February 2, 2015detection sna
Social Network Analysis (SNA) is getting more and more widespread now that online social networks like LinkedIn and Facebook are getting more popular12. This kind of analysis has traditionally been a manual task where investigators are connecting individuals and events more or less by hand. However, the wast amount of data available calls for tools that automate detection, recognizion and visualization of these connections on a large scale. Maltego 3.0 is such a tool. In this paper we will review the different aspects and applications of SNA and seek to discover how Maltego may help automating the information gathering and structuring. Before we can review how Maltego can help us with SNA, we need to understand what problems it aims to solve. The rest of the article is therefore structured as follows. In the next section, related work to this article will be reviewed. Section II will review some of the foundations for SNA as well as how such analysis is related to digital forensics. Section III will provide an introduction to what Maltego 3.0 is and why it exists. Section IV will look at the different approaches to SNA and how Maltego can be used in this regard. Finally, conclusions and proposals for further work will be given in section V.
In this work we are using the community edition of Maltego 3.0. This is a free version of the application and has a couple of limitations. The limitation that has affected our work is that we were only allowed to generate 12 vertices per transformation, and thereby making the size of our examples smaller than what we initially intended.
A. Related work
There has only been publish a few scientific articles specifically about Maltego. Danny Bradbury has recently written a couple of articles about SNA where Maltego has been used to gather and structure the data1112. Though the limited efforts on research concerning Maltego, there seems to be solid scientific research about SNA in general. The book by Wasserman and Faust from 1994 on Social Network Analysis is probably the most cited publication on the topic to date1. Much of the different techniques reviewed throughout this paper is based on their work. The work of Chen 9, Xu6 and Fard and Ester1 has all provided good foundations for the applications and limitations of SNA in the investigation of criminal groups.
A. Digital evidence
The term digital evidence has by Carrier and Spafford been defined as any digital data that contain reliable information that supports or refutes a hypothesis about the incident. 7They define electronic evidence as probative information stored or transmitted in digital form. The notion also includes some key principal elements which boils down to accuracy, reliability and integrity of the evidence.
How these types of evidence is handled, it is often referred to as the chain of custody. A quite important mechanism in this regard is the use of checksums, or digital fingerprints, meaning the integrity of the data. Other important data in this regard are timestamps and the forensics experts own documentation.
Evidence integrity includes digital fingerprints and Order of Volatility (OoV). When considering integrity preservation is the most important aspect, or intentionally not preserve part of, the evidence from the form it had before it was aquired. The notion forensic soundness is often referred to, meaning no alteration of source data, every bit is copied and no data is added to images. When it comes to collecting data in open sources, this is a challenge, since it is difficult to prove what form the data had when the object or subject in question interacted with the data, and who interacted last. The latter brings several other elements to play as well:
- Can we trust the host where the data is collected from?
- Does the information correlate to other data?
The principals in this section is central to the way Maltego can be used for collecting digital evidence for use in court cases.
Tools that collect and process open source data are commonly called crawlers. There are several ways of constructing such crawlers for efficiency and accuracy, e.g. the one found in Fard and Ester1.
Even though external scripts does not directly affect the evaluation of Maltego, most of the foundation of how a open source framework would be implemented is based on crawlers (a set of scripts crawling the web and generating the graph objects used by Maltego). This has a lot of impact when considering the integrity and reliability, accuracy and integrity of Maltego itself as argued in the previous section.
B. Graph Theory
Graph theory has shown to be an effective way of abstraction in large datasets. Problems, such as the travelling salesman problem (TSP) NP-hard problem, may be presented in means of graph theory.
Graphs, which was first documented in 1736, is mathematical way of representing sets of objects. These objects, named vertices, are connected by symmetric or asymmetric edges. The order of a vertex, are decided by the number of connected edges (e.g., a order of two means that there are two edges connected to the vertex). While graphs is the fundamental concept in networks, there are several other important notions as well.
