Current physical and cybersecurity systems have been relying on traditional three factor authentication to mitigate the threats posed by insider attacks. Typically, systems use one or two of the following factors to authenticate end-users: what you know (e.g., password), what you have (e.g., RSA ID), or what you are (e.g., fingerprint). Systems based on these factors have the following limitations: 1) access is typically bound to a single authentication occurrence leading to remote vulnerabilities, 2) the factors have little impact against persistent insider threats, and 3) many of the authentication systems violate system design principles such as user psychological acceptability by inconveniencing the end-users. In order to mitigate the identified limitations, we propose the usage of "where you are" as a complementary factor that can significantly improve both cybersecurity and physical security. Having accurate location tracking as a new factor for authentication: 1) provides continuous identification tracking and continuous mediation of access to resources, 2) requires remote threats to acquire a physical presence, 3) allows for the enforcement of cybersecurity and physical security policies in real-time through automation, and 4) provides enhanced security without inconveniencing the end-users. Using the strength of location as an authentication factor, this paper specifies design requirements that must be present in an insider-threat Prevention System (iTPS) that is capable of actively monitoring malicious insider behaviors. iTPS has the potential to radically change the physical protection systems and cybersecurity landscape by providing practitioners with the first-of-its-kind tool for real-time insider-threat prevention capabilities. iTPS is particularly suited to address the safety and security needs of critical infrastructure, nuclear facilities, and emergency response situations.
The development of vision-based navigation algorithms using a camera is becoming more important. The vision-based navigation can be categorized into two types. The first is to use sequential camera images as relative navigation. The second is to estimate the absolute navigation solution using a camera image and database. In absolute navigation, the difference between the database and the camera image is a major obstacle to image registration. One of the factors that make a lot of difference is the shadow effect. This shadow increases the inconsistency between the two images and eventually degrades the localization accuracy. This means that shadows have a significant impact when measuring the similarity of the two templates. To mitigate this effect, we inherited and developed the Monte Carlo Localization (MCL) algorithm based on a new similarity cost function, which is a key contribution to this paper. We have established the importance of information with information reallocation logic that considers shadow areas. The proposed algorithm allocates the importance of the information considering a portion of the shaded area in the camera image. First of all, we analyzed the effects of shadows on the camera. To compare the performance of the algorithm, we used not only the shadow restoration algorithm but also various template-based matching algorithms. The proposed algorithm is validated through various simulations and real flight experiments as well.
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In nuclear facilities, having efficient accountability of critical assets, personnel locations, and activities is essential for productive, safe, and secure operations. Such accountability tracked through standard manual procedures is highly inefficient and prone to human error. The ability to actively and autonomously monitor both personnel and critical assets can significantly enhance security and safety operations while removing significant levels of human reliability issues and reducing insider threat concerns. A Real-Time Location System (RTLS) encompasses several technologies that use wireless signals to determine the precise location of tagged critical assets or personnel. RTLS systems include tags that either transmit or receive signals at regular intervals, location sensors/beacons that receive/transmit signals, and a location appliance that collects and correlates the data. Combined with ephemeral biometrics (EB) to validate the live-state of a user, an ephemeral biometrically-enhanced RTLS (EMBERS) can eliminate time-consuming manual searches and audits by providing precise location data. If critical assets or people leave a defined secured area, EMBERS can automatically trigger an alert and function as an access control mechanism and/or ingress/egress monitoring tool. Three different EMBERS application scenarios for safety and security have been analyzed and the heuristic results of this study are outlined in this paper along with areas of technological improvements and innovations that can be made if EMBERS is to be used as safety and security tool.
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