Anomaly Detection Using Model Generation for Event-Based Systems Without a Preexisting Formal Model

2012 ◽  
Vol 42 (3) ◽  
pp. 654-668 ◽  
Author(s):  
Lindsay V. Allen ◽  
Dawn M. Tilbury
Keyword(s):  
Anomaly Detection ◽  
Formal Model ◽  
Model Generation ◽  
Event Based

scholarly journals Generating Reference Models for Structurally Complex Data

2013 ◽  
Vol 52 (05) ◽  
pp. 441-453 ◽  
Author(s):  
J. A. Lara ◽  
L. Martinez ◽  
A. Pérez ◽  
J. P. Valente ◽  
F. Alonso
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Reference Model ◽  
Control Group ◽  
Model Generation ◽  
Correct Result ◽  
Complex Data ◽  
Reference Models ◽  
Event Based ◽  
Fold Cross Validation

SummaryObjectives: We present a framework specially designed to deal with structurally complex data, where all individuals have the same structure, as is the case in many medical domains. A structurally complex individual may be composed of any type of single-valued or multivalued attributes, including time series, for example. These attributes are structured according to domain-dependent hierarchies. Our aim is to generate reference models of population groups. These models represent the population archetype and are very useful for supporting such important tasks as diagnosis, detecting fraud, analyzing patient evolution, identifying control groups, etc.Methods: We have developed a conceptual model to represent structurally complex data hierarchically. Additionally, we have devised a method that uses the similarity tree concept to measure how similar two structurally complex individuals are, plus an outlier detection and filtering method. These methods provide the groundwork for the method that we have designed for generating reference models of a set of structurally complex individuals. A key idea of this method is to use event-based analysis for modeling time series.Results: The proposed framework has been applied to the medical field of stabilometry. To validate the outlier detection method we used 142 individuals, and there was a match between the outlier ratings by the experts and by the system for 139 individuals (97.8%). To validate the reference model generation method, we applied k-fold cross validation (k = 5) with 60 athletes (basket-ball players and ice-skaters), and the system correctly classified 55 (91.7%). We then added 30 non-athletes as a control group, and the method output the correct result in a very high percentage of cases (96.6%).Conclusions: We have achieved very satisfactory results for the tests on data from such a complex domain as stabilometry and for the comparison of the reference model generation method with other methods. This supports the validity of this framework.

Automatic Formal Model Generation and Analysis of SDL

2003 ◽  
pp. 285-299 ◽  
Author(s):  
Annikka Aalto ◽  
Nisse Husberg ◽  
Kimmo Varpaaniemi
Keyword(s):  
Formal Model ◽  
Model Generation

scholarly journals EvAn: Neuromorphic Event-Based Sparse Anomaly Detection

2021 ◽  
Vol 15 ◽  
Author(s):  
Lakshmi Annamalai ◽  
Anirban Chakraborty ◽  
Chetan Singh Thakur
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Careful Analysis ◽  
Motion Blur ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Unsupervised Deep Learning ◽  
Event Based

Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events. This principle results in significant advantages over conventional cameras, such as low power utilization, high dynamic range, and no motion blur. Moreover, by design, such cameras encode only the relative motion between the scene and the sensor and not the static background to yield a very sparse data structure. In this paper, we leverage these advantages of an event camera toward a critical vision application—video anomaly detection. We propose an anomaly detection solution in the event domain with a conditional Generative Adversarial Network (cGAN) made up of sparse submanifold convolution layers. Video analytics tasks such as anomaly detection depend on the motion history at each pixel. To enable this, we also put forward a generic unsupervised deep learning solution to learn a novel memory surface known as Deep Learning (DL) memory surface. DL memory surface encodes the temporal information readily available from these sensors while retaining the sparsity of event data. Since there is no existing dataset for anomaly detection in the event domain, we also provide an anomaly detection event dataset with a set of anomalies. We empirically validate our anomaly detection architecture, composed of sparse convolutional layers, on this proposed and online dataset. Careful analysis of the anomaly detection network reveals that the presented method results in a massive reduction in computational complexity with good performance compared to previous state-of-the-art conventional frame-based anomaly detection networks.

Topology Based Automatic Formal Model Generation for Point Automation Systems

2015 ◽  
Vol 44 (1) ◽  
Author(s):  
Muhammet Ali Nur Oz ◽  
Ibrahim Sener ◽  
Ozgur Turay Kaymakci ◽  
Ilker Ustoglu ◽  
Galip Cansever
Keyword(s):  
Formal Model ◽  
Model Generation ◽  
Automation Systems

scholarly journals Event-based Security Policy and Formal Model of Critical Information Infrastructures Protecting Mechanism

2019 ◽  
Vol 5 (4) ◽  
pp. 99-105 ◽  
Author(s):  
S. Erokhin ◽  
A. Petukhov ◽  
P. Pilyugin
Keyword(s):  
Security Policy ◽  
Formal Model ◽  
Security Management ◽  
Formal Description ◽  
Critical Information ◽  
Information Infrastructures ◽  
Quality Of Protection ◽  
Protection Mechanisms ◽  
Event Based

The article considers the possibilities of security management of critical information infrastructures. Approaches to the construction of policies not focused on a fixed list of threats are proposed. It substantiates the possibility of building a security policy based on security events monitoring. A formal description of security events and formal model of protection mechanisms based on monitoring security events is proposed. The features of this approach for the protection of critical information infrastructures in order to improve the quality of protection are considered.

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