Random Graphs are generated by random processes. In addition to graphs as mentioned above, a portion of probabilty theory is applied as well. Random graphs are typically seen in nature and in inpredictable human behaviour.
In later years graph theory has gained more attention in regard to modelling human social behaviour, both online in social networks such as Facebook and LinkedIn as well as in physical interaction1.
Graphs are essential to the way social networks are presented graphically in Maltego.
1) Multigraphs: When more than one edge are connected between two vertices it is defined as a multigraph. In social networks there may be several reasons for subjects having redundant connections. One reason is mentioned in3 is in social networks. In an online social network such as facebook, users are connected and their connections are often shown in a friend list. This means that if the user himself is not vulnerable to enumeration through e.g. an open source search his connections may be. In definition there are several types of connections as shown in figure 2.
The four multigraph types are complementary to each other and show how subjects is connected. In other words: A criminal case with n number of subjects may be connected either through friendship, groups or events, e.g. if there was a cyber attack. The multigraph in figure 2 shows a practical example of how subjects are connected in such a fictional event.
Multigraph patterns such as the ones in figure 1 will almost certainly always be seen in Maltego generated graphs.
C. Network Analysis
Xu and Chen6 has defined three generations of network analysis:
First generation network analysis represents an analysis where an investigator gathers data about criminals in a matrix, ending at the drawing of a link chart. While first generation tools requires a totally manual approach second generation network analysis tools visualizes the link chart automatically. As will be formalized later on Maltego uses graph theory as a foundation for these visualizations.
Second generation network analysis has probably added a lot of value in terms effective investigations. According to Klerks13 the Analysts Notebook, which is one link analysis application, was widely accepted in dutch law enforcement as early as 1999.
Back in 2005 when Xu and Chen published their report, they stated that there were no existing third generation tool. Even though the techniques for generating visualizations has been enhanced and so has the graphical interfaces, there are no significant changes in the way the data is presented to the investigator. If we split network analysis into the three phases: 1) Generation, 2) pattern detection and recognition and 3) visualization, there are no known automated methods of recognizing and detecting patterns. Thus the job of detecting and recognizing patterns remain manual.
In future editions of tools, such as Maltego, automated SNA will help investigators solve and prove crimes, it probably already does. Automated pattern analysis will probably give the ability to detect the characteristics of criminal networks, again highlighting important structural elements of a given graph and connections.
There are differences in terms of social network analysis in open sources and traditional network analysis. While examples often refer to telephony logs, incident reports, bank transactions and so on (typically who called who), social networks in open sources often refer to the highly complex social structures such as LinkedIn and Facebook.
D. Social Networks
Social networks, which stem from sociology was first documented in the late 1800s. Sociology is the study of society, describing how people are related to each other. Individuals are typically tied through persistent connections or larger social groups. Typically such a group is connected through common properties.
Social groups are typically social network structures are grouped into chained, star or complete topologies as shown in figure 1. In criminal investigations such social networks has gained more awareness in later years due to typically faced problems such as finding the ties between persons of interest. Fard and Ester studied such a problem1, and how to identify suspects based on the suspect ties, and concluded that it can be automated through a P2P application and an unified medium.
INTRODUCTION TO MALTEGO 3.0
When forensics experts collect data from open sources, possibly the foremost task is to document how the data was acquired and to structure it. The latter part is challenging in terms of data quantities. Paterva is a South African company behind the open source intelligence and forensics application Maltego4. By providing a Graphical User Interface (GUI) for displaying data in several ways, such as with clustering by object attributions and the centrality view which will be handled later on. In short Maltego help the forensics expert to structure data. Since Maltego is more of a framework with GUI capabilities, advanced usage is based on plugins, either own ones written in some programming language (e.g. Java or Python). Additionally Maltego comes preloaded with some web-based plugins that uses Patervas servers. In Maltego a plugin is named a transform.
1) Integrity: Maltego does as mentioned consist of a GUI and an input interface. The input interface is quite ”dumb” accepting eXtensible Markup Language (XML) objects.
