Using graphs for intelligence analysis

The identification and monitoring of terrorist or criminal networks are imperatives to detect threats and defeat attacks. Let’s see how Linkurious and graph visualizations can help identify and track potential dangerous individuals and networks.

Challenges for intelligence analysis

Criminal or terrorist activities are rarely the acts of isolated individuals. Behind these activities we find more or less centralized organizations or networks. Intelligence experts are in charge of identifying every actors of such groups, despite their strategies to hide their connections to the networks (encrypted communication services, numerous middlemen, fake identities, etc). Getting the whole picture of the network is essential to monitor suspect activities, prevent attacks or detected potential threats.

Countering such activities is also about gathering as much information as possible, from any possible sources. The more data intelligence and security organisms are able to obtain, the easier it is to track and anticipate criminal or terrorist activities. This means that analysts and investigators have to handle large sets of heterogeneous data.

Graph analysis is particularly suited to this sort of challenge. Graph databases allow organizations to store and query in near real-time the relationships between billions of entities. Let’s see how these systems, combined to tools like Linkurious, can help intelligence analysts identify and investigate threats.

Applying a graph approach to intelligence analysis

We will dive into the investigation of a potential terrorism threat and explore how Linkurious can help identify and investigate suspicious networks.

For this purpose, we have created a dataset with fictitious data about people, including addresses, phone numbers and travel information. This data can easily be modeled as a graph:

Graph data model of our investigation data

Graph data model of our investigation data.

To keep our analysis understandable we chose a very simple model with only a limited volume of data. An authentic situation will definitely involve larger volumes and a wider range of data types.

Data entities, such as individual, email, phone, are modeled as nodes. Relationships between entities are symbolized with edges, labeled with the nature of the connection. The data then forms a network.

In our graph model we have five types of nodes: people, countries, addresses and phone numbers, and as many types of edges, or relationships.
Let’s start our investigation by trying to detect suspicious patterns in our data.

How to use graph patterns to detect potential threats

When dealing with large datasets, we need to find ways to focus the analysts’ attention on relevant information. Here, we want to detect potential terrorist cells. We are going to try to detect groups of at least three people who 1) visited an at-risk country (in our case Syria) and 2) are indirectly in contact (via their addresses or phone communications).

With a simple Cypher script query, Linkurious users can set up a monitoring activity for chosen patterns. Below is the script we will use to identify our pattern:

// Detecting threats:
MATCH (a:Person)-[s:HAS_CONTACTED|HAS_PHONE|HAS_ADDRESS*..10]-(b:Person)-[:HAS_BEEN_TO]->(d:Country {name:’Syria’})
WITH a, collect(s) as rels,collect(distinct b) as suspects,d,count(distinct b) as score
WHERE score > 2
RETURN a,suspects
ORDER BY score DESC

Linkurious reported three individuals: Jessica Wells, Bobby Murphy and Ruth Warren (on the left of the graph). As an analyst, I can visualize them and how they are interconnected. Jessica, Bobby and Ruth display a “has been to” relationship with Syria and appeared to be all connected to a unique phone number: Judy Lewis’ (on the right of the graph).

Visualization of a suspicious network around Jessica, Bobby & Ruth

Visualization of a suspicious network around Jessica, Bobby & Ruth.

Several nodes intermediate between our three people and Judy’s phone number. Phone calls and address are the bridges enabling the connection between our individuals. For analysts, this particular pattern could be pointing toward a recruiting network, with numerous middlemen to avoid detection. Those results could lead to specific recommendations and further investigations.

A graph approach provides the opportunity to detect specific cross-data patterns. With Linkurious, it is easy to visualize and understand both the network and the relationship between its members. Node-edges graph visualizations combine all the available information in a single representation.
Some of the nodes here seem to be connected to other entities. Linkurious allows analysts to interactively explore the data and uncover new information.

Investigate complex network with graph visualization

We identified a potential network with several people. Perhaps they have accomplices? We can try to investigate further, starting from one node of the network. Let’s pick Judy’s phone number for instance and extend the nodes around it.

Investigating Judy’s closest connections via her phone number

Investigating Judy’s closest connections via her phone number.

Judy is connected to a certain Robert Wells, via phone communications, and Robert is himself connected to Theresa Mills’ phone number. If we expand the nodes linked to Theresa’s phone, we get the following visualization.

Visualization of a sub-network around Theresa’s phone number

Visualization of a sub-network around Theresa’s phone number.

The sub-network around Theresa Mills is very specific. The nodes, all linked together, are phone numbers associated to seven individuals. Such pattern -a  small highly connected group with a unique bridge to other potential suspects – represents a sub-network within the larger network we are investigating.

From a single node, we went up to another group, gathering new information about the network. Interactive and scalable tools like Linkurious ease the exploration and analysis for experts.

Visualize and analyse intelligence and security data with Linkurious

Graph approaches are well suited for the investigation of criminal network and terrorist groups. Linkurious offers to intelligence agents a unique entry point to identify hidden insights in complex connected data. Analysts can determine specific pattern to monitor suspicious activities. The visualization interface allows them to navigate between the nodes to identify new key actors through hidden connections.

Discover how you can identify hidden insights in your graph data and try the demo of Linkurious.

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