A Holistic Systems Security Approach Featuring Thin Secure Elements for Resilient IoT Deployments

© 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.IoT systems differ from traditional Internet systems in that they are different in scale, footprint, power requirements, cost and security concerns that are often overlooked. IoT systems inherently present different fail-safe capabilities than traditional computing environments while their threat landscapes constantly evolve. Further, IoT devices have limited collective security measures in place. Therefore, there is a need for different approaches in threat assessments to incorporate the interdependencies between different IoT devices. In this paper, we run through the design cycle to provide a security-focused approach to the design of IoT systems using a use case, namely, an intelligent solar-panel project called Daedalus. We utilise STRIDE/DREAD approaches to identify vulnerabilities using a thin secure element that is an embedded, tamper proof microprocessor chip that allows the storage and processing of sensitive data. It benefits from low power demand and small footprint as a crypto processor as well as is compatible with IoT 29 requirements. Subsequently, a key agreement based on an asymmetric cryptographic scheme, namely B-SPEKE was used to validate and authenticate the source. We find that end-to-end and independent stand-alone procedures used for validation and encryption of the source data originating from the solar panel are cost-effective in that the validation is carried out once and not several times in the chain as is often the case. The threat model proved useful not so much as a panacea for all threats but provided the framework for the consideration of known threats, and therefore appropriate mitigation plans to be deployed.Peer reviewe

A Prolyl-Isomerase Mediates Dopamine-Dependent Plasticity and Cocaine Motor Sensitization

SummarySynaptic plasticity induced by cocaine and other drugs underlies addiction. Here we elucidate molecular events at synapses that cause this plasticity and the resulting behavioral response to cocaine in mice. In response to D1-dopamine-receptor signaling that is induced by drug administration, the glutamate-receptor protein metabotropic glutamate receptor 5 (mGluR5) is phosphorylated by microtubule-associated protein kinase (MAPK), which we show potentiates Pin1-mediated prolyl-isomerization of mGluR5 in instances where the product of an activity-dependent gene, Homer1a, is present to enable Pin1-mGluR5 interaction. These biochemical events potentiate N-methyl-D-aspartate receptor (NMDAR)-mediated currents that underlie synaptic plasticity and cocaine-evoked motor sensitization as tested in mice with relevant mutations. The findings elucidate how a coincidence of signals from the nucleus and the synapse can render mGluR5 accessible to activation with consequences for drug-induced dopamine responses and point to depotentiation at corticostriatal synapses as a possible therapeutic target for treating addiction

Functional and Structural Connectivity, and the Effects of Neurofeedback Training, in Imitation-Related Brain Networks in Autism

Autism is characterized by marked dysfunction in social behaviors, but the neuropathology underlying these deficits is not fully understood. A potential biomarker of social dysfunction in autism is impaired brain activation and abnormal connectivity in regions involved in imitation, including the human mirror neuron system (hMNS). This dissertation uses multimodal neuroimaging techniques to further characterize the function of imitation-related brain areas in autism. FMRI is used to examine activation in hMNS areas during a task that required participants to observe and execute motor movements. Resting state functional connectivity MRI is used to examine correlations in spontaneous BOLD-signal fluctuations within an imitation network. Diffusion-weighted imaging is used to examine structural characteristics of white matter fiber tracts connecting key nodes of the same imitation network. An additional goal of this dissertation is to investigate the effects of mu-rhythm-based neurofeedback training in individuals with autism. Currently, there are few therapeutic interventions that are effective in ameliorating the social symptoms of autism, and those that do exist require heavy investments of time, effort, and money. Neurofeedback training is a novel approach that has already been shown to be efficacious to some degree in this domain. Specifically, mu-rhythm based NFT, which targets a biomarker of hMNS function, may be able to induce lasting neuroplastic changes in the autistic brain and may in turn lead to positive behavioral outcomes. This dissertation is in part a study of the effects of 20 or more hours of NFT on task-related activation and functional connectivity in the hMNS in autism

Positive effects of neurofeedback on autism symptoms correlate with brain activation during imitation and observation.

