A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

Intrusion detection plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. In this paper, we explore how to model an intrusion detection system based on deep learning, and we propose a deep learning approach for intrusion detection using recurrent neural networks (RNN-IDS). Moreover, we study the performance of the model in binary classification and multiclass classification, and the number of neurons and different learning rate impacts on the performance of the proposed model. We compare it with those of J48, artificial neural network, random forest, support vector machine, and other machine learning methods proposed by previous researchers on the benchmark data set. The experimental results show that RNN-IDS is very suitable for modeling a classification model with high accuracy and that its performance is superior to that of traditional machine learning classification methods in both binary and multiclass classification. The RNN-IDS model improves the accuracy of the intrusion detection and provides a new research method for intrusion detection.

[1]  Yuting Su,et al.  Multiple/Single-View Human Action Recognition via Part-Induced Multitask Structural Learning , 2015, IEEE Transactions on Cybernetics.

[2]  Ali A. Ghorbani,et al.  A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[3]  A. Malathi,et al.  A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection , 2013 .

[4]  Mohammad Zulkernine,et al.  Random-Forests-Based Network Intrusion Detection Systems , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[5]  Ilya Sutskever,et al.  Learning Recurrent Neural Networks with Hessian-Free Optimization , 2011, ICML.

[6]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[7]  K. V. N. Sunitha,et al.  Effective discriminant function for intrusion detection using SVM , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[8]  M. A. Jabbar,et al.  Random Forest Modeling for Network Intrusion Detection System , 2016 .

[9]  Ling Shao,et al.  Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach , 2016, IEEE Transactions on Cybernetics.

[10]  Limin Wang,et al.  Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice , 2014, Comput. Vis. Image Underst..

[11]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[12]  Javed Akhtar Khan,et al.  A Survey on Intrusion Detection Systems and Classification Techniques , 2016 .

[13]  Mounir Ghogho,et al.  Deep learning approach for Network Intrusion Detection in Software Defined Networking , 2016, 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM).

[14]  Erhan Guven,et al.  A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2016, IEEE Communications Surveys & Tutorials.

[15]  Mansour Sheikhan,et al.  Intrusion detection using reduced-size RNN based on feature grouping , 2010, Neural Computing and Applications.

[16]  Yue Wu,et al.  A New Intrusion Detection System Based on KNN Classification Algorithm in Wireless Sensor Network , 2014, J. Electr. Comput. Eng..

[17]  Siyang Zhang,et al.  A novel hybrid KPCA and SVM with GA model for intrusion detection , 2014, Appl. Soft Comput..

[18]  Wenhui Li,et al.  Cross-view action recognition by cross-domain learning , 2016, Image Vis. Comput..

[19]  Mansoor Alam,et al.  A Deep Learning Approach for Network Intrusion Detection System , 2016, EAI Endorsed Trans. Security Safety.

[20]  Partha Sarathi Bhattacharjee.  Intrusion Detection System for NSL-KDD Data Set using Vectorised Fitness Function in Genetic Algorithm , 2017 .

[21]  Yu-Lin He,et al.  Fuzziness based semi-supervised learning approach for intrusion detection system , 2017, Inf. Sci..

[22]  Anamika Yadav,et al.  Performance analysis of NSL-KDD dataset using ANN , 2015, 2015 International Conference on Signal Processing and Communication Engineering Systems.

[23]  Nerijus Paulauskas,et al.  Analysis of data pre-processing influence on intrusion detection using NSL-KDD dataset , 2017, 2017 Open Conference of Electrical, Electronic and Information Sciences (eStream).

[24]  Jianxin Wu,et al.  Good Practices for Learning to Recognize Actions Using FV and VLAD , 2016, IEEE Transactions on Cybernetics.

[25]  Mohan S. Kankanhalli,et al.  Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.