| Revista: | Polibits |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000383467 |
| ISSN: | 1870-9044 |
| Autores: | García, Mario A1 Trinh, Tung1 |
| Instituciones: | 1Texas A&M University, Computer Science, Corpus Christi, Texas. Estados Unidos de América |
| Año: | 2015 |
| Periodo: | Ene-Jun |
| Número: | 51 |
| Paginación: | 5-10 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Aplicado, descriptivo |
| Resumen en inglés | In a large network, it is extremely difficult for an administrator or security personnel to detect which computers are being attacked and from where intrusions come. Intrusion detection systems using neural networks have been deemed a promising solution to detect such attacks. The reason is that neural networks have some advantages such as learning from training and being able to categorize data. Many studies have been done on applying neural networks in intrusion detection systems. This work presents a study of applying resilient propagation neural networks to detect simulated attacks. The approach includes two main components: the Data Preprocessing module and the Neural Network. The Data Preprocessing module performs normalizing data function while the Neural Network processes and categorizes each connection to find out attacks. The results produced by this approach are compared with present approaches |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Redes, Seguridad en cómputo, Redes neuronales artificiales |
| Keyword: | Computer science, Networks, Computing security, Artificial neural networks |
| Texto completo: | Texto completo (Ver HTML) |
