Name
Abstract | ||
|---|---|---|
The ever-growing use of the Internet comes with a surging escalation of communication and data access. Most existing intrusion detection systems have assumed the one-size-fits-all solution model. Such IDS is not as economically sustainable for all organizations. Furthermore, studies have found that Recurrent Neural Network out-performs Feed-forward Neural Network, and Elman Network. This paper, therefore, proposes a scalable application-based model for detecting attacks in a communication network using recurrent neural network architecture. Its suitability for online real-time applications and its ability to self-adjust to changes in its input environment cannot be over-emphasized. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ITNG.2010.45 | Information Technology: New Generations |
Keywords | Field | DocType |
recurrent neural networks,scalable intrusion detection,one-size-fits-all solution model,recurrent neural network out-performs,feed-forward neural network,elman network,data access,recurrent neural network architecture,communication network,existing intrusion detection system,ever-growing use,scalable application-based model,scalable,intrusion detection,intrusion,feed forward neural network,intrusion detection system,artificial neural networks,neural networks,recurrent neural network,system,security,feedforward neural network,clustering algorithms,communication,classification algorithms,neural,computer networks,network,support vector machines | Feedforward neural network,Telecommunications network,Computer science,Recurrent neural network,Computer network,Time delay neural network,Artificial neural network,Intrusion detection system,Scalability,Distributed computing,The Internet | Conference |
ISBN | Citations | PageRank |
978-1-4244-6270-4 | 3 | 0.46 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Longy O. Anyanwu | 1 | 3 | 0.80 |
| Jared Keengwe | 2 | 77 | 20.36 |
| Gladys A. Arome | 3 | 3 | 0.80 |