Modeling threats

Singh, Satnam; Tu, Haiying; Allanach, J.; Areta, Javier; Willett, Peter; Pattipati, Krishna R.

doi:10.1109/mp.2004.1341780

Cited by 21 publications

(22 citation statements)

References 7 publications

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“…Firstly, Singh et al, discuss how infrastructures worldwide are facing a very real threat from terrorist activity [178]. Their research focuses on the prediction of threat events from occurring using a semi-automated tool to perform analysis.…”

Section: Existing Behavioural Researchmentioning

confidence: 99%

Behavioural Observation for Critical Infrastructure Security Support

Hurst

Merabti

Fergus

2013

2013 European Modelling Symposium

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Section: Existing Behavioural Researchmentioning

confidence: 99%

Behavioural Observation for Critical Infrastructure Security Support

Hurst

Merabti

Fergus

2013

2013 European Modelling Symposium

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“…The applicability of HMMs for terrorist activity modeling and other national security problem situations has been illustrated in previous work, see e.g. [3,4,5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

“…A number of different terrorist plan HMMs are proposed in [5,6,7,8], including models for a truck bombing, Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

“…Markov chain network modeling the planning of a truck bombing. Refer to [5] for additional details. Markov chain network modeling the production of weapons grade material.…”

Section: Introductionmentioning

confidence: 99%

“…An empirical HMM can be constructed using available prior information, or with the help from experienced intelligence analysts (SMEs) [5]. For example, the HMM for development of a nuclear weapons program (DNWP) in [7] is gleaned using the open sources [9,10,11,12,13].…”

Section: Introductionmentioning

confidence: 99%

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Detectability prediction of hidden Markov models with cluttered observation sequences

Granström

Willett

Bar‐Shalom

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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There is good reason to model an asymmetric threat (a structured action such as a terrorist attack) as an HMM whose observations are cluttered. Recently a Bernoulli filter was presented that can process cluttered observations ("transactions") and is capable of detecting if there is an HMM present, and if so, estimate the state of the HMM. An important question in this context is: when is the HMM-in-clutter problem feasible? In other words, what system properties allow for a solvable problem? In this paper we show that, given a Gaussian approximation of the pdf of the log-likelihood, approximate detection error bounds can be derived. These error bounds allow a prediction of the detection performance, i.e. a prediction of the probability of detection given an "operating point" of transaction-level false alarm rate and miss probability. Simulations show that our analysis accurately predicts detectability of such threats. Our purpose here is to make statements about what sort of threats can be detected, and what quality of observations are necessary that this be accomplished.

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