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
341170067 2492695942 1970857195 2161260361
Abstract : The purpose of this paper is to explore the question of how modern terrorist groups manage to survive in the face of aggressive counterterrorist operations by security forces. Al Qa'ida survives to this day, despite the destruction of their Afghanistan sanctuary, the loss of countless key personnel, and continuous pressure by the United States and their allies. Why has al Qa'ida survived? Since much of the literature on terrorism focuses on how to eliminate them, this research paper focuses on why they still endure. In other words, instead of asking, "How do we kill them," this research asks, "Why don't they die?" This research employs a dynamic network analysis approach to explore the primary research question of terrorist survival. This analysis combines aspects of traditional social network analysis with a new multi-agent model that describes how terrorist groups raise agents through the organization to positions of prominence. The key to this process is the radicalization of members based on time, connectivity, and belief intensity. The testing dataset comes from the 1998 Tanzania Embassy bombing, expressed in the form of a meta-network. After four testing program iterations, the author concludes that terrorist organizational survival is based on the internal dynamics of leader selection and growth within the group as new members advance. These findings imply a number of recommendations for counterterrorist operations and intelligence activities in order to disrupt the growth and development of new leaders. Additionally, these results imply that current Joint and Army doctrine on network analysis insufficiently addresses the dynamic processes that network diagrams are intended to depict. American military counterinsurgency and counterterrorist operations can be greatly enhanced by moving from a network analysis approach based on structure to one based on dynamics. 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 addresses a method to analyse the covert social network foundation hidden behind the terrorism disaster. It is to solve a node discovery problem, which means to discover a node, which functions relevantly in a social network, but escaped from monitoring on the presence and mutual relationship of nodes. The method aims at integrating the expert investigator's prior understanding, insight on the terrorists' social network nature derived from the complex graph theory and computational data processing. The social network responsible for the 9 11 attack in 2001 is used to execute simulation experiment to evaluate the performance of the method. 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.