Machine Learning Driven Smishing Detection Framework for Mobile Security

The increasing reliance on smartphones for communication, financial transactions, and personal data management has made them prime targets for cyberattacks, particularly smishing, a sophisticated variant of phishing conducted via SMS. Despite the growing threat, traditional detection methods often struggle with the informal and evolving nature of SMS language, which includes abbreviations, slang, and short forms. This paper presents an enhanced content-based smishing detection framework that leverages advanced text normalization techniques to improve detection accuracy. By converting nonstandard text into its standardized form, the proposed model enhances the efficacy of machine learning classifiers, particularly the Naive Bayesian classifier, in distinguishing smishing messages from legitimate ones. Our experimental results, validated on a publicly available dataset, demonstrate a detection accuracy of 96.2%, with a low False Positive Rate of 3.87% and False Negative Rate of 2.85%. This approach significantly outperforms existing methodologies, providing a robust solution to the increasingly sophisticated threat of smishing in the mobile environment.

ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models

The increasing frequency and sophistication of cybersecurity vulnerabilities in software systems underscore the urgent need for robust and effective methods of vulnerability assessment. However, existing approaches often rely on highly technical and abstract frameworks, which hinders understanding and increases the likelihood of exploitation, resulting in severe cyberattacks. Given the growing adoption of Large Language Models (LLMs) across diverse domains, this paper explores their potential application in cybersecurity, specifically for enhancing the assessment of software vulnerabilities. We propose ChatNVD, an LLM-based cybersecurity vulnerability assessment tool leveraging the National Vulnerability Database (NVD) to provide context-rich insights and streamline vulnerability analysis for cybersecurity professionals, developers, and non-technical users. We develop three variants of ChatNVD, utilizing three prominent LLMs: GPT-4o mini by OpenAI, Llama 3 by Meta, and Gemini 1.5 Pro by Google. To evaluate their efficacy, we conduct a comparative analysis of these models using a comprehensive questionnaire comprising common security vulnerability questions, assessing their accuracy in identifying and analyzing software vulnerabilities. This study provides valuable insights into the potential of LLMs to address critical challenges in understanding and mitigation of software vulnerabilities.

Robust Partial Least Squares Using Low Rank and Sparse Decomposition

This paper proposes a framework for simultaneous dimensionality reduction and regression in the presence of outliers in data by applying low-rank and sparse matrix decomposition. For multivariate data corrupted with outliers, it is generally hard to estimate the true low dimensional manifold from corrupted data. The objective of the proposed framework is to find a robust estimate of the low dimensional space of data to reliably perform regression. The effectiveness of the proposed algorithm is demonstrated experimentally for simultaneous regression and dimensionality reduction in the presence of outliers in data.

"I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data

Large language models have recently attracted significant attention due to their impressive performance on a variety of tasks. ChatGPT developed by OpenAI is one such implementation of a large, pre-trained language model that has gained immense popularity among early adopters, where certain users go to the extent of characterizing it as a disruptive technology in many domains. Understanding such early adopters' sentiments is important because it can provide insights into the potential success or failure of the technology, as well as its strengths and weaknesses. In this paper, we conduct a mixed-method study using 10,732 tweets from early ChatGPT users. We first use topic modelling to identify the main topics and then perform an in-depth qualitative sentiment analysis of each topic. Our results show that the majority of the early adopters have expressed overwhelmingly positive sentiments related to topics such as Disruptions to software development, Entertainment and exercising creativity. Only a limited percentage of users expressed concerns about issues such as the potential for misuse of ChatGPT, especially regarding topics such as Impact on educational aspects. We discuss these findings by providing specific examples for each topic and then detail implications related to addressing these concerns for both researchers and users.

Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions

The Smart grid (SG), generally known as the next-generation power grid emerged as a replacement for ill-suited power systems in the 21st century. It is in-tegrated with advanced communication and computing capabilities, thus it is ex-pected to enhance the reliability and the efficiency of energy distribution with minimum effects. With the massive infrastructure it holds and the underlying communication network in the system, it introduced a large volume of data that demands various techniques for proper analysis and decision making. Big data analytics, machine learning (ML), and deep learning (DL) plays a key role when it comes to the analysis of this massive amount of data and generation of valuable insights. This paper explores and surveys the Smart grid architectural elements, machine learning, and deep learning-based applications and approaches in the context of the Smart grid. In addition in terms of machine learning-based data an-alytics, this paper highlights the limitations of the current research and highlights future directions as well.