Autism has been characterized by atypical task-related brain activation and functional connections, coinciding with deficits in sociocommunicative abilities. However, evidence of the brain's experience-dependent plasticity suggests that abnormal activity patterns may be reversed with treatment. In particular, neurofeedback training (NFT), an intervention based on operant conditioning resulting in self-regulation of brain electrical oscillations, has shown increasing promise in addressing abnormalities in brain function and behavior. We examined the effects of ≥ 20 h of sensorimotor mu-rhythm-based NFT in children with high-functioning autism spectrum disorders (ASD) and a matched control group of typically developing children (ages 8-17). During a functional magnetic resonance imaging imitation and observation task, the ASD group showed increased activation in regions of the human mirror neuron system following the NFT, as part of a significant interaction between group (ASD vs. controls) and training (pre- vs. post-training). These changes were positively correlated with behavioral improvements in the ASD participants, indicating that mu-rhythm NFT may be beneficial to individuals with ASD

Resting State Functional Connectivity MRI among Spectral MEG Current Sources in Children on the Autism Spectrum.

Social and communicative impairments are among the core symptoms of autism spectrum disorders (ASD), and a great deal of evidence supports the notion that these impairments are associated with aberrant functioning and connectivity of various cortical networks. The present study explored the links between sources of MEG amplitude in various frequency bands and functional connectivity MRI in the resting state. The goal of combining these modalities was to use sources of neural oscillatory activity, measured with MEG, as functionally relevant seed regions for a more traditional pairwise fMRI connectivity analysis. We performed a seed-based connectivity analysis on resting state fMRI data, using seed regions derived from frequency-specific amplitude sources in resting state MEG data in the same nine subjects with ASD (10-17 years of age). We then compared fMRI connectivity among these MEG-source-derived regions between participants with autism and typically developing, age-matched controls. We used a source modeling technique designed for MEG data to detect significant amplitude sources in six frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low gamma (30-60 Hz), and high gamma (60-120 Hz). MEG-derived source maps for each participant were co-registered in standard MNI space, and group-level source maps were obtained for each frequency. For each frequency band, the 10 largest clusters resulting from these t-tests were used as regions of interest (ROIs) for the fMRI functional connectivity analysis. Pairwise BOLD signal correlations were obtained between each pair of these ROIs for each frequency band. Each pairwise correlation was compared between the ASD and TD groups using t-tests. We also constrained these pairwise correlations to known network structures, resulting in a follow-up set of correlation matrices specific to each network we considered. Frequency-specific MEG sources had distinct patterns of fMRI resting state functional connectivity in the ASD group, but perhaps the most significant was a finding of hypoconnectivity between many sources of low and high gamma activity. These novel findings suggest that in ASD there are differences in functionally defined networks as shown in previous fMRI studies, as well as between sets of regions defined by magnetoencephalographic neural oscillatory activity

Resting State Functional Connectivity MRI among Spectral MEG Current Sources in Children on the Autism Spectrum.

