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With cyber incidents and data breaches becoming increasingly common, beingable to predict a cyberattack has never been more crucial. The ability ofNetwork Anomaly Detection Systems (NADS) to identify unusual behavior makesthem useful in predicting such attacks. However, NADS often suffer from highfalse positive rates. In this paper, we introduce a novel framework calledHoneyboost that enhances the performance of honeypot aided NADS. Using datafrom the LAN Security Monitoring Project, Honeyboost identifies most anomalousnodes before they access the honeypot aiding early detection and prediction.Furthermore, using extreme value theory, we achieve the highly desirable lowfalse positive rates.  Honeyboost is an unsupervised method comprising two approaches: horizontaland vertical. The horizontal approach constructs a time series from thecommunications of each node, with node-level features encapsulating theirbehavior over time. The vertical approach finds anomalies in each protocolspace. Using a window-based model, which is typically used in online scenarios,the horizontal and vertical approaches are combined to identify anomalies andgain useful insights. Experimental results indicate the efficacy of ourframework in identifying suspicious activities of nodes.

Honeyboost: Boosting honeypot performance with data fusion and anomaly detection