Francesca De Cillis

Vulnerability modeling and analysis for critical infrastructure protection applications

Abstract Effective critical infrastructure protection requires methodologies and tools for the automated evaluation of the vulnerabilities of assets and the efficacy of protection systems. This paper presents a modeling language for vulnerability analysis in critical infrastructure protection applications. The language extends the popular Unified Modeling Language (UML) to provide vulnerability and protection modeling functionality. The extended language provides an abstract representation of concepts and activities in the infrastructure protection domain that enables model-to-model transformations for analysis purposes. The application of the language is demonstrated through a use case that models vulnerabilities and physical protection systems in a railway station.

Indoor positioning system using walking pattern classification

In the age of automation the ability to navigate persons and devices in indoor environments has become increasingly important for a rising number of applications. While Global Positioning System can be considered a mature technology for outdoor localization, there is no off-the-shelf solution for indoor tracking. In this contribution, an infrastructure-less Indoor Positioning System based on walking feature detection is presented. The proposed system relies on the differences characterizing different human actions (e.g., walking, ascending or descending stairs, taking the elevator). The motion features are extracted in time domain by exploiting data provided by a 9DoF Inertial Measurement Unit. The positioning algorithm is based on walking distance and heading estimation. Step count and step length are used to determine the walking distance, while the heading is computed by quaternions. An experimental setup has been developed. The collected results show that system guarantee room level accuracy during long trials.

Optimization Models in a Smart Tool for the Railway Infrastructure Protection

In this paper we describe a smart tool, developed for the European project METRIP (MEthodological Tool for Railway Infrastructure Protection) based on optimal covering integer programming models to be used in designing the security system for a Railway Infrastructure. Two models are presented and tested on a railway station scheme. The results highlight the role that the optimization models can fulfill in the design of an effective security system.

Analysis of Criminal and Terrorist Related Episodes in Railway Infrastructure Scenarios

Abstract Prevention and preparedness of risks in railway transportation systems is crucial for homeland security and require, among other things, a proper analysis of the vulnerabilities of the assets, a clear awareness of criticalities, possible countermeasures and adequate methods to design, scale and optimize the protection. To this end, in this paper we present an analysis of various security incidents and terrorist attacks that took place from 1972 to 2011; all involving railway infrastructures. All of the collected information has been stored in a database, aptly named RISTAD (Railway Infrastructure Systems Terrorist Attacks Database). Specifically, this research focuses on railway assets in order to identify those environmental, architectonic and infrastructural aspects that make such targets more “attractive” from a criminal point of view. We analyzed about 540 criminal-related episodes utilizing open source documents in an effort to correlate the acts with the peculiar characteristics of the asset. The last section is specifically focused on stations, attempting to emphasize both interesting and, for some aspects, non-intuitive correlation among collected data (e.g., lethality and number of attacks as a function of the number of tracks, the number of daily passengers, station extension, presence and type of security systems, etc.).

Improving Situational Awareness for First Responders

This paper aims at exploring a novel approach for indoor localisation by exploiting data fusion. Specifically, personnel localisation in rescue scenarios is addressed: the key idea is to increase the situation awareness of rescuers. A pedestrian dead reckoning algorithm based on waist mounted inertial sensors is designed to cope with different human activities. The drifting estimate is re-calibrated by using information gathered from the environment. The outcomes of experimental trials performed in a real scenario are reported.

Fall-detection solution for mobile platforms using accelerometer and gyroscope data.

