20 citations · 54 across the 12 of their papers we have counts for
13 papers
The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data
Saroj Gopali, Sima Siami-Namini, Faranak Abri +1
As an intriguing case is the goodness of the machine and deep learning models generated by these LLMs in conducting automated scientific data analysis, where a data analyst may not…
The Accuracy of Domain Specific and Descriptive Analysis Generated by Large Language Models
Denish Omondi Otieno, Faranak Abri, Sima Siami-Namini +1
Large language models (LLMs) have attracted considerable attention as they are capable of showcasing impressive capabilities generating comparable high-quality responses to human i…
The Performance of Sequential Deep Learning Models in Detecting Phishing Websites Using Contextual Features of URLs
Saroj Gopali, Akbar S. Namin, Faranak Abri +1
Cyber attacks continue to pose significant threats to individuals and organizations, stealing sensitive data such as personally identifiable information, financial information, and…
Exploiting Large Language Models (LLMs) through Deception Techniques and Persuasion Principles
Sonali Singh, Faranak Abri, Akbar Siami Namin
With the recent advent of Large Language Models (LLMs), such as ChatGPT from OpenAI, BARD from Google, Llama2 from Meta, and Claude from Anthropic AI, gain widespread use, ensuring…
A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models
Saroj Gopali, Faranak Abri, Sima Siami-Namini +1
There exist several data-driven approaches that enable us model time series data including traditional regression-based modeling approaches (i.e., ARIMA). Recently, deep learning t…
Toward Explainable Users: Using NLP to Enable AI to Understand Users' Perceptions of Cyber Attacks
Faranak Abri, Luis Felipe Gutierrez, Chaitra T. Kulkarni +2
To understand how end-users conceptualize consequences of cyber security attacks, we performed a card sorting study, a well-known technique in Cognitive Sciences, where participant…
Phishing Detection through Email Embeddings
Luis Felipe Gutiérrez, Faranak Abri, Miriam Armstrong +2
The problem of detecting phishing emails through machine learning techniques has been discussed extensively in the literature. Conventional and state-of-the-art machine learning al…
Predicting Emotions Perceived from Sounds
Faranak Abri, Luis Felipe Gutiérrez, Akbar Siami Namin +2
Sonification is the science of communication of data and events to users through sounds. Auditory icons, earcons, and speech are the common auditory display schemes utilized in son…
Fake Reviews Detection through Analysis of Linguistic Features
Faranak Abri, Luis Felipe Gutierrez, Akbar Siami Namin +2
Online reviews play an integral part for success or failure of businesses. Prior to purchasing services or goods, customers first review the online comments submitted by previous c…
Cloud as an Attack Platform
Moitrayee Chatterjee, Prerit Datta, Faranak Abri +2
We present an exploratory study of responses from security professionals and ethical hackers in order to understand how they abuse cloud platforms for attack purposes. The par…
Fake Reviews Detection through Ensemble Learning
Luis Gutierrez-Espinoza, Faranak Abri, Akbar Siami Namin +2
Customers represent their satisfactions of consuming products by sharing their experiences through the utilization of online reviews. Several machine learning-based approaches can…
Launching Stealth Attacks using Cloud
Moitrayee Chatterjee, Prerit Datta, Faranak Abri +2
Cloud computing offers users scalable platforms and low resource cost. At the same time, the off-site location of the resources of this service model makes it more vulnerable to ce…
The Performance of Machine and Deep Learning Classifiers in Detecting Zero-Day Vulnerabilities
Faranak Abri, Sima Siami-Namini, Mahdi Adl Khanghah +2
The detection of zero-day attacks and vulnerabilities is a challenging problem. It is of utmost importance for network administrators to identify them with high accuracy. The highe…