Asian Journal of Information Technology
ISSN: Online 1993-5994ISSN: Print 1682-3915
Abstract
Network security systems are now mainly employed to secure company networks. An Intrusion Detection System has the capacity to detect in real-time all intrusions and to execute work to stop the attack. With the growth of computer networking, electronic commerce and web services, security of networking systems has become very important. Many companies now rely on web services as a major source of revenue. Computer hacking poses significant problems to these companies as distributed attacks can render their cyber-store front inoperable for long periods of time. This happens so often that an entire area of research, called intrusion detection has been devoted to detecting this activity. We show that evidence of many of these attacks can be found in a careful analysis of network data. We also illustrate that the learning abilities of neural networks can serve to detect this activity. Intrusion detection is an essential mechanism to protect computer systems from many attacks. Besides the classical prevention security tools, Intrusion Detection Systems (IDS) are nowadays widely used by the security administrators. In this study, we present Intrusion Detection Systems using neural networks.
How to cite this article:
R. Mohan Raj and S. Anbu. Quantification of Network Security Situational Based Awareness on Neural Networks.
DOI: https://doi.org/10.36478/ajit.2014.755.760
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2014.755.760