FORTIFIER: a FORmal disTrIbuted Framework to Improve the dEtection of thReatening objects in baggage

Abstract

Currently, security breaches in public places like airports and official buildings are a major concern by both governmental and corporative organizations. In these situations, X-ray devices must scan a vast amount of baggage in a short time frame. Hence, deploying scanners that automatize the task of detecting suspicious artefacts becomes of vital importance to prevent from threats. In this paper we present FORTIFIER, a formal distributed framework designed to detect suspicious artefacts. This approach consists in the integration of several image detection algorithms executed in a distributed environment, which are aimed to detect a wide spectrum of weapons like guns, knifes and bombs. The main core of our proposed framework for recognizing suspicious artefacts is divided in different phases, where each one is modelled with a specific finite state machine (FSM). Several FSMs are combined to detect different artefacts. We also present a case of study where some performance experiments are carried out for analysing the scalability of FORTIFIER. Initially, FORTIFIER is deployed in a single cloud environment. Once the main features that have a significant impact on the overall system performance are analysed, our proposed framework is deployed in a multi-cloud environment.

Keywords

• Formal modelling
• image recognition
• cloud systems