Introducing a Two-step Strategy based on Deep Learning Enhance the Accuracy of Intrusion Detection Systems in the Network

Subject Areas : Majlesi Journal of Telecommunication Devices

Ali Bahmani ¹ , Amirhassan Monajemi ²

1 - Islamic Azad University Isfahan
2 -

Received: 2019-03-06 Accepted : 2019-03-13 Published : 2019-03-01

Keywords: Intrusion Detection System, deep learning, Network Security,

Abstract :

Intrusion Detection System is one of the most important security features of modern computer networks that can detect network penetration through a series of functions. This system is independently used (e.g. Snort) or with various security equipment (such as Antivirus, UTM, etc.) on the network and detects an attack based on two techniques of abnormal detection and signature-based detection. Currently, most of the researches in the field of intrusion detection systems have been done based on abnormal behavior using a variety of methods including statistical techniques, Artificial Intelligence (AI), data mining, and machine learning. In this study, we can achieve an effective accuracy using a candidate class of the KDD dataset and deep learning techniques.

References:

1. Rhodes-Ousley M. Information security: the complete reference: McGraw Hill Education; 2013.

2. McClure S, Shah S, Shah S. Web hacking: Attacks and defense: Addison-Wesley Longman Publishing Co., Inc.; 2002.

3. Liao H-J, Lin C-HR, Lin Y-C, Tung K-Y. Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications. 2013;36(1):16-24.

4. Kumar V, Chauhan H, Panwar D. K-means clustering approach to analyze NSL-KDD intrusion detection dataset. International Journal of Soft. 2013.

5. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015;521(7553):436.

6. Kemmerer RA, Vigna G. Intrusion detection: a brief history and overview. Computer. 2002;35(4):supl27-supl30.

7. Lee W, Stolfo SJ, Mok KW, editors. Mining Audit Data to Build Intrusion Detection Models. KDD; 1998.

8. Kabiri P, Ghorbani AA. Research on intrusion detection and response: A survey. IJ Network Security. 2005;1(2):84-102.

9. Chung YY, Wahid N. A hybrid network intrusion detection system using simplified swarm optimization (SSO). Applied Soft Computing. 2012;12(9):3014-22.

10. Zbeel BM. Using Genetic Algorithm for Network Intrusion Detection. kufa studies center journal. 2013;1(29):209-24.

11. Horng S-J, Su M-Y, Chen Y-H, Kao T-W, Chen R-J, Lai J-L, et al. A novel intrusion detection system based on hierarchical clustering and support vector machines. Expert systems with Applications. 2011;38(1):306-13.

12. Chen C-M, Guan D-J, Huang Y-Z, Ou Y-H. Anomaly network intrusion detection using hidden Markov model. Int J Innov Comput Inform Control. 2016;12:569-80.

13. Ashfaq RAR, Wang X-Z, Huang JZ, Abbas H, He Y-L. Fuzziness based semi-supervised learning approach for intrusion detection system. Information Sciences. 2017;378:484-97.

14. Dash T. A study on intrusion detection using neural networks trained with evolutionary algorithms. Soft Computing. 2017;21(10):2687-700.

15. Chen M-H, Chang P-C, Wu J-L. A population-based incremental learning approach with artificial immune system for network intrusion detection. Engineering Applications of Artificial Intelligence. 2016;51:171-81.