Airport front-of-house vulnerabilities and mitigation options

Abstract

A method for risk-informed comparison of mitigation options in situations with large epistemological uncertainties is presented by example: that of an improvised explosive device (IED) attack at a generic airport frontof-house (FoH). Specifically, a probabilistic model is built and distributions of scaled fatalities from probabilistic combinations of vehicle-based IED and personnel-based IED threats are generated using Monte Carlo methods. A risk assessment of the threats is given from the statistics of the scaled fatality distributions. We consider the risk reduction due to a combination of mitigation options, including IED screening, vehicle stand-off, and interception and detection. Sensitivity and stress analysis show that the ranking of the options, by their risk reduction, is relatively robust to the epistemological uncertainty incorporated into the model. The qualified conclusion of the example is that vehicle stand-off is the priority risk-effective mitigation option for IED attack at a generic FoH.

A risk and cost–benefit analysis of police counter-terrorism operations at Australian airports

“…Nearly all windows facing the blast would be destroyed, and little of the structure left standing, causing the entire roof to collapse-causing 306 fatalities or severe injuries. In another study, blast pressure modeling from a 400 kg VBIED detonated in the passenger drop-off area of a generic airport predicted approximately 250 fatalities (Lord, Nunes-Vaz, Filinkov, & Crane, 2010). By way of comparison, these scenarios are similar to the 1995 Oklahoma City bombing that killed 165 people, the US Embassy attack in Kenya in 1998 that killed 213 people, and the 2008 truck bombing of the Islamabad Marriott Hotel that resulted in the deaths of 54 people.…”

“…Threat 2: Weisz (2012) concluded that a smaller 45 kg (100 pound) luggage bomb detonated near a check-in counter would also destroy nearly all windows at Dulles international airport, but would inflict considerably less structural damage overall and approximately 10 percent of the fatalities caused by a large truck bomb-that would be about 30 fatalities or severe injuries valued at $200 million. Lord et al (2010) predicted approximately 100 fatalities from a 36 kg IED detonated in the check-in area of a generic airport. The 2011 suicide bombing at Moscow's Domodedovo airport that killed 37, accomplished with an IED reportedly of 2-5 kg, did cause some flights to be diverted to other airports in Moscow immediately following the attack.…”

Toward an Epidemiology of Safety and Security Risks: An Organizational Vulnerability Assessment in International Airports

“…). This entails a focus on the triggering factors, for instance, the patterns followed by extreme meteorological events around airports (Lopez, ), or the behaviors enacted by improvised explosive devices attackers (Lord, Nunes‐Vaz, Filinkov, & Crane, ), rather than on the underlying organizational factors that contribute to the disruptive events. A notable exception to the prevalent hazard‐dependent approach is represented by Pettersen and Bjørnskau (), who investigate the organizational contradictions between aviation safety and airport security following the introduction, in Europe, of new security regulations.…”

“…The work by Lord et al. () segments the airport in subcomponents and elaborates a probabilistic model to assess the risks associated with an improvised explosive device attack. This investigation considers vulnerability from a hazard‐dependent perspective and revolves around the nature of the object of vulnerability.…”

Understanding and taxonomy of uncertainty in modeling, simulation, and risk profiling for border control automation

“…This paper is motivated by two circumstances: a discussion started by Dale K Pace 1 on uncertainty in M&S, as well as our experience of biometric-enabled machine development 2,3 where uncertainty addresses effects such as mis-identification and impersonation. Understanding uncertainty in these technologies is a topic of critical importance because it addresses the problem of trusted M&S. [4][5][6] We emphasize that (a) all M&S tools, recommenders, and risk profilers are machines; decisions under uncertainty are made by machines; and (b) these machine-generated decisions aim to support human decisions rather than replace them. This means that a designer/user of these machines relies on his/her knowledge and understanding of the uncertainty.…”