Large Scale Graph Based Network Forensics Analysis
In this paper we tackle the problem of performing graph based network forensics analysis at a large scale. To this end, we propose a novel distributed version of a popular network forensics analysis algorithm, the one by Wang and Daniels [18]. Our version of the Wang and Daniels algorithm has been f...
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| Published in | Pattern Recognition. ICPR International Workshops and Challenges Vol. 12665; pp. 457 - 469 |
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| Main Authors | , , |
| Format | Book Chapter |
| Language | English |
| Published |
Switzerland
Springer International Publishing AG
2021
Springer International Publishing |
| Series | Lecture Notes in Computer Science |
| Online Access | Get full text |
| ISBN | 3030688208 9783030688202 |
| ISSN | 0302-9743 1611-3349 |
| DOI | 10.1007/978-3-030-68821-9_39 |
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| Abstract | In this paper we tackle the problem of performing graph based network forensics analysis at a large scale. To this end, we propose a novel distributed version of a popular network forensics analysis algorithm, the one by Wang and Daniels [18].
Our version of the Wang and Daniels algorithm has been formulated according to the MapReduce paradigm and implemented using the Apache Spark framework. The resulting code is able to analyze in a scalable way graphs of arbitrary size thanks to its distributed nature. We also present the results of an experimental study where we assessed both the time performance and the scalability of our algorithm when run on a distributed system of increasing size. |
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| AbstractList | In this paper we tackle the problem of performing graph based network forensics analysis at a large scale. To this end, we propose a novel distributed version of a popular network forensics analysis algorithm, the one by Wang and Daniels [18].
Our version of the Wang and Daniels algorithm has been formulated according to the MapReduce paradigm and implemented using the Apache Spark framework. The resulting code is able to analyze in a scalable way graphs of arbitrary size thanks to its distributed nature. We also present the results of an experimental study where we assessed both the time performance and the scalability of our algorithm when run on a distributed system of increasing size. |
| Author | Petrillo, Umberto Ferraro Palini, Francesco Di Rocco, Lorenzo |
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| DOI | 10.1007/978-3-030-68821-9_39 |
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| Editor | Del Bimbo, Alberto Bertini, Marco Vezzani, Roberto Sclaroff, Stan Mei, Tao Farinella, Giovanni Maria Cucchiara, Rita Escalante, Hugo Jair |
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| PublicationTitle | Pattern Recognition. ICPR International Workshops and Challenges |
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