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We propose a graphical signature for intrusion detection given alert sequences. By correlating alerts with their temporal proximity, we build a probabilistic graph-based model to describe a group of alerts that form an attack or normal behavior. Using the models, we design a pairwise measure based on manifold learning to measure the dissimilarities between different groups of alerts. A large dissimilarity...
Anomaly detection in sensor network seems a challenge when encountering the limitation of the energy requirement and dynamics environments. It is to rapidly analyze and identify the abnormal events among the extreme volume data. Using correlation graph representation to correlate the events generated by sensor networks is capable to find the intentional dependency behavior's insight for detecting...
This paper proposes the Network Motif Model (NMM), a novel and efficient approach for extracting features from relational data. First, our approach constructs a data network according to the data relation. Then significant sub-graphs are identified by extracting the basic network motifs from the data network, inspired by the motif concepts of complex network. At last, the first-order information of...
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