Dr. Geeta Sandeep Nadella

Dr. Geeta Sandeep Nadella received an MS in Information Assurance from Wilmington University in 2015 and a Ph.D. in Information Technology from the University of Cumberlands in 2023. He has over twelve years of experience as a senior quality assurance consultant and over four years of experience as a seasoned Scrum Master. He is also an IEEE Computer Society Chair for the Eastern North Carolina Section and a Senior IEEE Member. He has also received the Epsilon-Pi-Tau Honorary Excellence Award from Wilmington University. With over forty certifications in Information Technology, he has extensive experience in the Financial Services and Credit Bureau Industry, Education Sector, Healthcare, Automobile, Utilities, Telecommunication, Assurance, Judicial-State, Tax, and Advisory. As a Technology evangelist and enthusiast, his research interests include but are not limited to Data Science, AI, ML, Big Data, Blockchain Technologies, Cyber Security and Human Computer Interactions.

EDUCATION

2023 Ph.D., Information Technology 05/2018 - 12/2023 GPA: 3.95 - University of Cumberlands, Williamsburg, KY
2015 MS-IT, Information Assurance 09/2013 - 05/2015 GPA: 3.89 - Wilmington University, New Castle, DE
2010 MS, Biotechnology 09 /2007 – 10/2010 - University of Salford, Manchester, UK.
2007 BSc, Biotechnology 09/2004 - 06/2007 - Andhra University, Vishakhapatnam, Andhra Pradesh, India.

RESEARCH, TEACHING, or OTHER INTERESTS

Management Information Systems, Management of Technology and Innovation, Artificial Intelligence, Human-Computer Interaction

Scopus Publications

Generative AI-Enhanced Cybersecurity Framework for Enterprise Data Privacy Management
Geeta Sandeep Nadella, Santosh Reddy Addula, Akhila Reddy Yadulla, Guna Sekhar Sajja, Mohan Meesala, Mohan Harish Maturi, Karthik Meduri, and Hari Gonaygunta
MDPI AG
This study presents a Generative AI-Enhanced Cybersecurity Framework designed to strengthen enterprise data privacy management while improving threat detection accuracy and scalability. By leveraging Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and traditional anomaly detection methods, the framework generates synthetic datasets that mimic real-world data, ensuring privacy and regulatory compliance. At its core, the anomaly detection engine integrates machine learning models, such as Random Forest and Support Vector Machines (SVMs), alongside deep learning techniques like Long Short-Term Memory (LSTM) networks, delivering robust performance across diverse domains. Experimental results demonstrate the framework’s adaptability and high performance in the financial sector (accuracy: 94%, recall: 95%), healthcare (accuracy: 96%, precision: 93%), and smart city infrastructures (accuracy: 91%, F1 score: 90%). The framework achieves a balanced trade-off between accuracy (0.96) and computational efficiency (processing time: 1.5 s per transaction), making it ideal for real-time enterprise deployments. Unlike analog systems that achieve > 0.99 accuracy at the cost of higher resource consumption and limited scalability, this framework emphasizes practical applications in diverse sectors. Additionally, it employs differential privacy, encryption, and data masking to ensure data security while addressing modern cybersecurity challenges. Future work aims to enhance real-time scalability further and explore reinforcement learning to advance proactive threat mitigation measures. This research provides a scalable, adaptive, and practical solution for enterprise-level cybersecurity and data privacy management.


Exploring Network Privacy Measures in Mobile Networks
Sanjaikanth E Vadakkethil Somanatha Pillai and Geeta Sandeep Nadella
Springer Nature Switzerland



Blockchain Fraud Detection Using Unsupervised Learning: Anomalous Transaction Patterns Detection Using K-Means Clustering
Geeta Sandeep Nadella, Karthik Meduri, Hari Gonaygunta, Snehal Satish, and Sanjaikanth E Vadakkethil Somanathan Pillai
ACM



Examining E-learning tools impact using IS-impact model: A comparative PLS-SEM and IPMA case study
Geeta Sandeep Nadella, Karthik Meduri, Snehal Satish, Mohan Harish Maturi, and Hari Gonaygunta
Elsevier BV



