Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Classification of network traffic is basic and essential for many network researches and managements. Hoverclassification of network traffic using port-based and simple payload-based methods is diminished with the rapid development of peer-to-peer (P2P) application using dynamic port, disguising techniques and encryption to avoid detection. An alternative method based on statistics and machine learning has attracted researchers’ attention in recent years. , a new approach based on the implementation of artificial neural network ensemble with the error-correcting output codes (ECOC) is proposed for classification of multi-class network traffic. As the error-correcting output codes have error correcting ability and improve the generalization ability of the base classifiers the experiments show that the proposed method can improve the multi-class classification accuracy by 12%-20% on datasets captured on the backbone router of our campus through a ek.
['Xiao Xie', 'Bo Yang', 'Yuehui Chen', 'Lin Wang', 'Zhenxiang Chen']
I am writing a research paper on a idea Network Traffic Classification Based on Error-Correcting Output Codes and NN Ensemble
[{"from": "human", "value": "I am writing a research paper on a idea Network Traffic Classification Based on Error-Correcting Output Codes and NN Ensemble"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Classification of network traffic is basic and essential for many network researches and managements. Hoverclassification of network traffic using port-based and simple payload-based methods is diminished with the rapid development of peer-to-peer (P2P) application using dynamic port, disguising techniques and encryption to avoid detection. An alternative method based on statistics and machine learning has attracted researchers\u2019 attention in recent years. , a new approach based on the implementation of artificial neural network ensemble with the error-correcting output codes (ECOC) is proposed for classification of multi-class network traffic. As the error-correcting output codes have error correcting ability and improve the generalization ability of the base classifiers the experiments show that the proposed method can improve the multi-class classification accuracy by 12%-20% on datasets captured on the backbone router of our campus through a ek."}]