In the last decade integration of Internet of Things (IoT) ecosystems has increased exponentially, and it is necessary that our understanding of human behavior when interacting with multiple smart devices in an IoT ecosystem keep pace.To better understand users' perceptions and use of in-home IoT ecosystem over time, we implemented an ecosystem in homes of participants so that we could both test previous findings about individual devices and identify differences that arise in the content of a home with multiple IoT devices.Specifically, we recruited eight participants from separate households who installed identical IoT configurations, and interviewed each participant for five weeks.We included an Android dashboard to provide device control and data transparency.We detail the semi-structured interviews to compare user perceptions of what devices are classified as IoT, the perceived sustainability of IoT devices, interactions with and desires of dashboard information, and exploration of current notification preferences and mitigation strategies.We discuss the factors which participants identified as being relevant to their personal experiences with IoT devices and contribute recommendations for dashboard designs and control mechanisms for IoT devices.We note that the participants uniformly had a more expansive definition of IoT than that found in much of the previous literature, implying that our understanding of perceptions of in-home IoT may be informed by previous research on security systems, wearables, watches, and phones.We identify where our results reify findings of studies of those devices.

Manufacturer Usage Description (MUD) is an Internet Engineering Task Force (IETF) standard designed to protect IoT devices and networks by creating an out-of-the-box access control list for an IoT device. Access control list of each device is defined in its MUD-File and may contain possibly hundreds of access control rules. As a result, reading and validating these files is a challenge, and determining how multiple IoT devices interact is difficult for the developer and infeasible for the consumer. To address this, we introduce the MUD-Visualizer to provide a visualization of any number of MUD-Files. MUD-Visualizer is designed to enable developers to produce correct MUD-Files by providing format correction, integrating them with other MUD-Files, and identifying conflicts through visualization. MUD-Visualizer is scalable, and its core task is to merge and illustrate ACEs for multiple devices, both within and beyond the local area network. MUD-Visualizer is made publicly available and can be found in GitHub.

Studies of privacy perception in the Internet of Things (IoT) include in-laboratory evaluations as well as investigations of purchase decisions, deployment, and long-term use. In this study, we implemented identical IoT configurations in eight households to evaluate the installation and privacy concerns in the early adoption of IoT devices in our participants' homes. The specific contributions of this work are insights into privacy perceptions of and challenges to the adoption of networked smart home devices and privacy management of IoT devices. The focus in this work was on participants' privacy concerns about devices deployed in an IoT ecosystem influencing their gradual change of use. We detail how we use a three-week longitudinal interview protocol to compare user perceptions of privacy risk. We assessed users' comfort with devices, perceived benefits, and data sensitivity. We discuss the factors identified by participants as relevant to their personal security and privacy management of in-home devices. We close with recommendations for privacy preserving smart home devices grounded in our participants' experiences.

Browser fingerprinting is a surveillance technique that uses browser and device attributes to track visitors across the web.Defeating fingerprinting requires blocking attribute information or spoofing attributes, which can result in loss of functionality.To address the challenge of escaping surveillance while obtaining functionality, we identify six design criteria for an ideal spoofing system.We present three fingerprint generation algorithms as well as a baseline algorithm that simply samples a dataset of fingerprints.For each algorithm, we identify trade-offs among the criteria: distinguishability from a non-spoofed fingerprint, uniqueness, size of the anonymity set, efficient generation, loss of web functionality, and whether or not the algorithm protects the confidentiality of the underlying dataset.We report on a series of experiments illustrating that the use of our Partially-Dependent algorithm for spoofing fingerprints will avoid detection by machine learning approaches to surveillance.

The IETF Manufacturer Usage Description (MUD) standard was designed to protect IoT devices through network micro-segmentation. In practice, this is implemented using per-device access control that is defined by the manufacturer. This access control is embedded in a “MUD-File”, which is transferred to the user’s network during the onboarding process, and may contain from one to hundreds of rules. Validating these rules for each device can be a challenge, particularly when devices are interacting. In response, MUD-Visualizer was designed to simplify the validation of individual and interacting MUD-Files through straightforward visualizations. In this work, we report on an evaluation of the usability and efficacy of MUD-Visualizer. The results illustrate that not only it is more usable compared to manual analysis, but the participants that used MUD-Visualizer also had more accurate results in less time.

Secure installation of Internet of Things (IoT) devices requires configuring access control correctly for each device. In order to enable correct configuration Manufacturer Usage Description (MUD) has been developed by Internet Engineering Task Force (IETF) to automate the protection of IoT devices by micro-segmentation using dynamic access control lists. The protocol defines a conceptually straightforward method to implement access control upon installation by providing a list of every authorized access for each device. This access control list may contain a few rules or hundreds of rules for each device. As a result, validating these rules is a challenge. In order to make the MUD standard more usable for developers, system integrators, and network operators, we report on an interactive system called MUD-Visualizer that visualizes the files containing these access control rules. We show that, unlike manual analysis, the level of the knowledge and experience does not affect the accuracy of the analysis when MUD-Visualizer is used, indicating that the tool is effective for all participants in our study across knowledge and experience levels.

