Security Analysis and Evaluation of Smart Toys

During the last years, interconnectivity and merging the physical and digital technological dimensions have become a topic attracting the interest of the modern world. Internet of Things (IoT) is rapidly evolving as it manages to transform physical devices into communicating agents which can consecutively create complete interconnected systems. A sub-category of the IoT technology is smart toys, which are devices with networking capabilities, created for and used in play. Smart toys’ targeting group is usually children and they attempt to provide a higher level of entertainment and education by offering an enhanced and more interactive experience. Due to the nature and technical limitations of IoT devices, security experts have expressed concerns over the effectiveness and security level of smart devices. The importance of securing IoT devices has an increased weight when it pertains to smart toys, since sensitive information of children and teenagers can potentially be compromised. Furthermore, various security analyses on smart toys have discovered a worryingly high number of important security flaws. The master thesis focuses on the topic of smart toys’ security by first presenting and analyzing the necessary literature background. Furthermore, it presents a case study where a smart toy is selected and analyzed statically and dynamically utilizing a Raspberry Pi. The aim of this thesis is to examine and apply methods of analysis used in the relevant literature, in order to identify security flaws in the examined smart toy. The smart toy is a fitness band whose target consumers involve children and teenagers. The fitness band is communicating through Bluetooth with a mobile device and is accompanied by a mobile application. The mobile application has been installed and tested on an Android device. Finally, the analyses as well as their emerged results are presented and described in detail. Several security risks have been identified indicating that developers must increase their efforts in ensuring the optimal level of security in smart toys. Furthermore, several solutions that could minimize security risks and are related to our findings are suggested, along with potentially interesting topics for future work and further research

Dynamic Effects of Monetary Policy Shocks on Macroeconomic Volatility

We use a simple New Keynesian model, with firm specific capital, non-zero steady-state inflation, long-run risks and Epstein-Zin preferences to study the volatility implications of a monetary policy shock. An unexpected increase in the policy rate by 150 basis points causes output and inflation volatility to rise around 10% above their steady-state standard deviations. VAR based empirical results support the model implications that contractionary shocks increase volatility. The volatility effects of the shock are driven by agents' concern about the (in) ability of the monetary authority to reverse deviations from the policy rule and the results are re-enforced by the presence of non-zero trend inflation

The Federal Reserve’s implicit inflation target and Macroeconomic dynamics. A SVAR analysis.

This paper identifies shocks to the Federal Reserve's inflation target as VAR innovations that make the largest contribution to future movements in long-horizon inflation expectations. The effectiveness of this scheme is documented via Monte-Carlo experiments. The estimated impulse responses indicate that a positive shock to the target is associated with a large increase in inflation, GDP growth and long-term interest rates. Target shocks are estimated to be a vital factor behind the increase in inflation during the pre-1980 period and are an important driver of the decline in long-term interest rates over the last two decades

Fiscal policy shocks and stock prices in the United States

This paper uses a range of structural VARs to show that the response of US stock prices to fiscal shocks changed in 1980. Over the period 1955-1980 an expansionary spending or revenue shock was associated with modestly higher stock prices. After 1980, along with a decline in the fiscal multiplier, the response of stock prices to the same shock became negative and larger in magnitude. We use an estimated DSGE model to show that this change is consistent with a switch from an economy characterised by active fiscal policy and passive monetary policy to one where fiscal policy was passive and the central bank acted aggressively in response to inflationary shocks

A Sparsity-Aware Adaptive Algorithm for Distributed Learning

In this paper, a sparsity-aware adaptive algorithm for distributed learning in diffusion networks is developed. The algorithm follows the set-theoretic estimation rationale. At each time instance and at each node of the network, a closed convex set, known as property set, is constructed based on the received measurements; this defines the region in which the solution is searched for. In this paper, the property sets take the form of hyperslabs. The goal is to find a point that belongs to the intersection of these hyperslabs. To this end, sparsity encouraging variable metric projections onto the hyperslabs have been adopted. Moreover, sparsity is also imposed by employing variable metric projections onto weighted $\ell_1$ balls. A combine adapt cooperation strategy is adopted. Under some mild assumptions, the scheme enjoys monotonicity, asymptotic optimality and strong convergence to a point that lies in the consensus subspace. Finally, numerical examples verify the validity of the proposed scheme, compared to other algorithms, which have been developed in the context of sparse adaptive learning

