PushID: A Pressure Control Interaction-Based Behavioral Biometric Authentication System for Smartwatches

Continuous authentication with feature-level fusion of touch gestures and keystroke dynamics to solve security and usability issues

Authentication is an important security component of almost any software application. It serves as the application’s security front door by controlling access with the goal of protecting the confidentiality and integrity of the system. However, with the large variety of software applications that an end user interacts with daily, authentication is becoming a usability issue that has the potential to weaken a system’s overall security. The increasing complexity of dealing with a variety of authentication mechanisms often causes end users to develop negative security behaviours, such as writing down passwords. Moreover, some of the currently available authentication mechanisms, such as alphanumeric passwords, raise universal access issues due to both the issue of remembering a complex sequence of characters and the difficulty some individuals may have in entering that exact sequence on a keyboard or mobile device. This article proposes an authentication approach that seeks to address these usability, universal access, and security issues.

This study has proposed and investigated a novel input variable selection method for nonlinear modelling based on principle component analysis (PCA) and cluster analysis. The proposed approach was applied to daily rainfall-runoff modelling of the Brue catchment of the United Kingdom using wavelet based hybrid forms of two nonlinear models, Artificial Neural Networks (ANNs) and Local Linear Regression (LLR), to identify meaningful wavelet decomposed sub-series. The homogenous group formation capability of cluster analysis and redundancy assessment capability of PCA were applied effectively in this study to solve input selection uncertainties associated with wavelet based hybrid models. Though this concept has been represented in the selection of effective wavelet decomposed subseries in runoff modelling, the application has gotten wider implications in time series modelling with highly redundant and large input space. The study revealed the weakness of conventional forms of cross-correlation analysis and also suggested that input selection could be improved by making sufficient natural clusters (equal to the desired number of input data series) of input space and restricting the search within each cluster according to silhouette or correlation value. The study also highlighted the higher modelling capability of ANN over traditional LLR models in rainfall-runoff modelling of the Brue catchment.

A challenging task in time series microarray data analysis is to identify co-expressed groups of genes from a large input space. The overall objective of this study is to obtain knowledge about the most important genes and clusters related to production and growth rate in a real-world microarray data analysis task. Various measures are engaged to evaluate the importance of each gene and to group genes based on their correlation with the output and each other. Some strategies for grouping and selecting genes are integrated resulting in several models tested for real biological data. All proposed models are tested on a real microarray data analysis problem and the results obtained are throughtly presented as well as interpreted from a biological perspective.

A Graphical User Interface (GUI) consists of virtual objects (widgets) that are more intuitive to use, for example buttons, edit boxes, etc. While GUIs have become ubiquitous, testing them remains largely ad- hoc. In contrast with console applications where there is only one point of interaction (the command line), GUIs provide multiple points each of which might have different states. This structure makes GUI testing especially challenging because of its large input space.

The construction of a neural network simulation metamodel requires the generation of training data; design points (inputs) and the estimate of the corresponding output generated by the simulation model. A common methodology is to focus some simulation effort in obtaining accurate estimates of the expected output values by executing several simulation replications at each point and taking the average as the estimate. However, with a limited amount of simulation effort available and a rather large input space, this approach may not produce the best expected value approximations. An alternate approach is to distribute that same simulation effort over a larger sample of input points, even if it means the resulting estimates of the expected outputs at each point will be less accurate. We will show through several examples that this approach may result in better neural network metamodels; this conclusion differs from other studies involving regression metamodels.

In this paper, we focus on the constructions of adaptively secure identity-based encryption (IBE) from lattices and verifiable random function (VRF) with large input spaces. Existing constructions of these primitives suffer from low efficiency, whereas their counterparts with weaker guarantees (IBEs with selective security and VRFs with small input spaces) are reasonably efficient. We try to fill these gaps by developing new partitioning techniques that can be performed with compact parameters and proposing new schemes based on the idea.

Cyber-Physical Systems (CPSs) have a large input space, with discrete and continuous elements across multiple layers. Hybrid Attack Graphs (HAGs) provide a flexible and efficient approach to generate attack sequences for a CPS. Analysis and testing of large-scale HAGs are prohibitively costly. To address scalability and analysis challenges of HAG generation, it is required to reduce the HAG size via sampling. Existing sampling techniques provide probabilistic sampling and do not consider the complete coverage of different types of vulnerabilities. Moreover, they do not consider the impact of successful attacks. In this work, we propose a sampling algorithm that is impact driven and coverage aware. In addition, we provide several sampling strategies for cyber-security experts to inquire about potential attacks. The experimental results performed on both synthetic and real-world graphs show that our sampling method reduces up to 50% of nodes and 86% edges while retaining all vulnerability types across different attack graph representations and graph generators.

