Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Intrusion Detection System Using Neural Network

Author : Abarna Anusia. S 1 Saveetha.D 2

Date of Publication :31st December 2017

Abstract: Nowadays increase in internet usage and network technologies have led to extent increase in number of attacks and intrusions. Detection of these attacks and intrusion has become an important part of the security. Intrusion Detection System (IDS) is one type of software application that monitors a network or system for malicious activity or policy violation. In this paper, we propose a Hybrid Intrusion Detection System that is combination of both Signature-based and Anomaly-based Intrusion Detection System. The proposed IDS uses a back propagation algorithm and feed forward Artificial Neural Network(ANN) to learn system’s behavior. We use the KDD’99 dataset in our experiments and obtain the expected results.

Reference :

    1. Subba, B., Biswas, S., & Karmakar, S. (2016, March). A Neural Network based system for Intrusion Detection and attack classification. In Communication Subba, B., Biswas, S., & Karmakar, S. (2016, March). A Neural Network based system for Intrusion Detection and attack classification. In Communication
    2. Ádám, N., Madoš, B., Baláž, A., & Pavlik, T. (2017, January). Artificial neural network based IDS. In Applied Machine Intelligence and Informatics (SAMI), 2017 IEEE 15th International Symposium on (pp. 000159-000164). IEEE
    3. El Farissi, I., Saber, M., Chadli, S., Emharraf, M., & Belkasmi, M. G. (2016, October). The analysis performance of an Intrusion Detection Systems based on Neural Network. In Information Science and Technology (CiSt), 2016 4th IEEE International Colloquium on (pp. 145-151). IEEE.
    4. Al-Janabi, S. T. F., & Saeed, H. A. (2011, December). A neural network based anomaly intrusion detection system. In Developments in E-systems Engineering (DeSE), 2011 (pp. 221-226). IEEE.
    5. Kumar, S., Viinikainen, A., & Hamalainen, T. (2016, December). Machine learning classification model for Network based Intrusion Detection System. In Internet Technology and Secured Transactions (ICITST), 2016 11th International Conference for (pp. 242-249). IEEE.

Recent Article