Anomaly Detection

Ma, H. et al., 2020. Isolation Mondrian Forest for Batch and Online Anomaly Detection. IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2020. Available at: arXiv preprint arXiv:2003.03692. Also available at:

We propose a new method, named isolation Mon- drian forest (iMondrian forest), for batch and online anomaly detection. The proposed method is a novel hybrid of isolation forest and Mondrian forest which are existing methods for batch anomaly detection and online random forest, respectively. iMondrian forest takes the idea of isolation, using the depth of a node in a tree, and implements it in the Mondrian forest structure. The result is a new data structure which can accept streaming data in an online manner while being used for anomaly detection. Our experiments show that iMondrian forest mostly performs better than isolation forest in batch settings and has better or comparable performance against other batch and online anomaly detection methods.

Benyamin Ghojogh

Dr. Benyamin Ghojogh (graduated)

PhD. Electrical and Computer Engineering, University of Waterloo
MSc. Electrical Engineering, Sharif University of Technology
B.Eng, Electrical Engineering, Amirkabir University of Technology

Beyamin is a postdoctoral scholar student working on theoretical and applied advances in manifold learning, data reduction and dimensionality...

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Maria Samad

MEng ECE, Ned University of Engineering and Technology
BEng Electrical and Computer Engineering, Carleton University

Maria is focussed on robust anomaly detection in high volume streaming data such as occur in embedded systems, trace logs and automotive sensors....

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