Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

/pdf/anomaly-detection-a-survey-1x33g9m0a3.pdf

Anomaly detection: A survey

https://www.robots.ox.ac.uk/~davidc/pubs/NDreview2014.pdf

Review: A review of novelty detection

The enormous amount of information stored in unstructured texts cannot simply be used for further processing by computers, which typically handle text as simple sequences of character strings. Therefore, specific (pre-)processing methods and algorithms are required in order to extract useful patterns. Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. In this article, we discuss text mining as a young and interdisciplinary field in the intersection of the related areas information retrieval, machine learning, statistics, computational linguistics and especially data mining. We describe the main analysis tasks preprocessing, classification, clustering, information extraction and visualization. In addition, we briefly discuss a number of successful applications of text mining.

https://www.cin.ufpe.br/~rbcp/artigos/2005-Hotho-A%20Brief%20Survey%20of%20Text%20Mining.pdf

A Brief Survey of Text Mining.

/pdf/environmental-sensor-networks-a-revolution-in-the-earth-1gkgkhzefh.pdf

Environmental Sensor Networks: A revolution in the earth system science?

Environmental Sensor Networks (ESNs) facilitate the study of fundamental processes and the development of hazard response systems. They have evolved from passive logging systems that require manual downloading, into ‘intelligent’ sensor networks that comprise a network of automatic sensor nodes and communications systems which actively communicate their data to a Sensor Network Server (SNS) where these data can be integrated with other environmental datasets. The sensor nodes can be fixed or mobile and range in scale appropriate to the environment being sensed. ESNs range in scale and function and we have reviewed over 50 representative examples. Large Scale Single Function Networks tend to use large single purpose nodes to cover a wide geographical area. Localised Multifunction Sensor Networks typically monitor a small area in more detail, often with wireless adhoc systems. Biosensor Networks use emerging biotechnologies to monitor environmental processes as well as developing proxies for immediate use. In the future, sensor networks will integrate these three elements (Heterogeneous Sensor Networks). The communications system and data storage and integration (cyberinfrastructure) aspects of ESNs are discussed, along with current challenges which need to be addressed. We argue that Environmental Sensor Networks will become a standard research tool for future Earth System and Environmental Science. Not only do they provide a ‘virtual’ connection with the environment, they allow new field and conceptual approaches to the study of environmental processes to be developed. We suggest that although technological advances have facilitated these changes, it is vital that Earth Systems and Environmental Scientists utilise them.

Environmental Sensor Networks: A revolution in Earth System Science?

Recently Active Networks were proposed as the approach that will enable quick introduction of new services in the current telecommunication infrastructure. This approach allows third party executable codes (proxylet) to be deployed into the network, which creates a big security risk. In this paper we present the application of Bayesian technology in the development of an anomaly detection system for proxylets. This system will be incorporated into our Intrusion Detection System (IDS) that will provide runtime security to ensure active platform integrity is maintained while running third party executable codes.

/pdf/active-platform-security-through-intrusion-detection-using-1crrqazsi7.pdf

Active Platform Security through Intrusion Detection Using Naïve Bayesian Network for Anomaly Detection

More and more sensor networks are being suggested as alternatives to single sensor packages as a means of measuring and recording environmental variations. The rate at which the sensors make readings from the environment is both important in terms of presenting an accurate picture of environmental changes to the users and resource usage in the network. This paper is looking at means of making an adaptive control mechanism to change the frequency of measurements by each node, as an alternative to non-adaptive fixed sampling rates.

/pdf/adaptive-sampling-mechanisms-in-sensor-networks-2ct37zq8gg.pdf

Adaptive Sampling Mechanisms in Sensor Networks

This paper analyzes the suitability of fuzzy clustering methods for the discovery of relevant document relationships, motivated by the need for enhanced relevance-based navigation of Web-accessible resources. The performance evaluation of a modified Fuzzy c-Means algorithm is carried out, and a comparison with a traditional hard clustering technique is presented. Clustering precision and recall are defined and applied as quantitative evaluation measures of the clustering results. The experiments with various test document sets have shown that in most cases fuzzy clustering performs better than the hard k-Means algorithm and that the fuzzy membership values can be used to determine document relevance and to control the amount of information retrieved to the user.

Evaluating fuzzy clustering for relevance-based information access

Sensor networks in environmental monitoring applications aim to provide scientists with a useful spatio-temporal representation of the observed phenomena. This helps to deepen their understanding of the environmental signals that cover large geographic areas. In this paper, the spatial aspect of this data handling requirement is met by creating clusters in a sensor network based on the rate of change of an oceanographic signal with respect to space. Inspiration was drawn from quorum sensing, a biological process that is carried out within communities of bacterial cells. The paper demonstrates the control the user has over the sensitivity of the algorithm to the data variation and the energy consumption of the nodes while they run the algorithm.

/pdf/a-biologically-inspired-clustering-algorithm-dependent-on-3cb7gbmjrd.pdf

A biologically-inspired clustering algorithm dependent on spatial data in sensor networks

This chapter describes the use of fuzzy clustering for knowledge representation in the context of e-Learning applications. A modified version of the Fuzzy c-Means algorithm that replaces the Euclidean norm by a dissimilarity function is proposed for document clustering. Experimental results are presented, which show substantial improvement in the modified algorithm, both in terms of computational efficiency and of quality of the clusters. The robustness of this fuzzy clustering algorithm for document collections is demonstrated and its use for on-line teaching and learning in a complete end-to-end system is explored.

Dynamic Knowledge Representation for e-Learning Applications

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