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

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

Dysbindin-1 Mutation Alters Prefrontal Cortex Extracellular Glutamate and Dopamine In Vivo.

Elevated risk for schizophrenia is associated with a variation in the DTNBP1 gene encoding dysbindin-1, which may underpin cognitive impairments in this prevalent neuropsychiatric disorder. The cognitive symptoms of schizophrenia involve anomalies in glutamate and dopamine signaling, particularly within the prefrontal cortex (PFC). Indeed, mice with Dtnbp1 mutations exhibit spatial and working memory deficits that are associated with deficits in glutamate release and NMDA receptor function as determined by slice electrophysiology. The present study extended the results from ex vivo approaches by examining how the Dtnbp1 mutation impacts high K+- and NMDA receptor-evoked glutamate release within the PFC using in vivo microdialysis procedures. Dntbp1 mutant mice are also reported to exhibit blunted K+-evoked dopamine release within the PFC. Thus, we examined also K+- and NMDA-evoked dopamine release within this region. Perfusion of high-concentration K+ or NMDA solutions increased the PFC levels of both dopamine and glutamate in wild-type (WT) but not in Dtnbp1 mutants (MUT), whereas mice heterozygous for the Dtnbp1 mutation (HET) exhibited blunted K+-evoked dopamine release. No net-flux microdialysis procedures confirmed elevated basal extracellular content of both glutamate and dopamine within the PFC of HET and MUT mice. These in vivo microdialysis results corroborate prior indications that Dtnbp1 mutations perturb evoked dopamine and glutamate release within the PFC, provide in vivo evidence for impaired NMDA receptor function within the PFC, and suggest that these neurochemical anomalies may be related to abnormally elevated basal neurotransmitter content

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