On-Line Intrusion Detection Model Based on Approximate Linear Dependent Condition with Linear Latent Feature Extraction

Most of the intrusion detection models (IDM) are constructed with off-line training data. Time-variance characteristic of the practical network system cannot be embodied in the off-line constructed IDM. On-line updating of the off-line IDM with the valued new samples is very necessary. In this paper, a new on-line instruction detection model based on approximate linear dependent (ALD) condition with linear latent feature extraction is proposed to address this problem. Specifically, the valued samples which can represent drift of the practical network are indentified with ALD and prior knowledge. Then, these selected samples are used to update the off-line IDM based on on-line latent feature extraction method and fast machine learning algorithm with sample-based updating strategy. Experiments based on KDD99 data are used to validate the proposed approach.

[1]  Rui Araújo,et al.  An on-line weighted ensemble of regressor models to handle concept drifts , 2015, Eng. Appl. Artif. Intell..

[2]  Jian Ma,et al.  A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering , 2010, Expert Syst. Appl..

[3]  SunXingming,et al.  Steganalysis of least significant bit matching using multi-order differences , 2014 .

[4]  Xingming Sun,et al.  Segmentation-Based Image Copy-Move Forgery Detection Scheme , 2015, IEEE Transactions on Information Forensics and Security.

[5]  Jian Tang,et al.  Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills , 2015 .

[6]  Zhihua Xia,et al.  Steganalysis of least significant bit matching using multi-order differences , 2014, Secur. Commun. Networks.

[7]  Wen Yu Liu,et al.  Adaptive ensemble modelling approach based on updating sample intelligent identification , 2016 .

[8]  Xingming Sun,et al.  Effective and Efficient Global Context Verification for Image Copy Detection , 2017, IEEE Transactions on Information Forensics and Security.

[9]  Dianhui Wang,et al.  An iterative learning algorithm for feedforward neural networks with random weights , 2016, Inf. Sci..

[10]  Pietro Sabatino,et al.  Ensemble based collaborative and distributed intrusion detection systems: A survey , 2016, J. Netw. Comput. Appl..

[11]  Brett J. Borghetti,et al.  A Survey of Distance and Similarity Measures Used Within Network Intrusion Anomaly Detection , 2015, IEEE Communications Surveys & Tutorials.

[12]  Zhihua Xia,et al.  A Privacy-Preserving and Copy-Deterrence Content-Based Image Retrieval Scheme in Cloud Computing , 2016, IEEE Transactions on Information Forensics and Security.

[13]  Jian Tang,et al.  Feature extraction and selection based on vibration spectrum with application to estimating the load parameters of ball mill in grinding process , 2012 .

[14]  Tianyou Chai,et al.  Soft sensor for parameters of mill load based on multi-spectral segments PLS sub-models and on-line adaptive weighted fusion algorithm , 2012, Neurocomputing.

[15]  Tianyou Chai,et al.  On-line principal component analysis with application to process modeling , 2012, Neurocomputing.