Thus, Maltego itself must be said to be juridically solid based on its simple architecture The operation against the
transform may be introduce a factor of uncertainty though. There are questions like who created the transform, what are their intentions and how is it implemented. In some commercial cases, such as the popular SocialNet plugin5 the transforms are not open source even though Maltego is.
To avoid untrusted transforms, it is easy enough to create custom ones. The script used for creating figure 6 for instance, is a LinkedIn scraper and parser producing objects like education, location and so on. This works by e.g. creating a Python transform which outputs desired XML which Maltego converts to objects, but there are downsides to this approach as well. Even though commercial transform pools such as SocialNet are not open source, they are well tested. Who are to say that custom transforms does not contain errors? Additionally creating custom transforms means that the programmer will have to maintain it himself instead of doing investigations.
There are upsides and downsides to both using commercially available and custom transforms. The path chosen should be carefully considered.
2) Maltego alternatives: Before Paterva was founded in 2007 other scientists, such as Xu and Xen6 developed a proposition for a open spurce collection utility named CrimeNet Explorer (CE). CE, built on the principals of hierarchical clustering, social network analysis and Multidimensional Scaling (MDS). The previous, as one might recall, resemble Maltego quite much. Results from controlled experiments in the CE paper was subjects with high precision and recall.
The Analysts Notebook (AN) which is led by i2, a part of IBM, is also an alternative to Maltego based on Social Network Analysis. The main concepts of operation that is implemented in the AN8:
- In highly centralized networks SNA is used for finding the subject which dominates network
- Betweeness is a measure for how many paths are running through the entities
- Link betweeness is how many links runs through one path
- Closeness measures proximity of an entity to other entities in the network
- Degree. How many links (or how many edges are connected to a vertice) are connected to a subject Eigenvector. In addition to have many connected subjects, an entity have weighted links representing influence. These are combined in the eigenvector
- Link direction says how information flows in the network
- Link weightings is related to how well-connected the subject is
In this section we have taken a look at two alternatives to Maltego. As it shows, many of the same techniques are used in both CE and AN, especially when considering graph theory. When it comes to accuracy, reliability and integrity there is a difference between Maltego and the others. It is in that regard important to realize that where Maltego relies on automated input, while the CE and AN rely on maual input. Thus, the accuracy, reliability and integrity relies on the scripts automating the process and the subjects inputting data. In regard to the visualization both Maltego, CE and AN seems to rely on the same principals: Graph theory.
MALTEGO 3.0 IN ACTION
Now that an introduction to the foundation of SNA has been given, it is time to see how this can by utilized to extract as much information as possible from the networks. We will in this chapter review the following techniques for structuring the gathered data: (1) The centrality principle, (2) Clustering, and (2) Object attribution. In addition to these introduction, we will for each review how Maltego can be used in order to ease the task of such structuring and visualization. This will be done by presenting a number of Proof Of Concept (POC)s in the form of example figures. The data used in these POCs are not gathered from public sources, but is of practical and ethical reasons generated in an ad-hoc manner.
When displaying a graph i Maltego, a view type has to be chosen. A view type can be considered a set of rules for how the verices are organized and displayed. Maltego has four built in view types, these are: (1) Mine View (MV), (2) Dynamic View (DV), (3) Edge Weighted View (EWV), and finally, (4) Node List View (NLV). MV displays the nodes in a hierarchicalmanner, where the vertices with only outgoing edges are on the top and those with only ingoing edges are on the bottom (see figure 13). Both DV and EWV are organizing the vertices such that the ones with the highest number of outgoing edges are placed at the center of the graph. One difference between DV (e.g., figure 12) and EWV (e.g., figure 11) is that the vertices in EWV are given a size based on their number of outgoing and ingoing edges. This may make it easier to detect and evaluate a vertex’s importance by looking at its size compared to the other vertices.
NLV is a list containing all the vertices in the graph, as well as the most important information such as type and value, for each vertex.
