Name
Abstract | ||
|---|---|---|
Efficiently managing and analyzing cloud logs is a difficult and expensive task due the growth in size and variety of formats. In this paper, we propose a binary-based approach for frequency mining correlated attacks in log data. This approach is conceived to work using the MapReduce programming model. Initial experimental results are presented and they serve as the subject of a data mining algorithm to help us predict the likelihood of correlated attacks taking place. (C) 2016 The Authors. Published by Elsevier B.V. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.procs.2016.04.253 | 7TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT 2016) / THE 6TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT-2016) / AFFILIATED WORKSHOPS |
Keywords | Field | DocType |
cloud, big data, logs, log management, binary approach, predict security attacks | Data mining,Programming paradigm,Computer science,Log management,Artificial intelligence,Data mining algorithm,Big data,Machine learning,Cloud computing,Binary number | Conference |
Volume | ISSN | Citations |
83 | 1877-0509 | 1 |
PageRank | References | Authors |
0.39 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mouad Lemoudden | 1 | 1 | 0.39 |
| Meryem Amar | 2 | 1 | 0.72 |
| Bouabid El Ouahidi | 3 | 14 | 6.08 |