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Currently, our society is undergoing a radical change due to the increasing pervasive use of ICT within all of its processes. Such an evolution has been triggered by the advent of the Internet of Things vision, where smart sensing devices can be integrated in the daily objects surrounding any human being. The increasing demand of dealing with the big data generated, managed, and stored by the applications...
The past decade has seen a lot of research on statistics-based network protocol identification using machine learning techniques. Prior studies have shown promising results in terms of high accuracy and fast classification speed. However, most works have embodied an implicit assumption that all protocols are known in advance and presented in the training data, which is unrealistic since real-world...
Signal-oriented software architecture is a trend for universal automatic test system development, and the establishment of a platform providing unifying external signal interface and testing services function is a key techniques for signal-oriented programming. Automatic-test universal supported platform isolates the use of resources and control details, hides the concrete realization of the relevant...
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...
Providing flexibility and extensibility, SIP has recently gained significant attention in many areas. Critical requirements on reliability, fault tolerance and security highlight the necessity of SIP robustness testing. There are only a few researches on SIP robustness testing. The biggest challenge is the anomalous message generation. This paper proposes a method and architecture for SIP robustness...
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