Entropy Based Method for Network Anomaly Detection

Qian, Quan; Che, Hongyi; Zhang, Rui

doi:10.1109/prdc.2009.38

Cited by 13 publications

(1 citation statement)

References 3 publications

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“…Isso seria um problema, por exemplo, para distinguir o comportamento de conjuntos de atributos. Neste caso, uma possível solução seria a medida Kullback-Leibler que calcula a entropia relativa entre duas distribuições (Quan et al, 2009). No problema de detecção de intrusão, ataques podem ser caracterizados por um número grande de requisições tal como o ataque por negação de serviço.…”

Section: Técnicas Baseadas Na Teoria Da Informaçãounclassified

Técnica de aprendizado semissupervisionado para detecção de outliers

Zamoner¹

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Outlier detection plays an important role for discovering knowledge in large data sets. The study is motivated by a plethora of real applications such as credit card frauds, fault detection in industrial components, network intrusion detection, loan application processing and medical condition monitoring. An outlier is defined as an observation that deviates from other observations with respect to a measure and exerts a substantial influence on data analysis. Although numerous machine learning techniques have been developed for attacking this problem, most of them work with no prior knowledge of the data. Semi-supervised outlier detection techniques are relatively new and include only a few labels of normal class for building a classifier. Recently, a network-based semi-supervised model was proposed for data classification by employing a mechanism based on particle competition and cooperation. Such particles are responsible for label propagation throughout the network. In this work, we adapt this model by defining a new outlier score based on visit frequency counting. The number of visits received by an outlier is significantly different from the remaining objects. This approach leads to an unorthodox way to deal with outliers. Our empirical evaluations on both real and simulated data sets demonstrate that the proposed technique works well with unbalanced data sets and achieves a precision compared to traditional outlier detection techniques. Moreover, the technique might provide new insights into how to differentiate objects because it considers not only the physical distance but also the pattern formation of the data.

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Section: Técnicas Baseadas Na Teoria Da Informaçãounclassified