Journal's Facebook pageResearch of MDCOP Mining Based on Time Aggregated Graph for Large Spatio-temproal Data Sets
- Department of Computer Science and Engineering, East China University of Science and Technology
Shanghai 200237, China
zhqwang@ecust.edu.cn, 2730023187@qq.com, yhq@ecust.edu.cn - Shanghai Engineering Research Center of Smart Energy
Shanghai, 200237, China
Abstract
Discovering mixed-drove spatiotemporal co-occurrence patterns (MD-COPs) is important for network security such as distributed denial of service (DDoS) attack. There are usually many features when we are suffering from a DDoS attacks such as the server CPU is heavily occupied for a long time, bandwidth is hoovered and so on. In distributed cooperative intrusion, the feature information from multi-ple intrusion detection sources should be analyzed simultaneously to find the spatial correlation among the feature information.In addition to spatial correlation, intrusion also has temporal correlation. Some invasions are gradually penetrating, and attacks are the result of cumulative effects over a period of time. So it is necessary to discover mixed-drove spatiotemporal co-occurrence patterns (MDCOPs) in network security. However, it is difficult to mine MDCOPs from large attack event data sets because mining MDCOPs is computationally very expensive. In informa-tion security, the set of candidate co-occurrence attack event data sets is exponential in the number of object-types and the spatiotemporal data sets are too large to be managed in memory. To reduce the number of candidate co-occurrence instances, we present a computationally efficient MDCOP Graph Miner algorithm by using Time Aggregated Graph. which can deal with large attack event data sets by means of file index. The correctness, completeness and efficiency of the proposed methods are analyzed.
Key words
Network spatiotemporal co-occurrence pattern intrusion detection, mixed-drove spatiotemporal co-occurrence pattern, large spatiotemporal data set, TimeAggregated Graph (TAG), file index
Digital Object Identifier (DOI)
https://doi.org/10.2298/CSIS180828032W
Publication information
Volume 16, Issue 3 (October 2019)
Recent Advances in Information Processing and Security
Year of Publication: 2019
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
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How to cite
Wang, Z., Han, T., Yu, H.: Research of MDCOP Mining Based on Time Aggregated Graph for Large Spatio-temproal Data Sets. Computer Science and Information Systems, Vol. 16, No. 3, 891–914. (2019), https://doi.org/10.2298/CSIS180828032W
