A HYBRID APPROACH OF INTRUSION DETECTION SYSTEM BASED ON NEURAL NETWORK AND NORMALIZATION
Keywords:Intrusion, Detection, Attacks, Neural Network, KYOTO.
In the whole world, the most famous threat that are spread around is done by the intruder computers over the internet. The types of external activity found over the system are termed as intrusion and the mechanism that is applied for the preservation of the information against these intrusions are called as intrusion detection system. For protecting the network, first there is a need to detect the attacks then take the proper action regarding it. There are techniques applied for scanning and analysing for highlighting the susceptibilities and loop-holes within the components of security, various aspects of network that are not secured and also implementation of the intrusion-detection and prevention-system techniques are also described here. In this paper, proposed methods based on Neural Network is described that provides better way of attack detection, that are required in various applications of security such as network forensics, portable computer and the event handling systems by applying various different approaches. Proposed work is implemented in MATALB.
How to Cite
Copyright (c) 2016 Kavita Patil
This work is licensed under a Creative Commons Attribution 4.0 International License.
IJOSCIENCE follows an Open Journal Access policy. Authors retain the copyright of the original work and grant the rights of publication to the publisher with the work simultaneously licensed under a Creative Commons CC BY License that allows others to distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation. Authors are permitted to post their work in institutional repositories, social media or other platforms.
Under the following terms:
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.