A. The Centrality Principle
When doing social network analysis, we need a metric in order to measure the importance of each vertex. Chen et al. has in their work measured the centrality of a vertex to determine its importance. This is referred to as the centrality principle9. They calculate three different types of centrality: degree, betweenness and closeness. The following definitions are based on the work of the i2 groups work on the Analysts notebook8. The degree centrality of a vertex is a measure of how many vertexes it is directly connected with. The information value of a high degree centrality is highly dependent on the rest of the network structure. E.g., if the other notes also are directly, or indirectly connected with each other, the value of a large degree centrality is less than they are only connected together by the central vertex. The betweenness centrality measures how a single vertex connect different cliques. A clique is a subset of vertexes in a multigraph that is only connected with each other, If a vertex is the only vertex able to distribute communication between two or more vertexes, it corresponds to a single point of failure in the network. If the vertex is removed from the network, the communication between the cliques will halt. The closeness centrality of a vertex relates, as the name implies, to the distance to the other vertexes. This distance can be considered a combination of geographic distance and distance in terms of the number of vertexes between the two vertexes.
Using these three centrality measurements, different roles in a network can be identified. It may be possible to identify individuals which is crucial for the network to function. However, it is important to recognize that the findings may be incomplete or inaccurate due to the fact that leaders may keep a low profile in these networks9.
In a court case there are often large quantities of data. Imagine that the data have been gathered by a custom web-spider, enumerating a subjects connections over several online social networks. Let the number of total connections be 1000.
The process of cluster analysis is assigning each object in a set to a group. If the objects are visualized the cluster density and distance from one object to others symbolize the likeness of the objects.
The different graph view-types in Maltego visualize clustering differently, and the dynamic and edge weighted view are the most suited for cluster analysis. Figure 7 shows an example of the eded weighted view.
C. Object Attribution
Social networks such as Facebook, LinkedIn and Twitter carries a lots of metadata attributed to specific users. Metadata such as age, gender, workplaces, education and so on is formally named attributes to an object from this point on. Some interesting common attributes was shown in 2[p.23] . The simple analysis of the three social networks showed that typical common attributes are surname, lastname and a profile picture.
Briefly explained the goal of attribution in Maltego is to associate different types of data types to a common root cause based on a combination of available evidence. The root cause is best presented as individuals, groups or communities associated with e.g., a crime.
What has been seen of Maltego so far leads to an interesting problem: May data be displayed in misleading ways, e.g., in court?
A common problem that arise when working with graphs and data being fundamentally different, or being of different types, is how to combine them without still finding relevant or not erronously creating connections. This is quite important since the reason for using Maltego is to find these connections and structure large quantities of data. A more advanced example can be taken from attack attribution where there are many sources of information to be taken into consideration for revealing the relevant patterns.
A solution to combining different datasets has been proposed by O. Thonnard10 . The solution was based on clustering and further data aggregation based on Multi-criteria Decision Analysis (MCDA). The results was data which was possible to analyse from multiple viewpoints making it possible to find patterns of interest. The thesis also shows how to combine the viewpoints, so-called data fusion. To make the applicability clearer a set of profile vectors from Facebook (enumerated user vectors created from e.g., a user and enumerated by the centrality principle) may be combined with the data log of a criminal profiling system. These two systems generates different types of data which may be converted to vectors, again being processed by MCDA and clustering.
In Maltego, attribution is implemented by using both pre-defined and dynamic attributes. When a transformation are generating its entities it may add as many additional value types as needed. Different entities of the same type contain different sets of values. The predefined properties has the advantage over the dynamic ones that they support arrays of values. This may come in handy, for example, if there are multiple direct connections between to entities. While doing analysis on entities, these attributes may provide additional clues to where to look for more information.
There is a strong belief that object attribution also has a large commercial potential when it comes to tailoring services for customers needs. The online service RapLeaf6 claims to be able to identify customer attributes such as age, relationship status, hobbies, etc. This is done by gathering information from open sources.
