A Sybil attack consists of an adversary assuming multiple identities to defeat the trust of an existing reputation system. When Sybil attacks are launched in vehicular networks, the mobility of vehicles increases the difficulty of identifying the malicious vehicle location. In this paper, a novel protocol for Sybil detection in vehicular networks is presented. Considering that vehicular networks are cyber-physical systems, the technique exploits well grounded results in the physical (i.e., transportation) domain to detect the Sybil attacks in the cyber domain. Compared to existing works that rely on additional cyber hardware support, or complex cryptographic primitives for Sybil detection, the protocol leverages the theory of platoon dispersion that models the physics of naturally occurring vehicle dispersion. Specifically, the proposed technique employs a certain number of roadside units that periodically collect reports from vehicles regarding their physical neighborhood. Leveraging from existing models of platoon dispersion, a protocol was designed to detect anomalously close neighborhoods that are reflective of Sybil attacks. To the best of the authors' knowledge, this paper is unique in integrating a well established theory in transportation engineering for detecting cyber space attacks in vehicular networks. The resulting protocol is simple, efficient, and robust in diverse attack environments.
In the United States, there are more than 35, 000 reported suicides with approximately 1, 800 of them being psychiatric inpatients. Staff perform intermittent or continuous observations in order to prevent such tragedies, but a study of 98 articles over time showed that 20% to 62% of suicides happened while inpatients were on an observation schedule. Reducing the instances of suicides of inpatients is a problem of critical importance to both patients and healthcare providers. In this paper, we introduce SHARE -A Self-Harm Activity Recognition Engine, which attempts to infer self-harming activities from sensing accelerometer data using smart devices worn on a subject's wrist. Preliminary classification accuracy of 80% was achieved using data acquired from 4 subjects performing a series of activities (both self-harming and not). The results, application, and proposed technology platform are discussed in-depth.
Software design and development often presents a high-risk element during the execution of engineering projects due to devaluation of possible conflicts or identifying defects during late stages of development. Many defects identified during the late stages of small spacecraft development can be avoided by constructing interactive, dynamic models. This process is often followed for hardware fabrication/test, but often not to the same extent for software. An alternative process to the typical software development is needed that enables modeling and simulation feedback at early design stages. Petri nets allow for software visualization, simulation and verification in a cost-effective way. An alternative software modeling approach using Petri nets is presented to rapidly design, develop and verify/validate small spacecraft software. Using the presented techniques, the Missouri University of Science and Technology Satellite Research Team successfully demonstrated core functionality of their software system at the Final Concept Review (FCR) of AFRL's University Nanosat Program's Nanosat-7 Competition.
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