‘There’s just nothing stable anymore’: A sociological examination of the relationship between social media consumption and youth identity in an age of uncertainty

This thesis investigates the relationship between young people’s identities and the consumption of social media in a time of economic crisis. The research is designed to examine the role of self-branding in young people’s relationship with consumption and what this means for the notion of self in a digital world. In practical terms, it explores the social transformations that have emerged in an uncertain world through a comparative research between Greece and the UK focusing on young people’s consumption of social media between the ages of sixteen and thirty years old. The research is underpinned by a qualitative analysis based on primary data captured by a triangulated three-stage process. Specifically, data capture entailed: focus group discussions; photo-elicitation interviews; and a period of observation of young people’s use of Instagram online. The data indicates that young people seek a way out from everyday lives affected by the Global Financial Crisis either by emigrating or escaping into the digital world in search of what they hope to be a better life. The thesis reflects on the online branding practices adopted by young people as they compete in this new frontier of marketised space and proposes that social media provides them with a key means by which they can construct their identities and in doing so creates an environment for profile curation. The thesis discusses the implications of the relationship between economic instability, social media, youth identities and the intersection of consumption and production of a digitally augmented brand of the self that is essentially ephemeral. It further reflects on the sociological significance of social media consumption as a performative space in which young people can assert a coherent sense of identity, while simultaneously tying them to the very society that obliges them to do so

Trading off communications bandwidth with accuracy in adaptive diffusion networks

In this paper, a novel algorithm for bandwidth reduction in adaptive distributed learning is introduced. We deal with diffusion networks, in which the nodes cooperate with each other, by exchanging information, in order to estimate an unknown parameter vector of interest. We seek for solutions in the framework of set theoretic estimation. Moreover, in order to reduce the required bandwidth by the transmitted information, which is dictated by the dimension of the unknown vector, we choose to project and work in a lower dimension Krylov subspace. This provides the benefit of trading off dimensionality with accuracy. Full convergence properties are presented, and experiments, within the system identification task, demonstrate the robustness of the algorithmic technique

Adaptive Robust Distributed Learning in Diffusion Sensor Networks

In this paper, the problem of adaptive distributed learning in diffusion networks is considered. The algorithms are developed within the convex set theoretic framework. More specifically, they are based on computationally simple geometric projections onto closed convex sets. The paper suggests a novel combine-project-adapt protocol for cooperation among the nodes of the network; such a protocol fits naturally with the philosophy that underlies the projection-based rationale. Moreover, the possibility that some of the nodes may fail is also considered and it is addressed by employing robust statistics loss functions. Such loss functions can easily be accommodated in the adopted algorithmic framework; all that is required from a loss function is convexity. Under some mild assumptions, the proposed algorithms enjoy monotonicity, asymptotic optimality, asymptotic consensus, strong convergence and linear complexity with respect to the number of unknown parameters. Finally, experiments in the context of the system-identification task verify the validity of the proposed algorithmic schemes, which are compared to other recent algorithms that have been developed for adaptive distributed learning

Trading off Complexity With Communication Costs in Distributed Adaptive Learning via Krylov Subspaces for Dimensionality Reduction

In this paper, the problemof dimensionality reduction in adaptive distributed learning is studied. We consider a network obeying the ad-hoc topology, in which the nodes sense an amount of data and cooperate with each other, by exchanging information, in order to estimate an unknown, common, parameter vector. The algorithm, to be presented here, follows the set-theoretic estimation rationale; i.e., at each time instant and at each node of the network, a closed convex set is constructed based on the received measurements, and this defines the region in which the solution is searched for. In this paper, these closed convex sets, known as property sets, take the form of hyperslabs. Moreover, in order to reduce the number of transmitted coefficients, which is dictated by the dimension of the unknown vector, we seek for possible solutions in a subspace of lower dimension; the technique will be developed around the Krylov subspace rationale. Our goal is to find a point that belongs to the intersection of this infinite number of hyperslabs and the respective Krylov subspaces. This is achieved via a sequence of projections onto the property sets and the Krylov subspaces. The case of highly correlated inputs that degrades the performance of the algorithm is also considered. This is overcome via a transformation whichwhitens the input. The proposed schemes are brought in a decentralized form by adopting the combine-adapt cooperation strategy among the nodes. Full convergence analysis is carried out and numerical tests verify the validity of the proposed schemes in different scenarios in the context of the adaptive distributed system identification task