D. Fusing network analysis and object attribution
When preparing for SNA with Maltego, the investigators has to determine what should be defined as entities, and what may be left as attributes. The entities that are being analyzed in Maltego may have a wast amount of attributes. These attributes may be divided into three groups. (1) Some of these attributes are unique to the entity, e.g., email address and phone number. (2) Other attributes are shared among indirectly connected entities, such as the participation in an event and a location on a given time. (3) The third type of attributes are the ones that may be shared by several entities, but having equal values of these attributes doesn’t infer any indirect connection between the entities.
The analyst should before starting the analysis determine which attributes to set as entities, and which attributes to set as additional attributes on other entities. Even though it is possible to run transforms from an entity with several attributes, connecting to other entities through the additional attributes should be considered a bad idea. Imagine having a Person entity with the primary value Name the additional attributes EmailAddress and Phonenumber. Lets call this person A. When running a transformation a transformation, a connection to Person B is made. This connection indicates that A has been in contact with B, but as figure 13 reveals, we are not able to tell wether the contact was made by email or by phone.
By letting both EmailAddress and Phonenumber be their own individual entities, as seen in figure 14, we are able to tell if the contact between A and B was through email, phone, or both. As well as giving an example on how additional entities may provide more information, figure 13 and figure 14 also demonstrates some of the different views that are available in Maltego.
DISCUSSION AND CONCLUSIONS
In this article we have presented both the mathematical an historical background of Social Network Analysis. After describing what SNA is and why we need tools in order to do i efficiently, we reviewed the commercial tool Maltego 3.0. In regard to using Maltego 3.0 for gathering data that may be used as evidence in a court of law. We discussed how the reliability and integrity of Maltego largely depends on the tools used to gather and structure the information. While reviewing Maltego we had a special emphasis on what types of information that could be possible extract from the social networks. We have reviewed how these types of information can be organized in the different views in Maltego, and explained some of the differences between these views. We have discussed how Maltego can be used to perform object attribution in order to discover more about each entity, and how this combined with SNA may give extensive information on an entity and how itis connected with its surroundings. Finally, we discussed how defining attributes as a set of entities for another entity may give more accurate information on how entities are connected.
It seems obvious that with the rapidly increasing amount of data publicly available on the internet, the value of SNA will continue to grow. The automation in 2. generation network analysis tools has expanded the limits for how much data that can be analyzed, and therefore utilize more of the available data. However, there seems to be a long road ahead before actual automation of SNA will become a reality. This especially applies to the reliability of indirect connection between entities, i.e., when there are events or objects that connects two or more people. It should also be considered a limitation that todays tools and techniques does not allow analysts to analyze the evolvement of the social network over time. On smaller social networks automated tools such as Maltego 3 may however provide an efficient, reliable and accurate visualization. Automated tools may enable analysts to faster understand the properties of a network and also uncover patterns and connections which the analysis didn’t initially looked for or knew existed.
In order to be able to use the information acquired from Maltego 3 in court, there is a need to be able to prove the information’s reliability and integrity. As discussed earlier in this paper these attributes rely heavily on the tools used for gathering the data. There is a dilemma between using closed source, but widely trusted tools, versus using in-house, known source and less tested tools while gathering data. This will probably remain an issue, but a combination of the two is likely to fit most needs.
A. Further work
As there are some fundamental differences in how data is entered into tools such as Analysts Notebook and tools such as Maltego 3, it should be interesting to look at how this affects the integrity and reliability of the evidence they provide. This could be done by defining how uncertainty in general can be measured in the data gathering tools.
There is also a need for more experimental research on the value of data gathering in open sources. This is specially relevant for the accuracy and correctness of the data that are gathered from sources such as Facebook and LinkedIn.
Finally, it could be interesting to look at how social networks evolve over time, i.e., how events affects the infrastructure in the social networks. This could be done by gathering data over a longer period of time and assign a time to the data that is gathered.
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