Anomaly Based Intrusion Detection System Using Neural Network
DOI:
https://doi.org/10.61841/8yj4cg88Keywords:
Intrusion Detection, Neural Networks, Cyber Security, , Machine Learning, , Deep Learning.Abstract
Intrusion detection system (or IDS) is an integral part of any Information and Communication Technology (or ICT) system. Building an efficient and reliable IDS that accurately detects an attempt to compromise the network using some known or unknown vulnerability in real time is still a huge challenge. We attempt to create a state-of-the-art Deep Neural Network that analyses the network traffic in real time, identifies an attempt to compromise a network, classifies the type of attack and then compare its accuracy and efficiency with that of conservative and existing models.
Downloads
References
1. Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey by Elike Hodo, Xavier Bellekens, Andrew Hamilton, Christos Tachtatzis and Robert Atkinson.
2. Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security by Rahul Vigneswaran K, Vinayakumar R, Soman KP and Prabaharan Poornachandran
3. Feature Extraction Methods for Intrusion Detection Systems by Hai Thanh Nguyen, Katrin Franke, Slobodan Petrović
4. Study on implementation of machine learning methods combination for improving attacks detection accuracy on Intrusion Detection System (IDS) by Bisyron Wahyudi Masduki, Kalamullah Ramli, Ferry Astika Saputra, Dedy Sugiarto
5. A Comparative Analysis of SVM and its Stacking with other Classification Algorithm for Intrusion Detection by Nanak Chand, Preeti Mishra, C. Rama Krishna, Emmanuel Shubhakar Pilli and Mahesh Chandra Govil
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.