Usability issues are vital components for online-based businesses. With Nigeria integrating electronic payment into its financial system coupled with rising internet penetration in the country, several businesses have created an online presence and are cashing in on the opportunities. This has created a form of online competition among e-commerce businesses. This study employs the user test method to test the usability issues associated with E-commerce websites in Nigeria and how this affects the success of e-commerce businesses. We find several usability issues with all e-commerce websites tested and a general need for user-focused improvement on all the websites. We also find the issues of security and trust as salient to expand the e-commerce business in Nigerians. Based on the result and analysis, recommendations on usability, data policy, security, registration and other vital issues are offered.

Non-Fungible Tokens (NFTs) have emerged as a way to collect digital art as well as an investment vehicle. Despite having been popularized only recently, NFT markets have witnessed several high-profile (and high-value) asset sales and a tremendous growth in trading volumes over the last year. Unfortunately, these marketplaces have not yet received much security scrutiny. Instead, most academic research has focused on attacks against decentralized finance (DeFi) protocols and automated techniques to detect smart-contract vulnerabilities. To the best of our knowledge, we are the first to study the market dynamics and security issues of the multi-billion dollar NFT ecosystem.

We present in this paper a new approach for Arabic font recognition. Our proposal is to use a fixed-length sliding window for the feature extraction and to model feature distributions with Gaussian Mixture Models (GMMs). This approach presents a double advantage. First, we do not need to perform a priori segmentation into characters, which is a difficult task for arabic text. Second, we use versatile and powerful GMMs able to model finely distributions of features in large multi-dimensional input spaces. We report on the evaluation of our system on the APTI (Arabic Printed Text Image) database using 10 different fonts and 10 font sizes. Considering the variability of the different font shapes and the fact that our system is independent of the font size, the obtained results are convincing and compare well with competing systems.

We study the model of projective simulation (PS) which is a novel approach to artificial intelligence (AI). Recently it wasshown that the PS agent performs well in a number of simple task environments, also when compared to standard models ofreinforcement learning (RL). In this paper we study the performance of the PS agent further in more complicated scenarios. Tothat end we chose two well-studied benchmarking problems, namely the “grid-world” and the “mountain-car” problem, whichchallenge the model with large and continuous input space. We compare the performance of the PS agent model with those ofexisting models and show that the PS agent exhibits competitive performance also in such scenarios.

This article investigates optimization of wind farms using a modifier adaptation scheme based on Gaussian processes. In this scheme measurements are used to identify plant-model mismatch using Gaussian process regression, which are then used to find the optimal plant control inputs. However, for systems with many agents and a large control input space, the identification of the input-output map of the plant is challenging. Therefore, the paper proposes a distributed learning approach, in which sub-parts of the plant are identified with individual GP regression models. Afterwards, all of these are used to build a model of the overall plant-model mismatch, which is then used in the optimization. In the wind farm case the sub-parts are the individual turbines. The distributed learning approach clearly outperforms the original central learning approach in numerical illustrations of wind farm test cases.

The striatum of the lamprey, the first vertebrate group to appear in evolution, shows striking similarities to that of mammals with respect to histochemical markers, afferent and efferent projections and the effect of dopamine depletion, which leads to hypokinetic motor symptoms. The cellular properties of lamprey striatal neurons were studied here using patch-clamp recordings in acute striatal slices. Sixty-five per cent of recorded neurons were characterised by a prominent inward rectification due to a K+ conductance of the Kir type. They had a ramping response with a long delay to the first action potential due to activation of a low-voltage-activated A-type K+ current. Many such inwardly rectifying neurons (IRNs) had a hyperpolarised resting membrane potential and some had spiny dendrites. The remaining 35% of the neurons (non-IRNs) represent a heterogeneous group, including some with characteristics similar to the fast-spiking interneuron of the mammalian striatum. They showed short-lasting, large after hyperpolarisations (AHPs) and discharged action potentials at high frequency. None of the recorded neurons were spontaneously active but they received GABAergic and glutamatergic synaptic input. The fact that most lamprey striatal neurons display inward rectification indicates that this is a conserved characteristic of striatal neurons throughout vertebrate phylogeny. This is a cellular property of critical importance for the operations of the striatum in mammals.

In our era of the service industry, information systems play a major place, even a vital position for businesses, organizations and individuals. Information systems are facing new ongoing security threats, more sophisticated and of different natures. In this context, it is important to prevent attackers from achieving their outcomes, manage the inevitable breaches, and minimize their impacts. Security practices must be conducted in an engineering framework; engineering of security has to be improved. For this, it is proposed to develop innovative and broad systemic approaches that operate together on several axes, by improving user experience. We track and solve Resilience, Security and Usability issues jointly in enterprise information systems. In this paper, we position socio-technical systems according to well-known information systems of enterprises and organizations. We treat the paradigms of socio-technical systems and we focus on the interplay between resilience, security and usability. A case study illustrates the proposed approach; it details the elaboration of design patterns for improving user experience.