U.S. Department of Energy

Pacific Northwest National Laboratory

Dynamic Network Analysis via Motifs (DYNAMO)

We propose to develop a graph mining-based approach and framework that will allow humans to discover and detect important or critical graph patterns in data streams through the analysis of local patterns of interactions and behaviors of actors, entities, and/or features. Conceptually, this equates to identifying small local subgraphs in a massive virtual dynamic network (generated from data streams) that have specific meaning and relevance to the user and that are indicators of a particular activity or event. We refer to these local graph patterns, which are small directed attributed subgraphs, as network motifs. Detecting motifs in streaming data amounts to more than looking for the occurrence of specific entities or features in particular states, but also their relationships, interactions, and collaborative behaviors.


In the detection of insider cyber threats, one may characterize the type of an insider agent, computer user, or organizational role by examining the modes and frequencies of its local interactions or network motifs within the cloud. For example, a cloud interaction graph may be generated from cloud telemetry data that shows a user’s particular interactions with or access to specific tenants, data stores, and applications. In examining such a graph, one can look closely for small local graph patterns or network motifs that are indicative of an insider cyber threat. Additional motif analysis of prox card access data may identify changes in an actor’s work patterns (time and place) and the people she interacts with. Motif analysis of email and phone communications records could identify an actor’s interactions with other people and their associated statuses and roles.

Each of the network motifs is an individual indicator of insider agent behavior. We might expect dozens to hundreds of potential indicators that are drawn from subject-matter-experts or learned from activity monitoring data. From these potential indicators, we may derive a sufficient and optimal set of indicators to apply to insider threat detection. The set of influential network motifs may be organized and monitored through a motif census, which is a scalar vector of the counts of each motif pattern in the cloud interaction graph. The motif census may be considered as a signature of network interaction patterns for a particular person, class of users, or type of insider agent.

For the electron microscopy use case, image features may be detected in an electron microscope image much like facial features are detected from a picture for facial recognition. The image features may be linked together to form a feature map that is analogous to a facial recognition map. Motif analysis may then be applied to the dynamic feature map to identify the occurrence of substructures that are indicative of a particular event or chemical behavior.

The concepts of the network motif and the motif census have some very compelling characteristics for threat, event, or feature detection. They provide a simple and intuitive representation for users to encode critical patterns. They provide a structure for assessing multiple indicators. They enable a mechanism for throttling the accuracy of findings and limiting false positives by adjusting the threshold associated with the computed similarity distance measure (e.g., lower threshold = higher accuracy and less false positives). Indicators in a motif census may be active or inactive depending on whether data is available to assess the indicator. In the case where active indicators may be alerting a potential insider or image feature or event, the inactive indicators identify additional data to gather and assess to further confirm or reject the alert. This enables an approach for directing humans or systems to specific areas to look for additional information or data.


Dynamic network motif analysis represents a graph-theoretic approach to conduct “pattern-of-life” analysis of agents or entities. The analytical approach should allow an analyst or scientist to:

  • Expose relationships and interactions of an agent with other various articles or entities such as other people, computing resources, places or locations, and subject areas
  • Learn normal relationship and interaction patterns of different classes of agents from monitored activities
  • Detect when anomalous patterns occur and offer potential explanations
  • Facilitate encoding of hypothetical behavioral patterns or indicators for an agent to use in future detection (e.g., off-hour access to computing resources, unusual access patterns, accumulation of large amounts of data)
  • Provide framework for evaluating and confirming multiple indicators
  • Analyze temporal evolution of interactions and behaviors of an agent
  • Support all the above analytics in the presence of streaming data


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