The investigation of the terrorist attack is a time-critical task. The investigators have a limited time window to diagnose the organizational background of the terrorists, to run down and arrest the wire-pullers, and to take an action to prevent or eradicate the terrorist attack. The intuitive interface to visualize the intelligence data set stimulates the investigators’ experience and knowledge, and aids them in decision-making for an immediately effective action. This paper presents a computational method to analyze the intelligence data set on the collective actions of the perpetrators of the attack, and to visualize it into the form of a social network diagram which predicts the positions where the wire-pullers conceals themselves.
Social network analysis @cite_17 is a study of social structures, which are made of nodes (individuals, organizations etc.), which are linked by one or more specific types of relationship (transmission of influence, presence of trust etc.). Terrorist or criminal organizations have been studied empirically @cite_19 . Factor analysis is applied to study email exchange in Enron, which ended in bankruptcy due to the institutionalized accounting fraud @cite_20 . The criminal organizations tend to be strings of inter-linked small groups that lack a central leader, but to coordinate their activities along logistic trails and through bonds of friends. Hypothesis can be built by paying attention to remarkable white spots and hard-to-fill positions in a network @cite_12 . The conspirators in the 9 11 terrorist organization are relevant in reducing the distance between hijackers, and enhancing communication efficiently @cite_21 . The 9 11 terrorists' social network is investigated from the viewpoint of efficiency and security trade-off @cite_3 . More security-oriented structure arises from longer time-to-task of the terrorists' objectives. The conspirators improve communication efficiency, preserving hijackers' small visibility and exposure.
@cite_21 @cite_3 @cite_19 @cite_20 @cite_12 @cite_17
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A consistent trade-off facing participants in any criminal network is that between organizing for efficiency or security—participants collectively pursue an objective while keeping the action leading to that goal concealed. Which side of the trade-off is prioritized depends on the objective that is pursued by the criminal group. The distinction is most salient when comparing terrorist with criminal enterprise networks. Terrorist networks are ideologically driven, while criminal enterprises pursue monetary ends. Time-to-task is shorter in the criminal enterprise and group efficiency is therefore prioritized over group security. Terrorist networks, in contrast, have longer horizons and security is prioritized over the execution of any single attack. Using Krebs’ exploratory research on networks of terrorist cells and electronic surveillance transcripts of a drug importation network, our analyses demonstrate how these opposing trade-offs emerge in criminal group structures. The Internet and social media have fuelled enormous interest in social network analysis. New tools continue to be developed and used to analyse our personal connections, with particular emphasis on detecting communities or identifying key individuals in a social network. This raises privacy concerns that are likely to exacerbate in the future. With this in mind, we ask the question ‘Can individuals or groups actively manage their connections to evade social network analysis tools?’ By addressing this question, the general public may better protect their privacy, oppressed activist groups may better conceal their existence and security agencies may better understand how terrorists escape detection. We first study how an individual can evade ‘node centrality’ analysis while minimizing the negative impact that this may have on his or her influence. We prove that an optimal solution to this problem is difficult to compute. Despite this hardness, we demonstrate how even a simple heuristic, whereby attention is restricted to the individual’s immediate neighbourhood, can be surprisingly effective in practice; for example, it could easily disguise Mohamed Atta’s leading position within the World Trade Center terrorist network. We also study how a community can increase the likelihood of being overlooked by community-detection algorithms. We propose a measure of concealment—expressing how well a community is hidden—and use it to demonstrate the effectiveness of a simple heuristic, whereby members of the community either ‘unfriend’ certain other members or ‘befriend’ some non-members in a coordinated effort to camouflage their community. This paper looks at the difficulty in mapping covert networks. Analyzing networks after an event is fairly easy for prosecution purposes. Mapping covert networks to prevent criminal activity is much more difficult. We examine the network surrounding the tragic events of September 11th, 2001. Through public data we are able to map a portion of the network centered around the 19 dead hijackers. This map gives us some insight into the terrorist organization, yet it is incomplete. Suggestions for further work and research are offered. Since the incident about 9.11, the Security Sectors of many countries have put great attentions on gathering and mining of crime data and establishing anti-terrorist databases. With the emergence of anti-terrorist application, data mining for anti-terrorist has attracted great attention from both researchers and officers as well as in China. The purpose of analyzing and mining related terrorist or crimes data is that analyzing of psychology, behavior and related laws about crime, and providing hidden clues to prevent a criminal case, and forecasting terror happening to keeping with crime limits.