Visible to the public Wavelet transform and unsupervised machine learning to detect insider threat on cloud file-sharing

TitleWavelet transform and unsupervised machine learning to detect insider threat on cloud file-sharing
Publication TypeConference Paper
Year of Publication2017
AuthorsFeng, W., Yan, W., Wu, S., Liu, N.
Conference Name2017 IEEE International Conference on Intelligence and Security Informatics (ISI)
ISBN Number978-1-5090-6727-5
Keywordscloud computing, cloud file-sharing, cloud file-sharing services, Collaboration, Company Data, company IP, discrete wavelet transform, discrete wavelet transforms, DWT, graph theory, graph-based unsupervised learning, graph-based unsupervised machine learning methods, Haar transforms, Haar wavelet, Haar wavelet function, Human Behavior, human factors, Insider Threat Detection, insider threats, Learning systems, local-outlier factor, LOF, machine learning algorithms, Metrics, OddBall, pagerank, Peer-to-peer computing, policy-based governance, pubcrawl, relationship graphs, Resiliency, security of data, Time series analysis, two-stage machine learning system, unsupervised learning, wavelet analysis, wavelet coefficients

As increasingly more enterprises are deploying cloud file-sharing services, this adds a new channel for potential insider threats to company data and IPs. In this paper, we introduce a two-stage machine learning system to detect anomalies. In the first stage, we project the access logs of cloud file-sharing services onto relationship graphs and use three complementary graph-based unsupervised learning methods: OddBall, PageRank and Local Outlier Factor (LOF) to generate outlier indicators. In the second stage, we ensemble the outlier indicators and introduce the discrete wavelet transform (DWT) method, and propose a procedure to use wavelet coefficients with the Haar wavelet function to identify outliers for insider threat. The proposed system has been deployed in a real business environment, and demonstrated effectiveness by selected case studies.

Citation Keyfeng_wavelet_2017