IoT Network Security Anomaly Detection and Classification using Deep Learning
Karthik Meduri
Science Research Society
The Internet of Things (IoT) is an expanding network of interconnected devices exposed to growing cyber security threats. Integrating AI-powered solutions presents a promising avenue for enhancing anomaly detection and classification. This study delves into developing a comprehensive methodology leveraging machine learning and deep learning techniques. Utilizing the BoTNeTIoT-L01 dataset, meticulously curated from IoT devices, the research focuses on data gathering, preprocessing, and exploratory data analysis to unearth underlying patterns and anomalies within network traffic data. Subsequently, a suite of machine learning models, including Logistic Regression, LightGBM (Light Gradient-Boosting Machine), and Decision Tree, along with a deep learning model optimized with the Adam optimizer, is employed to detect and classify anomalies effectively. The comparative analysis underscores the superior performance of advanced models such as LightGBM and Decision Tree, showcasing their efficacy in accurately identifying security threats within IoT environments. The study also addresses pertinent technical challenges, ethical considerations, and future directions, emphasizing the imperative for responsible deployment and ongoing innovation in AI-powered IoT security solutions.


Factors Influencing Trust in Cloud Adoption for Financial Services
Snehal Satish, Geeta Sandeep Nadella, Karthik Meduri, Mohan Harish Maturi, Farheen Fatima, and Hari Gonaygunta
IEEE
Cloud computing has emerged as a transformative technology in financial services, promising operational efficiency, scalability, and innovation. However, significant concerns related to security, Trust, and other challenges hinder the adoption of cloud services. This paper comprehensively analyzes these challenges, highlighting the interplay between security measures, product-related factors, and their impact on trust and cloud adoption within financial institutions. The study employs a predictive correlational quantitative research design to explore the security and trust factors influencing the adoption of cloud services. The methodology extends traditional correlation analysis by employing predictive correlation and multiple regression analysis within a Partial Least Squares Structural Equation Modeling (PLS-SEM) framework to maximize the explained variance of the dependent variable, Trust. The findings reveal that while security measures such as robust data protection, regulatory compliance, and cybersecurity protocols are crucial for mitigating risks and addressing concerns related to data breaches, privacy, and unauthorized access, they are insufficient to drive widespread adoption. Product-related factors, including operational efficiency, scalability, cost savings, and the ability to innovate and develop new products and services, are pivotal in shaping the perceived value and Trust in cloud computing. Additionally, the study identifies challenges such as multi-tenancy risks, the semantic gap in data analysis, loss of control over data, the complexity of regulatory compliance, and skill gaps in the workforce as significant barriers to cloud adoption. The study emphasizes the importance of a holistic approach that combines robust security measures with a compelling product offering tailored to the specific needs of financial institutions.


Cybersecurity Data Analytics System Success: An Exploratory Study on U.S Government Agencies
Elyson De La Cruz, Oludotun Oni, Geeta Sandeep Nadella, Hari Gonaygunta, Sai Sravan Meduri, and Anna Marie De La Cruz
IEEE
United States government organizations are compelled to comply with federal, state, and local cybersecurity regulations by law. This includes robust reactive and proactive measures to protect against the ever-increasing cybersecurity threat landscape. Given the intense government regulatory mandates, cybersecurity data analytics systems play an important role in uncovering patterns, optimizing resource allocation, and stimulating innovation. This includes deploying cybersecurity data analytics systems (CDAS) to detect and counteract malicious activities proactively. As such, there is a need to empirically examine the organizational factors that influence the success of CDAS within U.S. government agencies. This study assesses the impact of Top Management Support (TMS), Internal Processes (IP), and Learning and Growth (LAG) from a competitive advantages perspective based on the resource-based view (RBV) theory. This study gathered survey data from cybersecurity and IT professionals and analyzed the results using a second-generation multivariate analysis technique. The findings indicate that TMS, IP, and LAG significantly influence Cybersecurity Data Analytics Systems Success (CDASS). The practical implications of this study promote the need for top management support, efficient internal processes, and ongoing learning and growth initiatives to increase a CDA system’s implementation success, thereby improving the cybersecurity posture of U.S. government agencies and, in turn, improving the state of U.S. national cybersecurity preparedness.


