VASABI: Hierarchical User Profiles for Interactive Visual User Behaviour Analytics

Integrating machine learning for sustaining cybersecurity in digital banks

Cybersecurity continues to be an important concern for financial institutions given the technology's rapid development and increasing adoption of digital services. Effective safety measures must be adopted to safeguard sensitive financial data and protect clients from potential harm due to the rise in cyber threats that target digital organizations. The aim of this study is to investigates how machine learning algorithms are integrated into cyber security measures in the context of digital banking and its benefits and drawbacks. We initially provide a general overview of digital banks and the particular security concerns that differentiate them from conventional banks. Then, we explore the value of machine learning in strengthening cybersecurity defenses. We revealed that insider threats, distributed denial of service (DDoS) assaults, ransomware, phishing attacks, and social engineering are main cyberthreats that are digital banks exposed. We identify the appropriate machine learning algorithms such as support vector machines (SVM), recurrent neural networks (RNN), hidden markov models (HMM), and local outlier factor (LOF) that are used for detection and prevention cyberthreats. In addition, we provide a model that considers ethical concerns while constructing a cybersecurity framework to address potential vulnerabilities in digital banking systems. The advantages and disadvantages of incorporating machine learning into the cybersecurity strategy of digital banks are outlined using strengths, weaknesses, opportunities, threats (SWOT) analysis. This study seeks to provide a thorough knowledge of how machine learning may strengthen cybersecurity procedures, protect digital banks, and maintain customer trust in the ecosystem of digital banking.

Survey on Visual Analysis of Event Sequence Data

Event sequence data record series of discrete events in the time order of occurrence. They are commonly observed in a variety of applications ranging from electronic health records to network logs, with the characteristics of large-scale, high-dimensional and heterogeneous. This high complexity of event sequence data makes it difficult for analysts to manually explore and find patterns, resulting in ever-increasing needs for computational and perceptual aids from visual analytics techniques to extract and communicate insights from event sequence datasets. In this paper, we review the state-of-the-art visual analytics approaches, characterize them with our proposed design space, and categorize them based on analytical tasks and applications. From our review of relevant literature, we have also identified several remaining research challenges and future research opportunities.

The Effect of Alignment on People's Ability to Judge Event Sequence Similarity

Event sequences are central to the analysis of data in domains that range from biology and health, to logfile analysis and people's everyday behavior. Many visualization tools have been created for such data, but people are error-prone when asked to judge the similarity of event sequences with basic presentation methods. This article describes an experiment that investigates whether local and global alignment techniques improve people's performance when judging sequence similarity. Participants were divided into three groups (basic versus local versus global alignment), and each participant judged the similarity of 180 sets of pseudo-randomly generated sequences. Each set comprised a target, a correct choice and a wrong choice. After training, the global alignment group was more accurate than the local alignment group (98 versus 93 percent correct), with the basic group getting 95 percent correct. Participants' response times were primarily affected by the number of event types, the similarity of sequences (measured by the Levenshtein distance) and the edit types (nine combinations of deletion, insertion and substitution). In summary, global alignment is superior and people's performance could be further improved by choosing alignment parameters that explicitly penalize sequence mismatches.

Sequen-C: A Multilevel Overview of Temporal Event Sequences

Building a visual overview of temporal event sequences with an optimal level-of-detail (i.e. simplified but informative) is an ongoing challenge - expecting the user to zoom into every important aspect of the overview can lead to missing insights. We propose a technique to build a multilevel overview of event sequences, whose granularity can be transformed across sequence clusters (vertical level-of-detail) or longitudinally (horizontal level-of-detail), using hierarchical aggregation and a novel cluster data representation Align-Score-Simplify. By default, the overview shows an optimal number of sequence clusters obtained through the average silhouette width metric - then users are able to explore alternative optimal sequence clusterings. The vertical level-of-detail of the overview changes along with the number of clusters, whilst the horizontal level-of-detail refers to the level of summarization applied to each cluster representation. The proposed technique has been implemented into a visualization system called Sequence Cluster Explorer (Sequen-C) that allows multilevel and detail-on-demand exploration through three coordinated views, and the inspection of data attributes at cluster, unique sequence, and individual sequence level. We present two case studies using real-world datasets in the healthcare domain: CUREd and MIMIC-III; which demonstrate how the technique can aid users to obtain a summary of common and deviating pathways, and explore data attributes for selected patterns.

Role-Aware Information Spread in Online Social Networks

Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.

DPVis: Visual Analytics With Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this article, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.