arXiv subjects

Mauro Garofalo

Publications and source records attributed to Mauro Garofalo.

7 recordsLinked to original sources

arXiv2019

Spark-Based Anomaly Detection: the Case of Port and Net Scan

The two most spread network anomalies are port and net scan. In this work, we present and analyze the results obtained by traditional approaches for the detection of net scan and port scans. We use a simple threshold-based algorithm, working at flow-level and adapt it for the execution on Apache Spark. The use of Big Data Analytics technologies allows to significantly the execution times of the algorithm so to be used even in current, high-speed networks. The paper describes our approach and presents an experimental analysis in terms of detection performance and execution time. We use real traffic traces from MAWI archive and MAWILab anomaly detectors to compare with our results. The analysis shows that i) our traditional threshold-based algorithm is already able to achieve detection performance higher than MAWILab (in 95% of the considered cases with the best threshold value), currently considered the gold standard in the field; ii) the execution time is much shorter than the trace time, which makes it usable also in real time. Moreover, for each traffic trace we provide the research community with a new labeled dataset, validated by comparisons with MAWILab and extended with other anomalies not detected by it. We publish an updated dataset every day at our project website.

cs.NI↗

arXiv2017

Astrophysics and Big Data: Challenges, Methods, and Tools

Nowadays there is no field research which is not flooded with data. Among the sciences, Astrophysics has always been driven by the analysis of massive amounts of data. The development of new and more sophisticated observation facilities, both ground-based and spaceborne, has led data more and more complex (Variety), an exponential growth of both data Volume (i.e., in the order of petabytes), and Velocity in terms of production and transmission. Therefore, new and advanced processing solutions will be needed to process this huge amount of data. We investigate some of these solutions, based on machine learning models as well as tools and architectures for Big Data analysis that can be exploited in the astrophysical context.

astro-ph.IM↗

arXiv2016

DAMEWARE - Data Mining & Exploration Web Application Resource

Astronomy is undergoing through a methodological revolution triggered by an unprecedented wealth of complex and accurate data. DAMEWARE (DAta Mining & Exploration Web Application and REsource) is a general purpose, Web-based, Virtual Observatory compliant, distributed data mining framework specialized in massive data sets exploration with machine learning methods. We present the DAMEWARE (DAta Mining & Exploration Web Application REsource) which allows the scientific community to perform data mining and exploratory experiments on massive data sets, by using a simple web browser. DAMEWARE offers several tools which can be seen as working environments where to choose data analysis functionalities such as clustering, classification, regression, feature extraction etc., together with models and algorithms.

astro-ph.IM↗

arXiv2014

DAMEWARE: A web cyberinfrastructure for astrophysical data mining

Astronomy is undergoing through a methodological revolution triggered by an unprecedented wealth of complex and accurate data. The new panchromatic, synoptic sky surveys require advanced tools for discovering patterns and trends hidden behind data which are both complex and of high dimensionality. We present DAMEWARE (DAta Mining & Exploration Web Application REsource): a general purpose, web-based, distributed data mining environment developed for the exploration of large datasets, and finely tuned for astronomical applications. By means of graphical user interfaces, it allows the user to perform classification, regression or clustering tasks with machine learning methods. Salient features of DAMEWARE include its capability to work on large datasets with minimal human intervention, and to deal with a wide variety of real problems such as the classification of globular clusters in the galaxy NGC1399, the evaluation of photometric redshifts and, finally, the identification of candidate Active Galactic Nuclei in multiband photometric surveys. In all these applications, DAMEWARE allowed to achieve better results than those attained with more traditional methods. With the aim of providing potential users with all needed information, in this paper we briefly describe the technological background of DAMEWARE, give a short introduction to some relevant aspects of data mining, followed by a summary of some science cases and, finally, we provide a detailed description of a template use case.

astro-ph.IM↗

arXiv2013

Astrophysical data mining with GPU. A case study: genetic classification of globular clusters

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was successfully tested and validated on the detection of candidate Globular Clusters in deep, wide-field, single band HST images. The GPU version of GAME will be made available to the community by integrating it into the web application DAMEWARE (DAta Mining Web Application REsource (http://dame.dsf.unina.it/beta_info.html), a public data mining service specialized on massive astrophysical data. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm leads to a speedup of a factor of 200x in the training phase with respect to the CPU based version.

astro-ph.IM↗

arXiv2012

Genetic Algorithm Modeling with GPU Parallel Computing Technology

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm has provided an exploit of the internal training features of the model, permitting a strong optimization in terms of processing performances and scalability.

astro-ph.IM↗

arXiv2010

DAME: A Web Oriented Infrastructure for Scientific Data Mining & Exploration

Nowadays, many scientific areas share the same need of being able to deal with massive and distributed datasets and to perform on them complex knowledge extraction tasks. This simple consideration is behind the international efforts to build virtual organizations such as, for instance, the Virtual Observatory (VObs). DAME (DAta Mining & Exploration) is an innovative, general purpose, Web-based, VObs compliant, distributed data mining infrastructure specialized in Massive Data Sets exploration with machine learning methods. Initially fine tuned to deal with astronomical data only, DAME has evolved in a general purpose platform which has found applications also in other domains of human endeavor. We present the products and a short outline of a science case, together with a detailed description of main features available in the beta release of the web application now released.

astro-ph.IM↗