Falls are a major health risk that diminish the quality of life among elderly people. Apart from falls themselves, most dramatic consequences are usually related with long lying periods that can cause serious side effects. These findings call for pervasive long-term fall detection systems able to automatically detect falls. In this paper, we propose an effective fall detection algorithm for mobile platforms. Using data retrieved from wearable sensors, such as Inertial Measurements Units (IMUs) and/or SmartPhones (SPs), our algorithm is able to detect falls using features extracted from accelerometer and gyroscope. While mostly of the mobile-based solutions for fall management deal only with accelerometer data, in the proposed approach we combine the instantaneous acceleration magnitude vector with changes of the users heading in a Threshold Based Algorithm (TBA). In such a way, we were able to handle falls detection with minimal computational load, increasing the overall system accuracy with respect to traditional fall management methods. Experimental results show the strong detection performance of the proposed solution in discriminating between falls and typical Activities of Daily Living (ADLs) presenting fall-like acceleration patterns.

Optimal location of flow intercepting facilities to improve security in urban areas

The problem of the optimal location of control facilities in an urban area for improving security in case of large events or critical situations is presented. The paper focuses on the prevention of threats by the control and/or the interdiction of people and vehicle flows entering an area (restricted zone). In general, limited available resources do not allow monitoring all the access points of the area. So it is fundamental to identify the most suitable points where control facilities should be located. Two flow intercepting facility location models will be presented: the first one is devoted to identify the best location of a limited number of control facilities to maximise the security level; the second one is aimed at computing the minimum number of control facilities able to guarantee a prescribed level of security. Results obtained on test cases representative of different urban structures are presented.

Vulnerability Assessment in RIS Scenario Through a Synergic Use of the CPTED Methodology and the System Dynamics Approach

The 9/11 attacks dramatically stressed the fragility of our CI against terrorist and criminal actions. For their peculiarities and symbolic value, CI are largely exposed to attacks, as evident by the large number of targeted incidents that occurred. Within this context, the Railway Infrastructure System (RIS) holds a high-ranking position. Vulnerability analysis and quantitative simulation approach play a crucial role in identifying weak-points and outlying new and more appealing protection strategies. In this chapter, a vulnerability assessment mean fulfilled through a synergic use of System Dynamics method, CPTED (Crime Prevention Through Environmental Design) multidisciplinary approach and crime opportunity theories is depicted. The aim consists in analyzing how different factors may influence the railway asset attractiveness, fragility and vulnerability. Starting from the CPTED technique and situational crime prevention theories, we were able to outline which are the main physical, social and environmental aspects that provide opportunity for criminality in railway scenarios. Using the System Dynamics approach, we propose a pattern to model the railway asset scenario, integrating physical aspects and social factors. Results of simulations reproducing different operative conditions are presented and analyzed.

On field gesture-based human-robot interface for emergency responders

Coordination and control of rescue robots is a hard task, especially in harsh and hazardous environments, that potentially limit both the mobility and endurance of the robots and the safety of a human interaction. In this paper we provide a fast, flexible and cost effective framework for human-robot interaction specifically designed for the on-field interaction of human operators and robots, using as input a cheap Microsoft Kinect camera. The proposed architecture is based on a quick and cost effective gesture recognition algorithm developed in LabView and integrated into a ROS based communication framework that allows to decouple the mobile robot and the user terminal. The proposed architecture has been tested simulating harsh conditions by considering darkness, smoke, crowds, and user wearing firefighter uniforms.

Long-term gait pattern assessment using a tri-axial accelerometer

Abstract In this article, we present a pervasive solution for gait pattern classification that uses accelerometer data retrieved from a waist-mounted inertial sensor. The proposed algorithm has been conceived to operate continuously for long-term applications. With respect to traditional approaches that use a large number of features and sophisticated classifiers, our solution is able to assess four different gait patterns (standing, level walking, stair ascending and descending) by using three features and a decision tree. We assess the algorithm detection performances using data that we retrieved from a validation group composed by nine young and healthy volunteers, for a total number of 36 tests and 12.5 h of recorded acceleration data. Experimental results show that in continuous applications the proposed algorithm is able to effectively discriminate between standing (100%), level walking (∼99%), stair ascending (∼84%), and descending (∼85%), with an average classification accuracy for the four patterns that exceeds 92% in continuous, long-lasting applications.