Semi-supervised and compound classification of network traffic

This paper presents a new semi-supervised method to effectively improve traffic classification performance when few supervised training data are available. Existing semi supervised methods label a large proportion of testing flows as unknown flows due to limited supervised information, which severely affects the classification performance. To address this problem, we propose to incorporate flow correlation into both training and testing stages. At the training stage, we make use of flow correlation to extend the supervised data set by automatically labeling unlabeled flows according to their correlation to the pre-labeled flows. Consequently, the traffic classifier has better performance due to the extended size and quality of the supervised data sets. At the testing stage, the correlated flows are identified and classified jointly by combining their individual predictions, so as to further boost the classification accuracy. The empirical study on the real-world network traffic shows that the proposed method outperforms the state-of-the-art flow statistical feature based classification methods.

Classification of correlated internet traffic flows

A critical problem for Internet traffic classification is how to obtain a high-performance statistical feature based classifier using a small set of training data. The solutions to this problem are essential to deal with the encrypted applications and the new emerging applications. In this paper, we propose a new Naive Bayes (NB) based classification scheme to tackle this problem, which utilizes two recent research findings, feature discretization and flow correlation. A new bag-of-flow (BoF) model is firstly introduced to describe the correlated flows and it leads to a new BoF-based traffic classification problem. We cast the BoF-based traffic classification as a specific classifier combination problem and theoretically analyze the classification benefit from flow aggregation. A number of combination methods are also formulated and used to aggregate the NB predictions of the correlated flows. Finally, we carry out a number of experiments on a large scale real-world network dataset. The experimental results show that the proposed scheme can achieve significantly higher classification accuracy and much faster classification speed with comparison to the state-of-the-art traffic classification methods.

Congestion and optimal immigration policy

This paper presents a model to explain the stylized fact that many countries have a low ratio of migrants in their population while some countries have a high ratio of migrants. Immigration improves the income of the domestic residents, but migrants also increase the congestion of public services. If migrants are unskilled and therefore pay low taxes, and the government does not limit access to these services, then the welfare of the domestic residents decreases with the number of migrants. Visa auctions can lower the cost of immigration control and substitute legal migrants for illegal migrants. If the government decides to limit the access of migrants to public services, immigration control becomes unnecessary and the optimal number of migrants can be very large.

Concept definition in knowledge management : the development of an organizational memory scale

Theorists and researchers in the field of Knowledge Management are frequently frustrated by issues with concept definition, as illustrated by the following comment "there remains disagreement on methodologies, definitions and processes" from the summary article "Issues Raised at ECKM, 2008". How can we clearly define constructs of interest? How can we further research and understanding in the field if we are speaking with different vocabularies? This paper illustrates some of these issues by describing the concept definition process involved in the development of an organizational memory scale. The example being used to illustrate these issues was a self-report scale of organizational memory developed to survey experienced workers' attitudes to mentoring others to pass on their knowledge. The current research sought to differentiate between the types of organizational knowledge that experienced workers have and the possible relationships these have with attitudes pertaining to knowledge transfer via mentoring. Defining the construct to be measured is the vital first ingredient in scale development. Many researchers lament that the concept of organizational memory is a "rather loosely defined and under-developed concept" (e.g. Johnson & Paper, 1998, p.504), and this hints at the challenges that concept definition can entail. Furthermore, in the early stages of this particular project it became clear that the organizational memory scale had similar aims, and was able to borrow from, an existing sale of organizational socialization (Chao, O'Leary-Kelly, Woolf, Klein & Gardner, 1994). This paper describes the concept definition process involved in the development of the scale along with results from the exploratory factor analysis. There is a discussion of the relative contribution that the organizational memory scale makes alongside the existing measure of socialization (Chao et al., 1994), along with goals for further development.

Internet traffic classification by aggregating correlated naive bayes predictions

This paper presents a novel traffic classification scheme to improve classification performance when few training data arc available. In the proposed scheme, traffic flows are described using the discretized statistical features and flow correlation information is modeled by bag-of-flow (BoF). We solve the BoF-based traffic classification in a classifier combination framework and theoretically analyze the performance benefit. Furthermore, a new BoF-based traffic classification method is proposed to aggregate the naive Bayes (NB) predictions of the correlated flows. We also present an analysis on prediction error sensitivity of the aggregation strategies. Finally, a large number of experiments are carried out on two large-scale real-world traffic datasets to evaluate the proposed scheme. The experimental results show that the proposed scheme can achieve much better classification performance than existing state-of-the-art traffic classification methods.

An effective network traffic classification method with unknown flow detection

Traffic classification technique is an essential tool for network and system security in the complex environments such as cloud computing based environment. The state-of-the-art traffic classification methods aim to take the advantages of flow statistical features and machine learning techniques, however the classification performance is severely affected by limited supervised information and unknown applications. To achieve effective network traffic classification, we propose a new method to tackle the problem of unknown applications in the crucial situation of a small supervised training set. The proposed method possesses the superior capability of detecting unknown flows generated by unknown applications and utilizing the correlation information among real-world network traffic to boost the classification performance. A theoretical analysis is provided to confirm performance benefit of the proposed method. Moreover, the comprehensive performance evaluation conducted on two real-world network traffic datasets shows that the proposed scheme outperforms the existing methods in the critical network environment.