropy is computed in evaluating its relative change as the data streams evolve. Overall this thesis proposes efficient unsupervised anomaly detection models that employ distributed in-network data processing for accurate determination of anomalies. The resource constrained environment is taken in to account in each of the models with innovations made to achieve non-parametric and non-probabilistic detection

1993-1994 Louisiana Tech University Catalog

The Louisiana Tech University Catalog includes announcements and course descriptions for courses offered at Louisiana Tech University for the academic year of 1993-1994.https://digitalcommons.latech.edu/university-catalogs/1021/thumbnail.jp