110,108 research outputs found

    An experimental study on network intrusion detection systems

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    A signature database is the key component of an elaborate intrusion detection system. The efficiency of signature generation for an intrusion detection system is a crucial requirement because of the rapid appearance of new attacks on the World Wide Web. However, in the commercial applications, signature generation is still a manual process, which requires professional skills and heavy human effort. Knowledge Discovery and Data Mining methods may be a solution to this problem. Data Mining and Machine Learning algorithms can be applied to the network traffic databases, in order to automatically generate signatures. The purpose of this thesis and the work related to it is to construct a feasible architecture for building a database of network traffic data. This database can then be used to generate signatures automatically. This goal is achieved using network traffic data captured on the data communication network at the New Jersey Institute of Technology (NJIT)

    Query Optimizer Model for Performance Enhancement of Data Mining Based Query

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    ABSTRACT In present scenario almost applications are built upon data mining & OLAP tools and allow Users to answer information requests based on a data warehouse. that is managed by a powerful RDBMS. This paper is focused on query optimization technique which generates sequences of SQL statements in order to produce the requested information. The analysis for this paper is exposed that many sequences of queries generated by commercial tools are not very efficient. Semantic query optimizer architecture is suggested for these applications. The main component is a SQO optimizer that accepts previously generated sequences of queries and rewrites them according to a set of optimization strategies, before they are executed by the underlying database system. The advantages of this proposed architecture are discussed and this is an appropriate approach to optimize query sequences for data warehousing & Data mining based applications

    Association Rules Mining Based Clinical Observations

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    Healthcare institutes enrich the repository of patients' disease related information in an increasing manner which could have been more useful by carrying out relational analysis. Data mining algorithms are proven to be quite useful in exploring useful correlations from larger data repositories. In this paper we have implemented Association Rules mining based a novel idea for finding co-occurrences of diseases carried by a patient using the healthcare repository. We have developed a system-prototype for Clinical State Correlation Prediction (CSCP) which extracts data from patients' healthcare database, transforms the OLTP data into a Data Warehouse by generating association rules. The CSCP system helps reveal relations among the diseases. The CSCP system predicts the correlation(s) among primary disease (the disease for which the patient visits the doctor) and secondary disease/s (which is/are other associated disease/s carried by the same patient having the primary disease).Comment: 5 pages, MEDINFO 2010, C. Safran et al. (Eds.), IOS Pres

    Towards the cloudification of the social networks analytics

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    In the last years, with the increase of the available data from social networks and the rise of big data technologies, social data has emerged as one of the most profitable market for companies to increase their benefits. Besides, social computation scientists see such data as a vast ocean of information to study modern human societies. Nowadays, enterprises and researchers are developing their own mining tools in house, or they are outsourcing their social media mining needs to specialised companies with its consequent economical cost. In this paper, we present the first cloud computing service to facilitate the deployment of social media analytics applications to allow data practitioners to use social mining tools as a service. The main advantage of this service is the possibility to run different queries at the same time and combine their results in real time. Additionally, we also introduce twearch, a prototype to develop twitter mining algorithms as services in the cloud.Peer ReviewedPostprint (author’s final draft
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