Name
Abstract | ||
|---|---|---|
This paper presents an approach, based in a project in development, which combines Data Mining, Machine Learning and Computational Intelligence techniques, in order to create a user-centric and adaptable corporate security system. Thus, the system, named MUSES, will be able to analyse the user's behaviour (modelled as events) when interacting with the company's server, accessing to corporate assets, for instance. As a result of this analysis, and after the application of the aforementioned techniques, the Corporate Security Policies, and specifically, the Corporate Security Rules will be adapted to deal with new anomalous situations, or to better manage user's behaviour. The work reviews the current state of the art in security issues resolution by means of these kind of methods. Then it describes the MUSES features in this respect and compares them with the existing approaches. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2598394.2605438 | GECCO (Companion) |
Keywords | Field | DocType |
corporate security policies,computational intelligence,evolutionary computation,applications and expert systems,access controls,security rules | Security convergence,Security through obscurity,Computational intelligence,Computer science,Information security standards,Evolutionary computation,Cloud computing security,Artificial intelligence,Corporate security,Computer security model,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.36 | 26 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Miguel Mora | 1 | 314 | 42.81 |
| Paloma de las Cuevas | 2 | 23 | 5.20 |
| Juan Julián Merelo Guervós | 3 | 483 | 75.75 |
| Sergio Zamarripa | 4 | 2 | 0.36 |
| Anna Isabel Esparcia-Alcázar | 5 | 59 | 9.10 |