10 citations · 12 across the 6 of their papers we have counts for
7 papers
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…
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…
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 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…
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 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…