In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets with non-zero flow payload sizes are selected and their payload sizes are used as the early-stage flow features. Such features can be easily and rapidly extracted at the early flow stage, which makes them outstanding. The behavior patterns of different Intemet applications are analyzed by visualizing the early-stage packet size values. Analysis results show that most Internet applications can reflect their own early packet size behavior patterns. Early packet sizes are assumed to carry enough information for effective traffic identification. Three classical machine learning classifiers, classifier, naive Bayesian trees, i. e., the naive Bayesian and the radial basis function neural networks, are used to validate the effectiveness of the proposed assumption. The experimental results show that the early stage packet sizes can be used as features for traffic identification.
2015年10月27日,美国参议院以74票赞成、21票反对,通过了《网络安全信息共享法案》(Cybersecurity Information Sharing Act of 2015,CISA),该法案旨在鼓励私企与美国政府实时共享网络安全威胁信息,达到改善美国网络空间安全的目的。这是继2014年4月26日美国众议院通过《网络情报共享与保护法案》后。
