Cognitive-Affective Emotion Classification: Comparing Features Extraction Algorithm Classified by Multi-Class Support Vector Machine

Diana, Nova Eka; Sabiq, Ahmad

doi:10.2139/ssrn.3417178

Cited by 1 publication

(1 citation statement)

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“…The composition and proportion of the dataset are as shown in Table 1. As we can see, the composition and proportion of various types of traffics in the dataset can reflect the characteristics of the controlled network, and its data dimensionality can meet the requirement of data validation [33].…”

Section: Datasetmentioning

confidence: 99%

Detecting anomalous traffic in the controlled network based on cross entropy and support vector machine

Han

Xue

Yan

2019

IET Information Security

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Network anomaly detection is an effective way for analysing and detecting malicious attacks. However, the typical anomaly detection techniques cannot perform the desired effect in the controlled network just as in the general network. In the circumstance of the controlled network, the detection performance will be lowered due to its special characteristics including the stronger regularity, higher dimensionality and subtler fluctuation of its traffic. On the motivation, the study proposes a novel classifier framework based on cross entropy and support vector machine (SVM). The technique first subtracts the representative traffic characteristics from the network traffic and defines a 7-tuple feature vector for the controlled network by extending the traditional 5-tuple representation of the usual network. Then the probability distributions and cross entropies of the 7 tuples are calculated during the defined statistical window so as to generate the 7-tuple cross-entropy feature vector for profiling the network traffic fluctuation in the controlled network. Finally, the multi-class SVM classifier is trained by importing the 7-tuple cross-entropy feature vectors. Experimental results show that the proposed classifier can achieve higher detection rates and is more suitable to be used in the controlled network than the typical detection techniques. Nomenclature S ip source address of the network session S port source port of the network session D ip destination address of the network session D port destination port of the network session In number of source nodes that is connecting with the observed node in the network session Out number of destination nodes that the observed node is connected within the network session Vel corresponding traffic rates of the different connections in the network session

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Section: Datasetmentioning

confidence: 99%