Intrusion detection is facing great challenges as network attacks producing massive volumes of data are increasingly sophisticated and heterogeneous. In order to gain much more accurate and reliable detection results, machine learning and visualization techniques have been respectively applied to intrusion detection. In this paper, we review some important work related to machine learning and visualization techniques for intrusion detection. We present a collaborative analysis architecture for intrusion detection tasks which integrate both machine learning and visualization techniques into intrusion detection. We also discuss some significant issues related to the proposed collaborative analysis architecture.
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