Abstract-Recently, threat of previously unknown cyber-attacks are increasing because existing security systems are not able to detect them. Past cyber-attacks had simple purposes of leaking personal information by attacking the PC or destroying the system. However, the goal of recent hacking attacks has changed from leaking information and destruction of services to attacking large-scale systems such as critical infrastructures and state agencies. In the other words, existing defence technologies to counter these attacks are based on pattern matching methods which are very limited. Because of this fact, in the event of new and previously unknown attacks, detection rate becomes very low and false negative increases. To defend against these unknown attacks, which cannot be detected with existing technology, we propose a new model based on big data analysis techniques that can extract information from a variety of sources to detect future attacks. We expect our model to be the basis of the future Advanced Persistent Threat(APT) detection and prevention system implementations.
In order to achieve service differentiation, especially loss differentiation, in optical burst switching (OBS) networks, we propose a dynamic fiber delay line (FDL) partitioning algorithm, which divides FDLs into several groups over a feed-forward output buffering architecture. In the proposed scheme, a plurality of traffic classes and FDL groups can be considered, and each FDL group is assigned to traffic classes, so that the target loss probabilities of classes are guaranteed. Also, the optimal number of FDLs for each FDL group by the proposed algorithm is decided in Poisson traffic environments. The extensive simulation results validate the effectiveness of the proposed dynamic FDL partitioning algorithm for the loss differentiation in OBS networks.
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