• Corpus ID: 2183556

Mutual Information-based Intrusion Detection Model for Industrial Internet

@article{Dong2018MutualII,
  title={Mutual Information-based Intrusion Detection Model for Industrial Internet},
  author={Rui-Hong Dong and Dong-Fang Wu and Qiu-yu Zhang and Hong-xiang Duan},
  journal={Int. J. Netw. Secur.},
  year={2018},
  volume={20},
  pages={131-140}
}
High dimension, redundancy attributes and high computing cost issues usually exist in the industrial Internet intrusion detection field. [] Key Method Firstly, by using features selection method based on mutual information, the attributes set was reduced and traffic characteristics vector was established. Secondly, the normal and abnormal traffic characteristics maps were obtained via the traffic characteristics map technology based on multi correlation analysis. Finally, with the using of discrete cosineā€¦
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References

SHOWING 1-10 OF 43 REFERENCES

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

The evaluation results show that the feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.

Efficient Feature Selection Technique for Network Intrusion Detection System Using Discrete Differential Evolution and Decision

A significant improvement in detection accuracy, a reduction in training and testing time using the reduced feature set, and the fact that differential evolution (DE) is not limited to optimization of continuous problems but work well for discrete optimization are buttressed.

Feature Selection for Intrusion Detection System Using Ant Colony Optimization

This paper proposes an intrusion detection system that its features are optimally selected using ant colony optimization, providing higher accuracy in detecting intrusion attempts and lower false alarm with reduced number of features.

On efficiency enhancement of the correlation-based feature selection for intrusion detection systems

Experimental results on NSL-KDD dataset shows that the proposed approach with fewer features, significantly outperforms the existing schemes in terms of the training time, time taken to build the model, while it preserves or increases the system accuracy.

The Integrated Artificial Immune Intrusion Detection Model Based on Decision-theoretic Rough Set

An integrated artificial immune intrusion detection model based on decision-theoretic rough set attributes reduction algorithm and real time dendritic cell algorithm analyzed the environment and antigen information obtained and the dynamic increases of rule set was achieved.

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

An ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection that can effectively reduce the number of features and has a high detection rate and classification accuracy when compared to other classification techniques.

A Neural Network based system for Intrusion Detection and attack classification

Experimental results shows that the performance of the proposed ANN based IDS model is at par and in some cases even better than other IDS models, making it a suitable candidate for real time deployment and intrusion detection analysis.

Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs

A new approach consists with merging of feature selection and classification for multiple class NSL-KDD cup 99 intrusion detection dataset employing support vector machine (SVM) to improve the competence of intrusion classification with a significantly reduced set of input features from the training data.

Performance analysis of NSL-KDD dataset using ANN

  • B. IngreA. Yadav
  • Computer Science
    2015 International Conference on Signal Processing and Communication Engineering Systems
  • 2015
The performance of the proposed scheme has been compared with existing scheme and higher detection rate is achieved in both binary class as well as five class classification problems.

Intrusion detection system by improved preprocessing methods and NaĆÆve Bayes classifier using NSL-KDD 99 Dataset

Here NaiĢˆve Bayes classifier is used in supervised learning method which classifies various network events for the KDD cup'99 Dataset, the most commonly used dataset for Intrusion Detection.