96 citations · 259 across the 31 of their papers we have counts for
35 papers
Mitigating The Effect of Class Imbalance in Data with Hierarchical and Dependable Structure
Bipin Chhetri, Deepika Giri, Avishek Kadel +2
Classifying cybersecurity vulnerabilities using the Common Weakness Enumeration (CWE) taxonomy is challenging due to extreme class imbalance and strong hierarchical dependencies am…
Neuro-Agentic Control: A Deep Learning-based LLM-Powered Agentic AI Framework for Controlling Security Controls
Saroj Gopali, Bipin Chhetri, Deepika Giri +2
Cyberattacks on operational technology are increasingly causing costly downtime and physical damage, exposing the limitations of traditional rule-based monitoring in industrial IoT…
In-Context and Few-Shots Learning for Forecasting Time Series Data based on Large Language Models
Saroj Gopali, Bipin Chhetri, Deepika Giri +2
Existing data-driven approaches in modeling and predicting time series data include ARIMA (Autoregressive Integrated Moving Average), Transformer-based models, LSTM (Long Short-Ter…
The Application of Transformer-Based Models for Predicting Consequences of Cyber Attacks
Bipin Chhetri, Akbar Siami Namin
Cyberattacks are increasing, and securing against such threats is costing industries billions of dollars annually. Threat Modeling, that is, comprehending the consequences of these…
IoT Malware Network Traffic Detection using Deep Learning and GraphSAGE Models
Nikesh Prajapati, Bimal Karki, Saroj Gopali +1
This paper intends to detect IoT malicious attacks through deep learning models and demonstrates a comprehensive evaluation of the deep learning and graph-based models regarding ma…
Evolutionary Defense: Advancing Moving Target Strategies with Bio-Inspired Reinforcement Learning to Secure Misconfigured Software Applications
Niloofar Heidarikohol, Shuvalaxmi Dass, Akbar Siami Namin
Improper configurations in software systems often create vulnerabilities, leaving them open to exploitation. Static architectures exacerbate this issue by allowing misconfiguration…
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 Survey on Blockchain-Based Federated Learning and Data Privacy
Bipin Chhetri, Saroj Gopali, Rukayat Olapojoye +2
Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate by leveraging local computational power and the models transmission. Thi…
A Survey on the Applications of Blockchains in Security of IoT Systems
Zulfiqar Ali Khan, Akbar Siami Namin
The Internet of Things (IoT) has already changed our daily lives by integrating smart devices together towards delivering high quality services to its clients. These devices when i…
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…
Attack Prediction using Hidden Markov Model
Shuvalaxmi Dass, Prerit Datta, Akbar Siami Namin
It is important to predict any adversarial attacks and their types to enable effective defense systems. Often it is hard to label such activities as malicious ones without adequate…
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…
A Survey on Vulnerabilities of Ethereum Smart Contracts
Zulfiqar Ali Khan, Akbar Siami Namin
Smart contract (SC) is an extension of BlockChain technology. Ethereum BlockChain was the first to incorporate SC and thus started a new era of crypto-currencies and electronic tra…
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…
A Concern Analysis of FOMC Statements Comparing The Great Recession and The COVID-19 Pandemic
Luis Felipe Gutiérrez, Sima Siami-Namini, Neda Tavakoli +1
It is important and informative to compare and contrast major economic crises in order to confront novel and unknown cases such as the COVID-19 pandemic. The 2006 Great Recession a…
Cyber-Attack Consequence Prediction
Prerit Datta, Natalie Lodinger, Akbar Siami Namin +1
Cyber-physical systems posit a complex number of security challenges due to interconnection of heterogeneous devices having limited processing, communication, and power capabilitie…
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…
Fast Fourier Transformation for Optimizing Convolutional Neural Networks in Object Recognition
Varsha Nair, Moitrayee Chatterjee, Neda Tavakoli +2
This paper proposes to use Fast Fourier Transformation-based U-Net (a refined fully convolutional networks) and perform image convolution in neural networks. Leveraging the Fast Fo…
Vulnerability Coverage as an Adequacy Testing Criterion
Shuvalaxmi Dass, Akbar Siami Namin
Mainstream software applications and tools are the configurable platforms with an enormous number of parameters along with their values. Certain settings and possible interactions…
Detection of Coincidentally Correct Test Cases through Random Forests
Shuvalaxmi Dass, Xiaozhen Xue, Akbar Siami Namin
The performance of coverage-based fault localization greatly depends on the quality of test cases being executed. These test cases execute some lines of the given program and deter…