Search results for "Data mining"
showing 10 items of 907 documents
Detection of Anomalous HTTP Requests Based on Advanced N-gram Model and Clustering Techniques
2013
Nowadays HTTP servers and applications are some of the most popular targets for network attacks. In this research, we consider an algorithm for HTTP intrusions detection based on simple clustering algorithms and advanced processing of HTTP requests which allows the analysis of all queries at once and does not separate them by resource. The method proposed allows detection of HTTP intrusions in case of continuously updated web-applications and does not require a set of HTTP requests free of attacks to build the normal user behaviour model. The algorithm is tested using logs acquired from a large real-life web service and, as a result, all attacks from these logs are detected, while the numbe…
Iteratively reweighted least squares in crystal structure refinements
2011
The use of robust techniques in crystal structure multipole refinements of small molecules as an alternative to the commonly adopted weighted least squares is presented and discussed. As is well known, the main disadvantage of least-squares fitting is its sensitivity to outliers. The elimination from the data set of the most aberrant reflections (due to both experimental errors and incompleteness of the model) is an effective practice that could yield satisfactory results, but it is often complicated in the presence of a great number of bad data points, whose one-by-one elimination could become unattainable. This problem can be circumvented by means of a robust least-squares regression that…
Novel Combinatorial and Information-Theoretic Alignment-Free Distances for Biological Data Mining
2010
Among the plethora of alignment-free methods for comparing biological sequences, there are some that we have perceived as representative of the novel techniques that have been devised in the past few years and as being of a fundamental nature and of broad interest and applicability, ranging from combinatorics to information theory. In this chapter, we review these alignment free methods, by presenting both their mathematical definitions and the experiments in which they are involved in.
A Fuzzy One Class Classifier for Multi Layer Model
2009
The paper describes an application of a fuzzy one-class classifier (FOC ) for the identification of different signal patterns embedded in a noise structured background. The classification phase is applied after a preprocessing phase based on a Multi Layer Model (MLM ) that provides a preliminary signal segmentation in an interval feature space. The FOC has been tested on synthetic and real microarray data in the specific problem of DNA nucleosome and linker regions identification. Results have shown, in both cases, a good recognition rate.
Speeding up the Consensus Clustering methodology for microarray data analysis
2010
Abstract Background The inference of the number of clusters in a dataset, a fundamental problem in Statistics, Data Analysis and Classification, is usually addressed via internal validation measures. The stated problem is quite difficult, in particular for microarrays, since the inferred prediction must be sensible enough to capture the inherent biological structure in a dataset, e.g., functionally related genes. Despite the rich literature present in that area, the identification of an internal validation measure that is both fast and precise has proved to be elusive. In order to partially fill this gap, we propose a speed-up of Consensus (Consensus Clustering), a methodology whose purpose…
An Ambient Intelligence Architecture for Extracting Knowledge from Distributed Sensors
2009
Precisely monitoring the environmental conditions is an essential requirement for AmI projects, but the wealth of data generated by the sensing equipment may easily overwhelm the modules devoted to higher-level reasoning, clogging them with irrelevant details. The present work proposes a new approach to knowledge extraction from raw data that addresses this issue at different levels of abstraction. Wireless sensor networks are used as the pervasive sensory tool, and their computational capabilities are exploited to remotely perform preliminary data processing. A central intelligent unit subsequently extracts higher-level concepts represented in a geometrical space and carries on symbolic re…
An Adaptive Bayesian System for Context-Aware Data Fusion in Smart Environments
2017
The adoption of multi-sensor data fusion techniques is essential to effectively merge and analyze heterogeneous data collected by multiple sensors, pervasively deployed in a smart environment. Existing literature leverages contextual information in the fusion process, to increase the accuracy of inference and hence decision making in a dynamically changing environment. In this paper, we propose a context-aware, self-optimizing, adaptive system for sensor data fusion, based on a three-tier architecture. Heterogeneous data collected by sensors at the lowest tier are combined by a dynamic Bayesian network at the intermediate tier, which also integrates contextual information to refine the infe…
Rule based reasoning for network management
2006
This paper focuses on improving network management by the adoption of artificial intelligence techniques. We propose a distributed multi-agent architecture for network management, where a logical reasoner acts as a managing entity capable of directing, coordinating, and triggering monitoring and management actions in the proposed architecture. The logical inference system has been devised to enable automated isolation, diagnosis, and to repair network anomalies, thus enhancing the reliability, performance, and security of the network. The measurements of network events are captured by programmable sensors deployed on the network devices and are collected by the network management entity whe…
Simulated Annealing Technique for Fast Learning of SOM Networks
2011
The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensi…
Mobile agent application fields
2004
Publisher Summary Mobile agents are a recent paradigm for software design, which extends object oriented programming features. An agent can perform its task autonomously; a mobile agent can carry out complex tasks that require the agent to migrate from a network place to another one. Mobile agent application fields are many. It can replace web services in other cases, mobile agents and web services can be an effective solution together. The chapter discusses the three mobile agent application fields, which are: parallel and distributed computing, data mining and information retrieval, and networking. An overview of the development platforms is also discussed. Data mining and information ret…