Social and communicative impairments are among the core symptoms of autism spectrum disorders (ASD), and a great deal of evidence supports the notion that these impairments are associated with aberrant functioning and connectivity of various cortical networks. The present study explored the links between sources of MEG amplitude in various frequency bands and functional connectivity MRI in the resting state. The goal of combining these modalities was to use sources of neural oscillatory activity, measured with MEG, as functionally relevant seed regions for a more traditional pairwise fMRI connectivity analysis. We performed a seed-based connectivity analysis on resting state fMRI data, using seed regions derived from frequency-specific amplitude sources in resting state MEG data in the same nine subjects with ASD (10-17 years of age). We then compared fMRI connectivity among these MEG-source-derived regions between participants with autism and typically developing, age-matched controls. We used a source modeling technique designed for MEG data to detect significant amplitude sources in six frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low gamma (30-60 Hz), and high gamma (60-120 Hz). MEG-derived source maps for each participant were co-registered in standard MNI space, and group-level source maps were obtained for each frequency. For each frequency band, the 10 largest clusters resulting from these t-tests were used as regions of interest (ROIs) for the fMRI functional connectivity analysis. Pairwise BOLD signal correlations were obtained between each pair of these ROIs for each frequency band. Each pairwise correlation was compared between the ASD and TD groups using t-tests. We also constrained these pairwise correlations to known network structures, resulting in a follow-up set of correlation matrices specific to each network we considered. Frequency-specific MEG sources had distinct patterns of fMRI resting state functional connectivity in the ASD group, but perhaps the most significant was a finding of hypoconnectivity between many sources of low and high gamma activity. These novel findings suggest that in ASD there are differences in functionally defined networks as shown in previous fMRI studies, as well as between sets of regions defined by magnetoencephalographic neural oscillatory activity

Differences in regional brain structure in toddlers with autism are related to future language outcomes.

Language and social symptoms improve with age in some autistic toddlers, but not in others, and such outcome differences are not clearly predictable from clinical scores alone. Here we aim to identify early-age brain alterations in autism that are prognostic of future language ability. Leveraging 372 longitudinal structural MRI scans from 166 autistic toddlers and 109 typical toddlers and controlling for brain size, we find that, compared to typical toddlers, autistic toddlers show differentially larger or thicker temporal and fusiform regions; smaller or thinner inferior frontal lobe and midline structures; larger callosal subregion volume; and smaller cerebellum. Most differences are replicated in an independent cohort of 75 toddlers. These brain alterations improve accuracy for predicting language outcome at 6-month follow-up beyond intake clinical and demographic variables. Temporal, fusiform, and inferior frontal alterations are related to autism symptom severity and cognitive impairments at early intake ages. Among autistic toddlers, brain alterations in social, language and face processing areas enhance the prediction of the childs future language ability

Quitting starts in the brain: a randomized controlled trial of app-based mindfulness shows decreases in neural responses to smoking cues that predict reductions in smoking

Current treatments for smoking yield suboptimal outcomes, partly because of an inability to reduce cue-induced smoking. Mindfulness training (MT) has shown preliminary efficacy for smoking cessation, yet its neurobiological target remains unknown. Our prior work with nonsmokers indicates that MT reduces posterior cingulate cortex (PCC) activity. In individuals who smoke, the PCC, consistently a main hub of the default mode network, activates in response to smoking cues. In this randomized controlled trial, we tested the effects of app-delivered MT on PCC reactivity to smoking cues and whether individual differences in MT-mediated PCC changes predicted smoking outcomes. Smoking cue-induced PCC reactivity was measured using functional magnetic resonance imaging at baseline and 1 month after receiving smartphone app-based MT (n = 33) vs. an active control (National Cancer Institute\u27s QuitGuide, n = 34). Whether individual differences in treatment-related changes in PCC activity predicted smoking behavior was assessed. The MT group demonstrated a significant correlation between a reduction in PCC reactivity to smoking cues and a decline in cigarette consumption (r = 0.39, p = 0.02). No association was found in the control group (r = 0.08, p = 0.65). No effects of group alone were found in PCC or cigarette reduction. Post hoc analysis revealed this association is sex specific (women, r = 0.49, p = 0.03; men: r = -0.08, p = 0.79). This initial report indicates that MT specifically reduces smoking cue-induced PCC activity in a subject-specific manner, and the reduction in PCC activity predicts a concurrent decline in smoking. These findings link the hypothesized behavioral effects of MT for smoking to neural mechanisms particularly in women. This lays the groundwork for identifying individuals who may benefit from targeted digital therapeutic treatments such as smartphone-based MT, yielding improved clinical outcomes