比特流協(xié)議分類模型
發(fā)布時(shí)間:2019-07-04 19:36
【摘要】:針對(duì)比特流協(xié)議分類困難的問(wèn)題,提出一種比特流協(xié)議分類模型,該模型只利用比特流的物理取值和統(tǒng)計(jì)特性,不考慮協(xié)議中各個(gè)部分的語(yǔ)法、語(yǔ)義等信息來(lái)進(jìn)行協(xié)議的分類。將比特流協(xié)議進(jìn)行進(jìn)制轉(zhuǎn)換、數(shù)據(jù)單元切分、對(duì)數(shù)據(jù)單元進(jìn)行詞頻統(tǒng)計(jì),得到對(duì)應(yīng)比特流流協(xié)議的數(shù)據(jù)單元頻率統(tǒng)計(jì)圖;使用基于貝葉斯理論設(shè)計(jì)的機(jī)器學(xué)算法對(duì)其進(jìn)行學(xué)習(xí),得到分類模型,將分類模型用于實(shí)際的協(xié)議分類;诹挚蠈(shí)驗(yàn)室公布的數(shù)據(jù)集測(cè)試結(jié)果表明,該模型能較好地對(duì)比特流協(xié)議進(jìn)行分類,正確率高,運(yùn)行穩(wěn)定、速度快。
[Abstract]:In order to solve the problem that it is difficult to classify bitstream protocol, a bitstream protocol classification model is proposed. The model only makes use of the physical value and statistical characteristics of bitstream, and does not consider the syntax, semantics and other information of each part of the protocol to classify the protocol. The bit flow protocol is converted, the data unit is segmented, and the word frequency statistics of the data unit are carried out, and the data unit frequency statistics diagram of the corresponding bit flow protocol is obtained. The machine algorithm based on Bayesian theory is used to learn it, and the classification model is obtained, and the classification model is applied to the actual protocol classification. The test results based on the dataset published by Lincoln Laboratory show that the model can be classified better than the special flow protocol, with high accuracy, stable operation and fast speed.
【作者單位】: 中國(guó)工程物理研究院計(jì)算機(jī)應(yīng)用研究所;電子科技大學(xué)計(jì)算機(jī)科學(xué)與工程學(xué)院;
【分類號(hào)】:TP393
本文編號(hào):2510171
[Abstract]:In order to solve the problem that it is difficult to classify bitstream protocol, a bitstream protocol classification model is proposed. The model only makes use of the physical value and statistical characteristics of bitstream, and does not consider the syntax, semantics and other information of each part of the protocol to classify the protocol. The bit flow protocol is converted, the data unit is segmented, and the word frequency statistics of the data unit are carried out, and the data unit frequency statistics diagram of the corresponding bit flow protocol is obtained. The machine algorithm based on Bayesian theory is used to learn it, and the classification model is obtained, and the classification model is applied to the actual protocol classification. The test results based on the dataset published by Lincoln Laboratory show that the model can be classified better than the special flow protocol, with high accuracy, stable operation and fast speed.
【作者單位】: 中國(guó)工程物理研究院計(jì)算機(jī)應(yīng)用研究所;電子科技大學(xué)計(jì)算機(jī)科學(xué)與工程學(xué)院;
【分類號(hào)】:TP393
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