Sensing via signal analysis, analytics, and cyberbiometric patterns

Includes bibliographical references.2022 Fall.Internet-connected, or Internet of Things (IoT), sensor technologies have been increasingly incorporated into everyday technology and processes. Their functions are situationally dependent and have been used for vital recordings such as electrocardiograms, gait analysis and step counting, fall detection, and environmental analysis. For instance, environmental sensors, which exist through various technologies, are used to monitor numerous domains, including but not limited to pollution, water quality, and the presence of biota, among others. Past research into IoT sensors has varied depending on the technology. For instance, previous environmental gas sensor IoT research has focused on (i) the development of these sensors for increased sensitivity and increased lifetimes, (ii) integration of these sensors into sensor arrays to combat cross-sensitivity and background interferences, and (iii) sensor network development, including communication between widely dispersed sensors in a large-scale environment. IoT inertial measurement units (IMU's), such as accelerometers and gyroscopes, have been previously researched for gait analysis, movement detection, and gesture recognition, which are often related to human-computer interface (HCI). Methods of IoT Device feature-based pattern recognition for machine learning (ML) and artificial intelligence (AI) are frequently investigated as well, including primitive classification methods and deep learning techniques. The result of this research gives insight into each of these topics individually, i.e., using a specific sensor technology to detect carbon monoxide in an indoor environment, or using accelerometer readings for gesture recognition. Less research has been performed on analyzing the systems aspects of the IoT sensors themselves. However, an important part of attaining overall situational awareness is authenticating the surroundings, which in the case of IoT means the individual sensors, humans interacting with the sensors, and other elements of the surroundings. There is a clear opportunity for the systematic evaluation of the identity and performance of an IoT sensor/sensor array within a system that is to be utilized for "full situational awareness". This awareness may include (i) non-invasive diagnostics (i.e., what is occurring inside the body), (ii) exposure analysis (i.e., what has gone into the body through both respiratory and eating/drinking pathways), and (iii) potential risk of exposure (i.e., what the body is exposed to environmentally). Simultaneously, the system has the capability to harbor security measures through the same situational assessment in the form of multiple levels of biometrics. Through the interconnective abilities of the IoT sensors, it is possible to integrate these capabilities into one portable, hand-held system. The system will exist within a "magic wand", which will be used to collect the various data needed to assess the environment of the user, both inside and outside of their bodies. The device can also be used to authenticate the user, as well as the system components, to discover potential deception within the system. This research introduces levels of biometrics for various scenarios through the investigation of challenge-based biometrics; that is, biometrics based upon how the sensor, user, or subject of study responds to a challenge. These will be applied to multiple facets surrounding "situational awareness" for living beings, non-human beings, and non-living items or objects (which we have termed "abiometrics"). Gesture recognition for intent of sensing was first investigated as a means of deliberate activation of sensors/sensor arrays for situational awareness while providing a level of user authentication through biometrics. Equine gait analysis was examined next, and the level of injury in the lame limbs of the horse was quantitatively measured and classified using data from IoT sensors. Finally, a method of evaluating the identity and health of a sensor/sensory array was examined through different challenges to their environments

Naval Reserve support to information Operations Warfighting

Since the mid-1990s, the Fleet Information Warfare Center (FIWC) has led the Navy's Information Operations (IO) support to the Fleet. Within the FIWC manning structure, there are in total 36 officer and 84 enlisted Naval Reserve billets that are manned to approximately 75 percent and located in Norfolk and San Diego Naval Reserve Centers. These Naval Reserve Force personnel could provide support to FIWC far and above what they are now contributing specifically in the areas of Computer Network Operations, Psychological Operations, Military Deception and Civil Affairs. Historically personnel conducting IO were primarily reservists and civilians in uniform with regular military officers being by far the minority. The Naval Reserve Force has the personnel to provide skilled IO operators but the lack of an effective manning document and training plans is hindering their opportunity to enhance FIWC's capabilities in lull spectrum IO. This research investigates the skill requirements of personnel in IO to verify that the Naval Reserve Force has the talent base for IO support and the feasibility of their expanded use in IO.http://archive.org/details/navalreservesupp109451098

Expanding the Hyper-Enabled Operator Technology across the Special Forces Enterprise

The Hyper-Enabled Operator (HEO) system is the next-generation Special Forces system that will increase the survivability and lethality of operators by providing the right person with the right information at the right time. This system was originally intended for direct-action operators; however, the need for information is common to many Special Forces jobs, including joint terminal air controllers, helicopter pilots, helicopter crew chiefs, intelligence officers, psyops officers, civil affairs officers, and vehicle drivers. This analysis set out to determine the applicability of HEO technology to these eight different positions. First, the HEO system was analyzed to identify the technologies that will play a role in the system. Stakeholder analysis then provided insights into each job, allowing for the determination of their capability gaps. These capability gaps were then aligned against HEO technology. The analysis revealed that several high-level requirements should be added to the HEO system to make it adaptable across the Special Forces enterprise

In God we trust, all others we scan for malware: a study on the effect of trust in using AI empowered smartphones.

