基于SVM概率输出的P2P流媒体识别法

P2P流媒体占用大量带宽,且容易传播病毒,有必要对其进行识别.分析了Abacus方法的不足,提出一种基于SVM概率输出的P2P流媒体识别法P-Abacus.P-Abacus将待识别样本属于已知应用可能性的大小反映在概率输出上.对输出结果进行排序,根据最大概率,判决样本是属于最大概率类应用还是未知应用,或是需要进一步判断.若需进一步判断,则通过计算前两大类构建SVM概率输出的差值,来判断样本是属于其中的一类,还是未知应用.由于SVM概率输出包含大量可用信息,使得P-Abacus具有更好的识别效果.实验表明,P-Abacus比Abacus具有更高的识别率和更低的误判率,且时间开销增加有限.

P2P Streaming Media Recognition Method Based on SVM Probabilistic Output

P2P streaming media has taken up a lot of bandwidth, and is prone to spread the virus, so is required to be identified accurately. This paper analyzed the shortcomings of the method P-Abacus, and proposed a kind of P2P streaming media recognition method P-Abacus based on SVM probabilistic output. P-Abacus can express it in probabilistic output, which reflects the extent of the sample belonging to known applications. We ordered the output, and according to maximum probability,made a judgement on that whether the sample belongs to class of maximum probability or unknown,or needs a further judgement If a further judgement is needed,we calculated the probabilistic output difference of the SVM built between the two largest classes, and made sure that whether the sample belongs to one of the two largest classes,or unknown. Thus P-Abacus has a better recognition effect,because probabilistic output contains more information that can be utilized. Experiments show that P-Abacus has a higher recognition rate and a lower false positive rate than Abacus, and has a limited increase of time overhead.