Despite the proven efficacy of large language models (LLMs) like GPT in numerous applications, concerns have emerged regarding their exploitation in creating phishing emails or network intrusions, which have shown to be detrimental. The multimodal functionalities of large vision-language models (LVLMs) enable them to grasp visual commonsense knowledge. This study investigates the feasibility of using two widely available commercial LVLMs, LLAVA, and multimodal GPT4, for effectively bypassing CAPTCHAs or producing bot-driven fraud through malicious prompts. It was found that these LVLMs can interpret and respond to the visual information presented in image, puzzle, and text-based CAPTCHA and reCAPTCHA, thereby potentially circumventing the challenge-response authentication security measure. This capability suggests that such systems could facilitate unauthorized access to secured accounts via remote digital methods. Remarkably, these attacks can be executed with the standard, unaltered versions of the LVLMs, eliminating the need for previous adversarial methods like jailbreaking.

The COVID-19 pandemic has presented an unprecedented challenge to the education system, necessitating data-driven strategies to mitigate its impact on students and staff. This research paper introduces a novel machine-learning approach for forecasting COVID-19 hotspots in K-12 schools across Florida. Our study leverages comprehensive datasets encompassing epidemiological, environmental, demographic, and school-specific factors. This research paper showcases a machine-learning approach for forecasting COVID-19 hotspots in Florida’s K-12 schools. By harnessing the power of data and predictive analytics, our approach in this paper empowers education stakeholders to proactively manage and mitigate the pandemic’s impact. Our preliminary results are promising. The four machine learning models (Logistic Regression, Support Vector Machine, Random Forest, XGBOOST) have demonstrated their ability to identify potential hotspots and provide valuable lead time for proactive interventions. This research represents a critical step in enhancing the safety of Florida’s public schools during the ongoing pandemic. This research contributes to machine learning and public health and is a vital tool in the ongoing battle against COVID-19 in educational settings.

YouTube thumbnails play a vital role as visual indicators, succinctly capturing the essence of a video alongside its title and description. Beyond mere previews, these thumbnails have evolved into significant digital artifacts with implications for disk image encryption. This research delves into potentially integrating the advanced YOLOv4 (You Only Look Once) algorithm into creating YouTube thumbnails. YOLOv4 enhances the process by automatically identifying and emphasizing objects of interest in these visual previews. This paper diversifies the dataset to improve the model’s effectiveness, expanding its capacity to recognize and highlight objects more effectively. We address data security challenges by broadening the training data, incorporating authentication, and decrypting the dataset to align it with real-world thumbnail images. The primary objective is to assess the efficacy of YOLOv4 object detection models in authenticated YouTube thumbnail videos. The network underwent training to recognize 80 object classes, achieving a 90% prediction rate and a 92% confidence rate.

With the increasing demand for digital privacy, content-hiding (or vault) apps are becoming popular among mobile phone users. Content-hiding apps affiliate to decoy apps. They are used for hiding photos, text, or videos and appear to have an interface very similar to commonly-used utility/productivity/gaming applications (for example, a calculator user interface). While these kinds of applications are convenient for people and let them hide private data, it raises concerns among app security researchers about their presence in legit and illicit app markets. It can also set a barrier for digital investigators, practitioners, victim service agencies, and the intelligence community since these apps are known to encrypt/delete data and make it unrecoverable. Such data could be anything ranging from contraband to classified data. Our research focuses on developing a fully automated Android Vault app Identification and Extraction system, primarily from the Google Play store. Through the feature extractions from description and images of applications followed by various machine learning and deep learning models, the system successfully identifies the content-hiding applications. The system can also automatically extract the user data from vault applications running on Android phones. To facilitate the advancement of research, we also keep an inventory of vault apps found in the Google Play store and offer to trace such apps even if they get removed from the Google Play store for security/other reasons. Our methodology and findings can be further extended to detect and classify content-hiding and anti-forensic apps in any Android app market and not limited to the Google Play store.

Inspection of Anti-COVID vaccination tweets can be useful for many such analyses, and extraction of relevant information about opinion expressed on Twitter. This study proposes an analytical framework for analyzing tweets (COVID Vaccine, especially the Anti- COVID Vaccine) to identify and categorize fine-grained details about the COVID19 disaster such as affected individuals, public feelings towards the vaccine and reopening of business, polarity of public opinions on the vaccine and services provided, discussed topic changing over temporal dimension, and different clustering algorithms. In this project, we have analyzed COVID -Vaccine related tweets and Anti-Vaccine tweets, performed sentiment analysis and Topic modeling, and compared various models’ behavior based on different configuration and training datasets. The result of this work will help policy makers and data scientists to identify the best approach for twitter sentiment analysis and topic modeling as well as providing feedback on people attitude and opinion on COVID-19 vaccine.

A framework for IOT data acquisition and analysis is proposed. This framework can help data collection from a variety of different IoT devices. More precisely, our objective is to construct a practical methodology to facilitate forensic collection of forensic data from a multitude of IoT devices and related sites, such as smart phone, hubs, and cloud. Then we will use timeline analysis to present all related data in digital admissibility. In this paper, we will be focusing on building a framework of data acquisitions and forensic analysis for IoT devices being connected to mobile device and cloud.