CYBERSECURITY AWARENESS AMONG STUDENTS: A COMPARATIVE REVIEW OF RECENT STUDIES

Grace Llego ¹, Jim Alves-Foss²¹ Center for Secure and Dependable Systems, University of Idaho. Moscow, ID USA,

ABSTRACT

Since about 2010, there has been increased attention in gauging and improving digital citizenship across the world. One important aspect of digital citizenship is cybersecurity awareness. This paper reports on a review of 35 studies that have been conducted in this area, primarily consisting of surveys of students. The primary purpose of this review was to compare and evaluate the methods used in these studies to provide guidance towards future studies. The studies surveyed from 32 to over 3000 participants evaluated cybersecurity awareness and/or practice, with about half of them providing copies of the survey questions to allow for follow-on comparative studies.

Keywords

Cyber security awareness, Surveys. Digital Literacy

AN INNOVATIVE ANALYSIS OF PARTIAL PENALTY ON IMBALANCED DATA IN CREDIT CARD FRAUD PREDICTION

Grace Llego ¹, Jiawei Zhang² Xin Zhang³ Xinyin Mia¹ Senior Investment Analyst, PRA Group (Nasdaq: PRAA), Norfolk, Virginia, USA ² Data Scientist, PRA Group (Nasdaq: PRAA), Norfolk, Virginia, USA³ Senior Data Analyst, American Airlines Group Inc (Nasdaq: AAL), Dallas, Texas,USA

ABSTRACT

This paper provides an innovative methodology of partial penalty on machine learning models to handle the data imbalance scenario occurring in credit card fraud detection implementation. Unlike the normal over-sampling or under-sampling methodologies, partial penalty directs the machine learning model to focus on learning the minor class of target variable even when the class distribution is extremely imbalanced. Besides comparing the partial penalty approach with over-sampling and under-sampling approaches to handle data imbalance scenario, we’ve implemented this new approach under five machine learning classification models, including Logistic Regression, Random Forest, kNN, Decision Tree, and Light Gradient Boosting Model. The new partial penalty approach realizes a performance of 88.35% F1 score and 98.79% AUC score with Light GBM, higher than either over-sampling or under-sampling approaches.

Keywords

Partial Penalty, Gradient Boosting, Data Imbalance, Credit Card Fraud Detection, SMOTE

A MIXED-PRECISION RISC-V PIPELINE WITH HARDWARE-MANAGED PRECISION SELECTION FOR MATRIX MULTIPLICATION

Ashwin Geeni ¹, Independent Researcher, USA

ABSTRACT

AI and deep learning workloads rely heavily on matrix multiplication, which demands both high speed and numerical accuracy. Traditional processors use fixed arithmetic precision, forcing a global trade-off between performance and accuracy. This paper presents the Mixed Precision Pipeline (MPP), a five-stage RISC-V processor pipeline that dynamically switches between INT8, FP16, and FP32 precisions based on runtime numerical stability analysis..

Keywords

Mixed-precision computing, RISC-V, Matrix multiplication, Hardware acceleration, Numerical stability