Semi-Supervised Co-Training and Active Learning Based Approach for Multi-View Intrusion Detection
Although there is immense data available from networks and hosts, a very small proportion of this data is labeled due to the cost of obtaining expert labels. This proves to be a significant bottle-neck for developing supervised intrusion detection systems that rely solely on labeled data. In spite of the data being collected from real network environments and hence potentially holding valuable information for intrusion detection, such systems cannot exploit the remaining unlabeled data. In this work, one intelligently leverages both labeled and unlabeled data. Also, intrusion detection tasks naturally lend themselves into a multiview scenario, and can benefit significantly if these multiple views are combined meaningfully.