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The increasing popularity of approaches based on random forest in computer vision tasks is due to its simplicity and flexibility with complex data. Random forest is a set of decision trees that can be divided in two subsets according to the view of the feature descriptors provided as input: orthogonal and oblique. In the former, the feature space is separated orthogonally (axis-aligned) by a single...
In this paper, we present our analysis using four different systems on two different one-way network traffic data sets. Specifically, we have explored the usage of two network traffic analyzers, namely Corsaro and Cisco ASA 5515-X, and two machine learning based systems, namely the C4.5 Decision Tree classifier and the AdaBoost.M1 classifier. We have employed these four systems on two publicly available...
Traffic classification becomes more challenging since the traditional techniques such as port numbers or deep packet inspection are ineffective against voice over IP (VoIP) applications, which uses non-standard ports and encryption. Statistical information based on network layer with the use of machine learning (ML) can achieve high classification accuracy and produce transportable signatures. However,...
Total Order Broadcast (TOB) is a fundamental building block at the core of a number of strongly consistent, fault-tolerant replication schemes. While it is widely known that the performance of existing TOB algorithms varies greatly depending on the workload and deployment scenarios, the problem of how to forecast their performance in realistic settings is, at current date, still largely unexplored...
We analyzed the memory limitation problem of traffic identification arithmetic when faced the large and fast stream data, and extracted the traffic attribute based on the P2P working mechanism. Using VFDT method to identify the P2P traffic can scan the traffic data only once relying on the Hoeffding Restriction, the method reduce the complexity of algorithm on the part of timing and memory and ensure...
Classifying network traffic is very challenging and is still an issue yet to be solved due to the increase of new applications and traffic encryption. In this paper, we propose a novel hybrid approach for the network flow classification, in which we first apply the payload signature based classifier to identify the flow applications and unknown flows are then identified by a decision tree based classifier...
Because of BGP's critical importance as the de-facto Internet inter domain routing protocol, accurate and quick detection of abnormal BGP routing dynamics is of fundamental importance to Internet security, where the costs of different errors are unequal. In such situation, cost-sensitive learning is a good solution. This paper studies the effect of both over-sampling and under-sampling in training...
The early identification of applications through the observation and fast analysis of the associated packet flows is a critical building block of intrusion detection and policy enforcement systems. The simple techniques currently used in practice, such as looking at the transport port numbers or at the application payload, are increasingly less effective for new applications using random port numbers...
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