Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks

The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic analysis using traditional methods (e.g., through classical machine-learning models) is much less effective under those settings, as the features picked manually are not distinctive any more. In this work, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue

UHF Energy Harvesting and Power Management

As we are entering the era of Internet of Things (i.e. IoT), the physical devices become increasingly connected with each other than ever before. The connection between devices is achieved through wireless communication schemes, which unfortunately consume a significant amount of energy. This is undesirable for devices which are not directly connected to power. This is because these devices will essentially carry batteries to supply the needed energy for these operations and the batteries will eventually be depleted. This motivates the need to operate these devices off harvested energy. UHF energy harvesting, as an enabling technology for the UHF RFID, stands out amongst other energy harvesting approaches as it does not heavily rely on the natural surrounding environment and also offers a very good wireless operating range from its radiating energy source. Unlike the RFID, the power consumption and the operational range requirement of these IoT devices can vary significantly. Thus, the design of the RF energy harvesting front-end and the power management need to be re-thought for specific applications. To that end, in this thesis, discussions mainly evolve around the design of UHF energy harvesters and their associated power management units using lower power analog approaches. First, we present the background of the low power UHF energy harvesting, specially threshold-compensated rectifiers will be presented as a key technology in this area and this will be used as a build practical harvester for the UHF RFID application. Secondly, key issues with the threshold compensation will be identified and this is exploited either (i) to improve the dynamic power conversion efficiency of the harvester, (ii) to improve dynamic settling behaviour of the harvester. To exploit the ”left-over” harvested energy, an intelligent integrated power management solution has been proposed. Finally, the charge-burst approach is exploited to implement an energy harvester with -40 dBm input power sensitivity.Thesis (Ph.D.) -- University of Adelaide, School of Electrical & Electronic Engineering, 201

Zika Virus Induced More Severe Inflammatory Response Than Dengue Virus in Chicken Embryonic Livers

Dengue (DENV) and Zika virus (ZIKV) are important flaviviruses in tropical and subtropical regions, causing severe Dengue Hemorrhagic Fever (DHF)/Dengue Shock Syndrome (DSS) and microcephaly, respectively. The infection of both viruses during pregnancy were reported with adverse fetal outcomes. To investigate the effects of ZIKV and DENV infections on fetal development, we established an infection model in chicken embryos. Compared with DENV-2, the infection of ZIKV significantly retarded the development of chicken embryos. High viral loads of both DENV-2 and ZIKV was detected in brain, eye and heart 7 and 11 days post-infection, respectively. Interestingly, only ZIKV but not DENV-2 was detected in the liver. Even both of them induced apparent liver inflammation, ZIKV infection showed a more severe inflammatory response than DENV-2 infection based on the inflammation scores and the gene expression levels of IL-1β, TNF, IL-6, and TGFβ-2 in liver. Our results demonstrated that ZIKV induced more severe inflammatory response in chicken embryo liver compared to DENV-2, which might partially attribute to viral replication in liver cells. Clinicians should be aware of the potential liver injury associated with ZIKV infection in patients, especially in perinatal fetuses

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Creación y Simulación de Metodologías de Análisis, Clasificación e Integración de Nuevos Requerimientos a Software Propietario

La priorización de nuevos requerimientos a implementar en un software propietario es un punto fundamental para su mantenimiento, la conservación de la calidad, observación de las reglas de negocio y los estándares de la empresa. Aunque existen herramientas de priorización basadas en técnicas probadas y reconocidas, las mismas requieren una calificación previa de cada requerimiento. Si la empresa cuenta con solicitudes provenientes de varios clientes de un mismo producto, aumentan los factores que afectan a la empresa, las herramientas disponibles no contemplan estos aspectos y hacen mucho más compleja la tarea de calificación. Este trabajo de investigación abarca la realización de un relevamiento de los métodos de priorización y selección de nuevos requerimientos utilizados por empresas de la zona de Rosario, y la definición de una metodología para la selección un nuevo requerimiento, que implica el análisis y evaluación de todas las implicaciones sobre el producto de software y la empresa, respetando sus reglas de negocio. La metodología creada conduce a la definición de los procesos para la construcción de una herramienta de calificación y priorización de nuevos requerimientos en software propietario que tiene solicitudes de varios clientes al mismo tiempo, con instrumentos de calificación que consideran todos los aspectos relacionados, proveerá técnicas de priorización actuales y emitirá informes personalizados según diferentes perspectivas de la empresa.Eje: Ingeniería de SoftwareRed de Universidades con Carreras en Informática (RedUNCI