Researchers are suggesting Social Network Analysis as a means of studying the Dark Web which will help us develop policies and methods to curb its menaces.
Recently, there has been much talk about the somewhat mythical concept of the ‘Dark Web’. True to the nature of exotic domains, the realm of the dark web is a mixture of facts and fiction.
In the broad sense, the Dark Web may be regarded as the underground of the virtual world. Just like in the real world, this virtual locale is filled with shady users, who often resort to illicit activities.
To use an analogy, the Dark Web may be compared to the London tunnels and the activities of the participants as the life therein. The bustling city above, more often than not, is absolutely unaware of the goings on in this secret world. Similarly, the common internet users’ community at large is oblivious to the functioning of the dark web actors.
In itself, the Dark Web is a virtual world of its own, complete with its own norms, rules and ways of functioning. Owing to their secret nature, they also have codes and messages which they use to screen outsider entry and to ensure their security.
As is obvious, the Dark Web cannot be accessed by Google or any other search engine. Some of the websites herein, are located simply on an I.P. address and aren’t assigned any names.
The secret world of the Dark Web is used by many actors ranging from petty, wannabe hackers to matured social criminals who pose significant threats to national security. Apart from cyber-crimes like financial frauds and website hacking, the Dark Web is the place for communication and execution by terrorist organisations.
In order to understand the movements and activities of the key-players who pose major threats in many different fields, it’s imperative to undertake proper Dark Web analysis.
In doing so, not only the top bosses but also their potential targets may be identified. Moreover, this will allow us to formidably gauge the threats that they pose. The knowledge gained from such analysis will be significantly beneficial in developing effective techniques to counter cyber-crimes and terrorism.
A shared interest in a common topic or cause is the adhesive force which brings the actors of the Dark Web together. The communities thus formed are sometimes referred to as Virtual Communities of Interest or VCoI.
Observations made by researchers in this field have revealed that the members of any VCoI often interact with each other on social networks. The online platforms used for the interactions include blogs, forums and so on.
Consequently, an understanding of their common interests is a necessary factor in the study of a VCoI. As a matter of fact, it is easier to monitor the activities of the potential actors on the social networks than on the dedicated Dark Web platforms.
Owing to this ease of access, researchers have resorted to the method of Social Network Analysis (SNA), to analyse the working of the Dark Web. To put it generally, this method involves a mapping of the various interactions undertaken by an individual or a group within the online social networks.
Scholars Xu & Chen have pointed out in The Topology of Dark Networks(2008), that the networks of the Dark Web are in perpetual internal, as well as, external interactions. Similar to other networks on the internet, a dark network is also characterised by their close-knit structures formed by data transmissions in short paths. Moreover, owing to the fact that common interest is a binding factor, their structures are somewhat clustered.
Apart from the VCoI, there may also be a Virtual Community of Practice (VCoP), bound by common practices. Social Network Analysis, helps the Dark Web researchers to study these interactions within and beyond the VCoI and VCoP. The data may then be mapped graphically and, consequently, their patterns may be studied.
The data used in SNA may be collected both manually and by making use of automated tools, specifically designed for the purpose of social network data mining.
Since the primary motive of SNA is to exclude the key actors of the Dark Web, researchers have focused on the development of algorithms for this purpose. First, the core community is to be represented based on an overall analysis of the social network surrounding a particular topic.
Second, the key member is identified on the basis of certain factors. Mostly, key-members are those members who are responsible for the production of contents which are then consumed by the other members. In fact, these members are often the ones who are the most relevant to the community’s interest.
Owing to the need for credibility, the primary members have often been found to use the same persona for different social network interactions. In order to enhance the scope of the analysis, researchers may even develop a social network, specifically for this purpose.
In order to attract the targeted players from the Dark Web, these networks are modelled around specific topics and adhere to a particular networking structure. This structure helps in identifying and excluding the key members.
The interaction data returned by the mining techniques are then represented in the form of weighted graphical distributions. The vertices and the edges of these said graphs are used to represent the potential key members and their activities are analysed through weighted modes of statistical analysis.
However, a simple graphical representation is not enough for this purpose. With regard to simple diagrammatic representations of these data, researchers have often faced pertinent issues.
Simple visual representation of such complex data has resulted in chaotic structures of the intertwined web of interactions. To deal with this issue, researchers have applied the Louvain Method to exclude the communities.
Application of Louvain’s modularity to the interaction data has revealed some interesting information with regard to the Dark Web. It has been found that the Dark Web functions at three interconnected levels or tiers.
Important members have been found posting both at the low and the high levels of the Dark Web. According to the analysts, this could be done for the purposes of gaining popularity, as well as, for controlling the behaviours of the lower levels. Usually, these postings are done under the same persona. However, the frequent visitors of the dark markets use a different persona for posting.
As our dependence on the internet increases, so does the vastness of the Dark Web. Owing to the technological developments, the complexities of the Dark Web have also grown manifold. Consequently, the seriousness of the threats posed by the actors of these dark communities has also experienced, so to say, a paradigm shift.
In order to analyse the Dark Web, researchers have come up with a variety of methods. Out of these, Social Network Analysis is one of the most effective. It has been found that the actors of the Dark Web interact with each other on various topic-based social networks and web platforms, such as blogs and the likes. The method of SNA collects the data from these online platforms and maps the interactions of the potential dark community members. Such a mapping allows the researcher to graphical and pictorially analyse the interaction data and identify the communities of the Dark Web. Although the primary motive of the SNA method is to identify and exclude the key members of the dark communities, it has revealed some other interesting information, as of now.
In all, the results found by the preliminary research in this field have pointed out that the Social Network Analysis method has the potential to provide a holistic understanding of the functioning of the Dark Web. If properly employed, this method can solve many mysteries of the Dark Web in the days to come.
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