Name
Abstract | ||
|---|---|---|
We propose a hypergraph-based framework for modeling and detecting malevolent activities. The proposed model supports the specification of order-independent sets of action symbols along with temporal and cardinality constraints on the execution of actions. We study and characterize the problems of consistency checking, equivalence, and minimality of hypergraph-based models. In addition, we define and characterize the general activity detection problem, that amounts to finding all subsequences that represent a malevolent activity in a sequence of logged actions. Since the problem is intractable, we also develop an index data structure that allows the security expert to efficiently extract occurrences of activities of interest. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TKDE.2017.2658621 | IEEE Trans. Knowl. Data Eng. |
Keywords | Field | DocType |
Security,Hidden Markov models,Analytical models,Servers,Data models,Indexes,Correlation | Data mining,Data modeling,Computer science,Server,Hypergraph,Cardinality,Theoretical computer science,Equivalence (measure theory),Artificial intelligence,Data structure,Activity detection,Hidden Markov model,Machine learning | Journal |
Volume | Issue | ISSN |
29 | 5 | 1041-4347 |
Citations | PageRank | References |
2 | 0.37 | 24 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonella Guzzo | 1 | 497 | 39.90 |
| A. Pugliese | 2 | 115 | 12.90 |
| Antonino Rullo | 3 | 45 | 6.48 |
| Domenico Sacca | 4 | 1936 | 579.90 |
| Antonio Piccolo | 5 | 140 | 18.21 |