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One crucial challenge in network flow monitoring is how to accurately and efficiently monitor the large volume of network flows. Several approaches proposed to address this challenge either lack flexibility adapting to greatly varying network traffic (e.g. sNetFlow), or require intensive computing resources (e.g. ANF). In this paper, we propose a novel double-sampling and hold approach for net work...
Reports show that DDoS attacks are ubiquitous on the Internet and may jeopardize networks’ stable operation. In order to understand the nature of this threat and further to enable effective control and management, a whole picture of the Internet-wide attacks is a necessity. Traditional methods use darknets to this end. However, with the IPv4 address space exhaustion, darknets become hard to acquire...
The ultimate goal of distance metric learning is to use discriminative information to keep data samples in the same class close, and those in different classes separate. Local distance metric methods can preserve discriminative information by considering neighborhood influence. We propose a discriminative distance metric approach by maximizing local pair wise constraints. Based on the local learning...
this paper focus on analyzing traffic characterization of a large-scale access network based on active IP address count and link utilization. We find out that the behavior of internal and external hosts is asymmetric, the utilization of internal IP addresses per day is about 18.64% and the maximum link utilization is about 53%. Based on these, we proposed a scheme for detecting traffic anomalies which...
In this paper, we study a new research problem of causal discovery from streaming features. A unique characteristic of streaming features is that not all features can be available before learning begins. Feature generation and selection often have to be interleaved. Managing streaming features has been extensively studied in classification, but little attention has been paid to the problem of causal...
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