The acceptance of new technologies is an important subject in organizational research that has a long past of theorizing. Models such as C-TAM (Bruner & Kumar, 2005) have been proposed to explain the intention to use such technologies. However, such models were developed prior to the incorporation of Artificial Intelligence (AI), one of the latest and most exciting themes in today's society, whose applications increase from day to day. The introduction of this new technology has substantially changed the technology's status regarding its potential impact and subtle influence on free choice. The penetration that AI-powered technology has gained through smartphones is one of the phenomena that deserves attention, especially in the face of controversial opinions from personality leaders in science and technology like Stephen Hawking and Elon Musk on the topic of trust. This study sought to test the role of trust as a mediator between C-TAM and the intention to use AI-enhanced applications. With a sample of 211 smartphone users, the results showed several total mediations, highlighting the key role trust plays in the successful implementation of this technology.A aceitação de novas tecnologias é um assunto importante na investigação organizacional que tem um longo passado de teorização. Modelos tais como o C-TAM (Bruner & Kumar, 2005) têm sido propostos para explicar a intenção de usar tais tecnologias. Contudo, tais modelos foram desenvolvidos antes da incorporação da Inteligência Artificial (IA), um dos temas mais recentes e entusiasmantes da sociedade atual, cujas aplicações aumentam de dia para dia. A introdução desta nova tecnologia mudou substancialmente o estatuto da tecnologia no que concerne ao seu impacto potencial e á influência subtil na livre escolha. A penetração que a tecnologia potenciada pela IA tem ganho através dos smartphones é um dos fenómenos que merece atenção, especialmente face às opiniões controversas emitidas por personalidade líderes na ciência e tecnologia como Stephen Hawking e Elon Musk relativas ao tópico da confiança. Este estudo procurou testar o papel da confiança como mediadora entre o C-TAM e a intenção de utilizar aplicações potenciadas com IA. Com uma amostra de 211 utilizadores de smartphone, os resultados mostraram várias mediações totais, salientando o papel fundamental que a confiança desempenha na implementação bem-sucedida desta tecnologia

A Conceptual Model for Network Decision Support Systems

We introduce the concept of a network DSS (NWDSS) consisting of fluid, heterogeneous nodes of human and machine agents, connected by wireless technology, which may enter and leave the network at unpredictable times, yet must also cooperate in decision-making activities. We describe distinguishing properties of the NWDSS and propose a 3-tier conceptual model comprised of digital infrastructure, transactive memory systems and emergent collaborative decision-making. We suggest a decision loop of Sense-Analyze-Adapt-Memory leveraging TMS as a starting point for addressing the agile collaborative requirements of emergent decision-making. Several examples of innovative NWDSS services are presented from Naval Postgraduate School field experiments

Application of Self-Monitoring for Situational Awareness

Self-monitoring devices and services are used for physical wellness, personal tracking and self-improvement. These individual devices and services can only provide information based on what they can measure directly or historically without an intermediate system. This paper proposes a self-monitoring system to perform situational awareness which may extend into providing insight into predictable behaviors. Knowing an individual’s current state and likelihood of particular behaviors occurring is a general solution. This knowledge-based solution derived from sensory data has many applications. The proposed system could monitor current individual situational status, automatically provide personal status as it changes, aid personal improvement, contribute to other self-monitoring systems, and enhance other life-tracking objectives

Implementing Resiliency of Adaptive Multi-Factor Authentication Systems

Multifactor authentication (MFA) is getting increasingly more popular to safeguard systems from unauthorized users access. Adaptive Multi-Factor Authentication (A-MFA) is an enhanced version of MFA that provides a method to allow legitimate users to access a system using different factors that are changing based on different considerations. In other words, authentication factors include passwords, biometrics among others are adaptively selected by the authentication system based on criteria (e.g., whether the user is trying to log in from within system boundary, or whether or not the user is trying to access during organization operating hours). The criteria (i.e. triggering events) that A-MFA uses to select authentication factors adaptively are usually pre-defined and hard-coded in the authentication system itself. In this paper, the graphical user interface application is designed to add more resiliency to the existing Adaptive Multi-Factor Authentication (A-MFA) method by enabling system administrators to rank the triggering criteria based on the users’ roles, system assets, tolerance to risks, etc. The proposed tool allows system administrators to determine when to tighten and soften user access to the system. The tool uses multiple criteria decision making (MCDM) method to allow system admins to access the trustworthiness of user. Based on the trustworthiness of the user, the tool selects the number and complexity of the authentication methods. This tool will help to utilize the systems administrator situational awareness to improve security. This work aims to preserve the AMFA strengths and at the same time give system administrators more flexibility and authority in controlling access to systems

Bulk Biometric Metadata Collection

Smart police body cameras and smart glasses worn by law enforcement increasingly reflect state-of-the-art surveillance technology, such as the integration of live-streaming video with facial recognition and artificial intelligence tools, including automated analytics. This Article explores how these emerging cybersurveillance technologies risk the potential for bulk biometric metadata collection. Such collection is likely to fall outside the scope of the types of bulk metadata collection protections regulated by the USA FREEDOM Act of 2015. The USA FREEDOM Act was intended to bring the practice of bulk telephony metadata collection conducted by the National Security Agency (“NSA”) under tighter regulation. In the wake of the disclosures by Edward Snowden in June 2013, members of Congress called for statutory reform to eliminate or significantly curtail indiscriminate metadata surveillance of United States citizens. The Snowden revelations illuminated that the bulk telephony metadata collection program had been legally justified under Section 215 of the USA PATRIOT Act. This Article contends that the USA FREEDOM Act, which amended Section 215 of the USA PATRIOT Act, does not restrict other types of non-telephony bulk metadata collection. This Article concludes that, rather than more tightly regulating metadata surveillance, the Act allows for metadata surveillance to proceed under differing justifications and in more delegated contexts. The potential of ubiquitous and continuous data collection and analysis that may stem from smart body cameras or smart glasses worn by law enforcement offers an important case study on why the USA FREEDOM Act is unable to regulate bulk biometric metadata collection