Analysis of Cloud Based Keystroke Dynamics for Behavioral Biometrics Using Multiclass Machine Learning

With the rapid proliferation of interconnected devices and the exponential growth of data stored in the cloud, the potential attack surface for cybercriminals expands significantly. Behavioral biometrics provide an additional layer of security by enabling continuous authentication and real-time monitoring. Its continuous and dynamic nature offers enhanced security, as it analyzes an individual's unique behavioral patterns in real-time. In this study, we utilized a dataset consisting of 90 users' attempts to type the 11-character string 'Exponential' eight times. Each attempt was recorded in the cloud with timestamps for key press and release events, aligned with the initial key press. The objective was to explore the potential of keystroke dynamics for user authentication. Various features were extracted from the dataset, categorized into tiers. Tier-0 features included key-press time and key-release time, while Tier-1 derived features encompassed durations, latencies, and digraphs. Additionally, Tier-2 statistical measures such as maximum, minimum, and mean values were calculated. The performance of three popular multiclass machine learning models, namely Decision Tree, Multi-layer Perceptron, and LightGBM, was evaluated using these features. The results indicated that incorporating Tier-1 and Tier-2 features significantly improved the models' performance compared to relying solely on Tier-0 features. The inclusion of Tier-1 and Tier-2 features allows the models to capture more nuanced patterns and relationships in the keystroke data. While Decision Trees provide a baseline, Multi-layer Perceptron and LightGBM outperform them by effectively capturing complex relationships. Particularly, LightGBM excels in leveraging information from all features, resulting in the highest level of explanatory power and prediction accuracy. This highlights the importance of capturing both local and higher-level patterns in keystroke data to accurately authenticate users

Analysis of the Consumer Perceptions of Online Shopping: Case of Bangladesh

Though online shopping has become a new type of retail shopping, it has been adopted worldwide, including in Bangladesh, influencing ordinary citizens\u27 lives. In Bangladesh, consumers have not been habituated to online shopping frequently. This study aims to identify consumer perceptions of online shopping in Bangladesh. The study has 140 sample sizes from the Chuadanga district by forming a self-structured closed-ended Questionnaire. The SPSS version 16.0 statistical tool is used in this study. Several statistical tools, like frequency tests and percentages, were used to measure the objective. We found that consumers are primarily young, below 30 ages, who shop online to save time, and for available varieties of products and services and prefer to pay through cash on delivery method. Most consumers feel risk in online shopping and are also concerned about the security of the payment system. Overall, Consumers are satisfied with online shopping. Also, this study has a few limitations. Future research with a larger sample size and additional variables is recommended