Social Network Analysis

The multitarget recursive Bayes nonlinear filter is the theoretically optimal approach to multisensor-multitarget detection, tracking, and identification. For applications in which this filter is appropriate, it is likely to be tractable for only a small number of targets. In earlier papers we derived closed-form equations for an approximation of this filter based on propagation of a first-order multitarget moment called the probability hypothesis density (PHD). In a recent paper, Erdinc, Willett, and Bar-Shalom argued for the need for a PHD-type filter which remains first-order in the states of individual targets, but which is higher-order in target number. In this paper we show that this is indeed possible. We derive a closed-form cardinalized PHD (CPHD) filter, which propagates not only the PHD but also the entire probability distribution on target number.

PHD filters of higher order in target number

The multitarget recursive Bayes nonlinear filter is the theoretically optimal approach to multisensor-multitarget 
detection, tracking, and identification. For applications in which this filter is appropriate, it is likely to be tractable 
for only a small number of targets. In earlier papers we derived closed-form equations for an approximation of 
this filter based on propagation of a first-order multitarget moment called the probability hypothesis density 
(PHD). In a recent paper, Erdinc, Willett, and Bar-Shalom argued for the need for a PHD-type filter which 
remains first-order in the states of individual targets, but which is higher-order in target number. In this paper 
we show that this and much more is possible. We derive a closed-form cardinalized PHD (CPHD), filter, which 
propagates not only the PHD but also the entire probability distribution on target number.

A theory of PHD filters of higher order in target number

Cyber situational awareness – A systematic review of the literature

Games and Decisions. By R. Duncan Luce and Howard Raiffa. Pp. xi, 509. 70s. 1957. (J Wiley & Sons)

Lone wolf terrorists pose a large threat to modern society. The current ability to identify and stop these kinds of terrorists before they commit a terror act is limited since they are hard to detect using traditional methods. However, these individuals often make use of Internet to spread their beliefs and opinions, and to obtain information and knowledge to plan an attack. Therefore there is a good possibility that they leave digital traces in the form of weak signals that can be gathered, fused, and analyzed. In this article we present an analysis method that can be used to analyze extremist forums to detect digital traces of possible lone wolf terrorists. This method is conceptually demonstrated using the FOI Impactorium fusion platform. We also present a number of different technologies which can be used to harvest and analyze pieces of information from Internet that may serve as weak digital traces that can be fused using the suggested analysis method in order to discover possible lone wolf terrorists.

/pdf/harvesting-and-analysis-of-weak-signals-for-detecting-lone-22pho5p37y.pdf

Harvesting and analysis of weak signals for detecting lone wolf terrorists

Describing social positions and roles is an important topic within social network analysis. One approach is to compute a suitable equivalence relation on the nodes of the target network. One relation that is often used for this purpose is regular equivalence, or bisimulation, as it is known within the field of computer science. In this paper we consider a relation from computer science called simulation relation. Simulation creates a partial order on the set of actors in a network and we can use this order to identify actors that have characteristic properties. The simulation relation can also be used to compute simulation equivalence which is a less restrictive equivalence relation than regular equivalence but is still computable in polynomial time. This paper primarily considers weighted directed networks and we present definitions of both weighted simulation equivalence and weighted regular equivalence. Weighted networks can be used to model a number of network domains, including information flow, trust propagation, and communication channels. Many of these domains have applications within homeland security and in the military, where one wants to survey and elicit key roles within an organization. Identifying social positions can be difficult when the target organization lacks a formal structure or is partially hidden.

Detecting Social Positions Using Simulation

https://www.csc.kth.se/~hedvig/publications/if_07.pdf

An information fusion demonstrator for tactical intelligence processing in network-based defense

Lone wolf terrorists pose a large threat to modern society. The current ability to identify and stop these kind of terrorists before they commit a terror act is limited since they are very hard to detect using traditional methods. However, these individuals often make use of Internet to spread their beliefs and opinions, and to obtain information and knowledge to plan an attack. Therefore, there is a good possibility that they leave digital traces in the form of weak signals that can be gathered, fused, and analyzed. In this work we present an analysis method that can be used to analyze extremist forums to profile possible lone wolf terrorists. This method is conceptually demonstrated using the FOI Impactorium fusion platform. We also present a number of different technologies that can be used to harvest and analyze information from Internet, serving as weak digital traces that can be fused using the suggested analysis method, in order to discover possible lone wolf terrorists.

http://www.csis.pace.edu/~ctappert/dps/2012EISIC/data/4782a197.pdf

Analysis of Weak Signals for Detecting Lone Wolf Terrorists

The paper discusses a recently developed demonstrator system where new ideas in tactical information fusion may be tested and demonstrated. The main services of the demonstrator are discussed, and essential experience from the use and development of the system is shared.

Christian Mårtenson

Papers

Harvesting and analysis of weak signals for detecting lone wolf terrorists

Detecting Social Positions Using Simulation

An information fusion demonstrator for tactical intelligence processing in network-based defense

Analysis of Weak Signals for Detecting Lone Wolf Terrorists

The IFD03 Information Fusion Demonstrator