A Network Intrusion Detection Model Based on Convolutional Neural Network

Intrusion detection is an important research direction in the field of power monitoring network security. The increase of data volume and the diversification of intrusion modes make the traditional detection methods unable to meet the requirements of the current network environment. The emergence of convolutional neural network provides a new way to solve this dilemma. An intrusion detection model based on convolutional neural network is proposed in this paper. The method that converts the flow data into an image is used to represent the flow data in the form of a grayscale image, and use the texture representation in the image to classify the intrusion modes. Through the conversion of traffic data to images, the intrusion detection problem is transformed into image recognition problem, which substitute convolutional neural network technology into the intrusion detection problem. Firstly, the intrusion data set KDD 99 is preprocessed, and generate a two-dimensional image matrix group that meets the requirements. Then, the appropriate model structure for training is selected through comparison experiments. Finally, comparing the trained model with the other machine learning methods is to verify the model about reliability and effectiveness.

[1]  Geoffrey E. Hinton Deep belief networks , 2009, Scholarpedia.

[2]  Shreya Dubey,et al.  KBB: A hybrid method for intrusion detection , 2015, 2015 International Conference on Computer, Communication and Control (IC4).

[3]  Abien Fred Agarap A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data , 2017, ICMLC.

[4]  Kai Huang,et al.  Intrusion Detection Using Convolutional Neural Networks for Representation Learning , 2017, ICONIP.

[5]  Ralf C. Staudemeyer,et al.  Applying long short-term memory recurrent neural networks to intrusion detection , 2015 .

[6]  Xiaohong Yuan,et al.  Semi-Supervised Deep Neural Network for Network Intrusion Detection , 2016 .

[7]  Nhien-An Le-Khac,et al.  Collective Anomaly Detection Based on Long Short-Term Memory Recurrent Neural Networks , 2016, FDSE.

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

[9]  Md Zahangir Alom,et al.  Intrusion detection using deep belief networks , 2015, 2015 National Aerospace and Electronics Conference (NAECON).

[10]  Claudia Eckert,et al.  Deep Learning for Classification of Malware System Call Sequences , 2016, Australasian Conference on Artificial Intelligence.

[11]  Sufyan T. Faraj Al-Janabi,et al.  A Neural Network Based Anomaly Intrusion Detection System , 2011, 2011 Developments in E-systems Engineering.

[12]  Thomas H. Morris,et al.  Developing a Hybrid Intrusion Detection System Using Data Mining for Power Systems , 2015, IEEE Transactions on Smart Grid.