In this paper, we leverage the limited functionality of IoT devices and the homophily of a single home network to identify control plane attacks. We illustrate the use of privacy-preserving data analysis in machine learning to evaluate the leptokurtic distributions of routes from a single device in an individual home in a specific geographic location. Previously, route hijacking has been approached as a large-scale systems problem, requiring network service providers to take action. Route information from the edge has traditionally been considered inactionable, however, small enterprises and homeowners may be targeted for such attacks for reasons ranging from nations attacking suppliers in critical systems to simple monetization of e-crime. We describe how a single small entity can leverage large-scale historical data with their individual histories to identify these attacks. We implement our proposed method in the form of a local agent that monitors the IoT devices and services for detecting BGP hijacking as well as an agent server that utilizes global history in initializing the local agents.

Today, Internet of Things (IoT) devices, web browsers, phones, and even cars may be fingerprinted for tracking, and their connections routed through or to malicious entities. When IoT devices interact with a remote service, the integrity or authentication of that service is not guaranteed. IoT and other edge devices could be subject to man-in-the-middle (MiTM) attacks, with IoT devices attempting to connect to remote services. It is also straight-forward to use phishing or pharming to convince a user to accept a connection to a potentially malicious unfamiliar device. These risks could be mitigated by leveraging information on the edge of the network about the path to and destination of a connection. In this work we sample packets, then use packet analysis and local history to identify risky or suspicious connections. In contrast to other machine learning and big data approaches, the use of local data enables risk detection without loss of privacy.

Simulation of the cerebral cortex requires a combination of extensive domain-specific knowledge and efficient software. However, when the complexity of the biological system is combined with that of the software, the likelihood of coding errors increases, which slows model adjustments. Moreover, few life scientists are familiar with software engineering and would benefit from simplicity in form of a high-level abstraction of the biological model. Our primary aim was to build a scalable cortical simulation framework for personal computers. We isolated an adjustable part of the domain-specific knowledge from the software. Next, we designed a framework that reads the model parameters from comma-separated value files and creates the necessary code for Brian2 model simulation. This separation allows rapid exploration of complex cortical circuits while decreasing the likelihood of coding errors and automatically using efficient hardware devices. Next, we tested the system on a simplified version of the neocortical microcircuit proposed by Markram and colleagues (2015). Our results indicate that the framework can efficiently perform simulations using Python, C++, and GPU devices. The most efficient device varied with computer hardware and the duration and scale of the simulated system. The speed of Brian2 was retained despite an overlying layer of software. However, the Python and C++ devices inherited the single core limitation of Brian2. The CxSystem framework supports exploration of complex models on personal computers and thus has the potential to facilitate research on cortical networks and systems.

Recently, Markram et al. (2015) presented a model of the rat somatosensory microcircuit (Markram model). Their model is high in anatomical and physiological detail, and its simulation requires supercomputers. The lack of neuroinformatics and computing power is an obstacle for using a similar approach to build models of other cortical areas or larger cortical systems. Simplified neuron models offer an attractive alternative to high-fidelity Hodgkin-Huxley-type neuron models, but their validity in modeling cortical circuits is unclear. We simplified the Markram model to a network of exponential integrate-and-fire (EIF) neurons that runs on a single CPU core in reasonable time. We analyzed the electrophysiology and the morphology of the Markram model neurons with eFel and NeuroM tools, provided by the Blue Brain Project. We then constructed neurons with few compartments and averaged parameters from the reference model. We used the CxSystem simulation framework to explore the role of short-term plasticity and GABAB and NMDA synaptic conductances in replicating oscillatory phenomena in the Markram model. We show that having a slow inhibitory synaptic conductance (GABAB) allows replication of oscillatory behavior in the high-calcium state. Furthermore, we show that qualitatively similar dynamics are seen even with a reduced number of cell types (from 55 to 17 types). This reduction halved the computation time. Our results suggest that qualitative dynamics of cortical microcircuits can be studied using limited neuroinformatics and computing resources supporting parameter exploration and simulation of cortical systems. The simplification procedure can easily be adapted to studying other microcircuits for which sparse electrophysiological and morphological data are available.

Abstract The development of wireless sensor networks (WSNs) struggles with limited computing, communication, and energy resources. Bio-Integrated Systems (BISs) contain cultured cells that perform certain tasks. For instance, neurons can work as brains for robots. BISs could provide solutions to overcome WSNs resource constraints. Biological entities integrating in computing hardware gains interest especially in robotics. This interest is due to the potentials of such integration in deep learning capacity, massive parallelism, energy savings, and communication and integration with living subjects. This paper collects existing BISs research and provides research motivations for Bio-Integrated WSNs (BI-WSNs). BI-WSNs solutions for improving energy preserving, sensing, processing, and communication are proposed and supported with existing examples. Further, on-going research on integrating neural networks in WSNs is presented. Challenges related to protection of biological entities from external environment are discussed. Finally, a prospective model of BI-WSNs consisting in optogenetic communication combined with neural network processing is given. Graphical abstract This graphical abstract represents the different components of a node of a sensor networks. This paper studies the possibilities to replace each of these components with a biological counterpart enabling similar functionalities. The benefits and challenges for the development of such biological sensors, named Bio-Integrated Wireless Sensor Network (BI-WSN), are discussed in this study.

