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A crucial aspect in network monitoring for security purposes is the visual inspection of the traffic pattern, mainly aimed to provide the network manager with a synthetic and intuitive representation of the current situation. Toward that end, neural projection techniques can map high-dimensional data into a low-dimensional space adaptively, for the user-friendly visualization of monitored network traffic. This work proposes two projection methods, namely, Cooperative Maximum Likelihood Hebbiandoi:10.1016/j.neucom.2008.12.038 fatcat:vi55fw5osrawhi3ijw3zswosga