The widespread usage of new user tracking methods, i.e. web-based fingerprinting, is becoming a serious privacy concern as third parties try to track users across different websites. Meanwhile, it is usually difficult or impossible for users to opt-out fingerprinting if they want to fully benefit the services provided by the application or website. Several studies tried to address the privacy issue in browser fingerprinting, mostly by faking attribute values. However, such configuration spoofing may lead to inconsistencies that paradoxically make the user stand out even more. This study analyzes these paradoxes in browser configuration with the creation of a Markov model based on a test dataset. Given a target spoofed attribute, the implemented tool in this study outputs the other attributes that must be consequently altered, not to cause paradoxical configuration. Similarly, this tool can suggest a set of random attributes to be spoofed with suggested values, not creating a paradoxical configuration. The tool Implemented in this study can be used by browser extension developers and should help them spoof browser attributes more sophistically, thus preserving users' privacy against cross-site web-based browser fingerprinting.

A Multi-Electrode Array (MEA) is a practical device for recording the extracellular activity of in-vitro biological culture. Such culture - for instance neurons - is prone to mistakes leading to irrelevant recordings or no recording at all. Additionally, with the expenses generated by in-vitro culture, minimizing risks is a must. This paper proposes a framework designed and implemented for simulating the spatial positioning of neuronal cultures on a MEA. The framework serves as a sandbox for researchers to simulate the model of their MEA experiments before its eventual in-vitro implementation. The framework enables simulating the density of the plated culture, the death of cells over time, choosing diverse reconstructed morphologies of cells, and simulating their spiking activity in interaction with Brian2 simulator.

The observation of interactions between neurons of a network can reveal important information about how information is processed within that network. Such observation can be established with the analysis of causality between the activities of the different neurons in the network. This analysis is called effective connectivity analysis. However, methods for such analysis are either computationally heavy for daily use or too inaccurate for making reliable analyses. Cox method produces reliable analysis, but the computation takes hours on CPUs, making it slow to use on research. In this paper, two algorithms are presented that speed up analysis of Cox method by parallelizing the computation on a graphical processing unit (GPU) with the help of a Compute Unified Device Architecture platform. Both algorithms are evaluated according to the network size and recording duration. The interest of proposing GPU implementations is in gaining the computation time but another important interest is that such implementation requires rethinking the algorithm in different ways than as the sequential implementation. This rethinking itself brings new optimization possibilities, e.g. by employing OpenCL. Utilizing this accelerated implementation, the Cox method is then applied on an experimental dataset from CRCNS in a personal computer. This should facilitate observations of biological neural network organizations that can provide new insights to improve understanding of memory, learning and intelligence.

Using an activity tracker for measuring activity-related parameters, e.g. steps and energy expenditure (EE), can be very helpful in assisting a person's fitness improvement. Unlike the measuring of number of steps, an accurate EE estimation requires additional personal information as well as accurate velocity of movement, which is hard to achieve due to inaccuracy of sensors. In this paper, we have evaluated regression-based models to improve the precision for both steps and EE estimation. For this purpose, data of seven activity trackers and two reference devices was collected from 20 young adult volunteers wearing all devices at once in three different tests, namely 60-minute office work, 6-hour overall activity and 60-minute walking. Reference data is used to create regression models for each device and relative percentage errors of adjusted values are then statistically compared to that of original values. The effectiveness of regression models are determined based on the result of a statistical test. During a walking period, EE measurement was improved in all devices. The step measurement was also improved in five of them. The results show that improvement of EE estimation is possible only with low-cost implementation of fitting model over the collected data e.g. in the app or in corresponding service back-end.

Integrating neurobiological cultures with computer systems presents an opportunity to enhance computational energy efficiency. These Bio-Integrated Systems (BISs) require knowledge about structure and behavior of neural components and their interfacing. In the early design phases, modeling neurons offers cost, failure-free and retrial benefits compared to laboratory grown neural networks. The usefulness of these models lays in characteristics of being realistic but also computationally efficient.This survey reviews computational models of spiking neurons and their changes in connections, known as plasticity. The review studies models that are faithful to real neural cultures, and are computational efficient for real-time BISs. Also, criteria and methods for comparing models with 'in-vitro' experiments are reviewed to conclude on the level of realism of models in comparison with biological setups. Izhikevich's model of spiking neurons is recommended due to its accuracy in reproducing real neural firing patterns, computational efficiency, and ease of parameter adjustment. The model of Spike-timing dependent plasticity is recommended as current basis for representing neuron changes in connections. For the analysis of network connectivity and connectivity changes in BIS, the Cox method is recommended because it evaluates connections based on activities from all recorded neurons as opposed to pair-wise approaches. (C) 2015 Published by Elsevier B.V.