Government Cybersecurity Data Analytics System Success: An Exploratory Study of Technology and Organization
Elyson De La Cruz, James C. Hyatt, Geeta Sandeep Nadella, Hari Gonaygunta, Sai Sravan Meduri, and Anna Marie De La Cruz
IEEE
United States Government (USG) organizations rely on data analytics systems to uncover patterns and trends, driving effective resource allocation and fostering innovation. Equally critical is deploying cybersecurity data analytics systems (CDAS) to detect and counteract malicious activities proactively. Government organizations must abide by regulatory and executive branch mandates, which are custodians of sensitive data, national security information, and critical infrastructure. Ensuring this data's integrity, confidentiality, and availability is paramount to maintaining public trust and national security. This study addresses a crucial gap by empirically investigating the technological and organizational factors that impact the success of CDAS with USG agencies. Integrating the diffusion of innovation (DOI) and resource-based view (RBV) constructs, we explore the extent to which Compatibility (COMP), Trialability (TRI), Learning and Growth (LAG), and Internal Processes (IP) predict USG CDAS implementation success. Data collected from cybersecurity and information technology (IT) professionals were rigorously analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that COMP, TRI, LAG, and IP significantly enhance system success, underscoring the critical importance of these technological and organizational considerations. Our study highlights that robust organizational support and strategic processes are paramount to fortifying cybersecurity posture and sustaining competitive advantage, ensuring US government agencies' cyber resilience and security in an increasingly threat-prone environment. This study empirically examines government organizations and captures the perceptions of the cybersecurity and IT professionals that safeguard the nations against sophisticated cyber threats, protect sensitive information, and maintain the continuity of essential services, enhancing national security and public trust.


Human-centered AI for personalized workload management: A multimodal approach to preventing employee burnout
Karthik Meduri, Geeta Sandeep Nadella, Hari Gonaygunta, Deepak Kumar, Santosh Reddy Addula, Snehal Satish, Mohan Harish Maturi, and Shafiq Ur Rehman
EnPress Publisher
This study investigates the impact of artificial intelligence (AI) integration on preventing employee burnout through a human-centered, multimodal approach. Given the increasing prevalence of AI in workplace settings, this research seeks to understand how various dimensions of AI integration—such as the intensity of integration, employee training, personalization of AI tools, and the frequency of AI feedback—affect employee burnout. A quantitative approach was employed, involving a survey of 320 participants from high-stress sectors such as healthcare and IT. The findings reveal that the benefits of AI in reducing burnout are substantial yet highly dependent on the implementation strategy. Effective AI integration that includes comprehensive training, high personalization, and regular, constructive feedback correlates with lower levels of burnout. These results suggest that the mere introduction of AI technologies is insufficient for reducing burnout; instead, a holistic strategy that includes thorough employee training, tailored personalization, and continuous feedback is crucial for leveraging AI’s potential to alleviate workplace stress. This study provides valuable insights for organizational leaders and policymakers aiming to develop informed AI deployment strategies that prioritize employee well-being.


Study on Empowering Cyber Security by Using Adaptive Machine Learning Methods
Hari Gonaygunta, Geeta Sandeep Nadella, Priyanka Pramod Pawar, and Deepak Kumar
IEEE
Machine Learning (ML) is pivotal in enhancing cybersecurity solutions, surpassing rule-based methods. The complexity of modern malware demands robust detection systems. Traditional signature-based approaches struggle with zero-day and polymorphic threats. Our study presents a versatile ML-based approach adept at identifying active malware and thwarting phishing attempts. We efficiently evaluate web page features to detect malware and phishing attacks by employing ML algorithms in a hierarchical feed-forwarding framework. This approach involves constructing a multilayer model, utilizing ensemble learning techniques in the third layer. Comparative analysis reveals Ensemble Voting (EV) as superior, consistently outperforming Random-Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms with an accuracy of 96.16% and a low false-positive rate of 2.6%. Such systems are essential for industrial-level incident detection and security analyst training, emphasizing the indispensable role of ML in constructing dependable cybersecurity infrastructures capable of mitigating evolving cyber threats effectively.


Enhancing Cybersecurity: The Development of a Flexible Deep Learning Model for Enhanced Anomaly Detection
Hari Gonaygunta, Geeta Sandeep Nadella, Priyanka Pramod Pawar, and Deepak Kumar
IEEE
Using expert systems and relevant machine learning methods, automating network intrusion detection has become commonplace. However, the interconnectedness of many industrial control systems and the Internet of Things (IoT) has made cyber attacks on critical infrastructure communication networks a significant concern. These Critical Cyber-Physical Systems (CPSs) experience massive network traffic, posing a challenge for conventional machine-learning techniques to identify anomalies. This paper introduces a novel approach that overcomes these limitations, leveraging the power of deep learning to detect and classify anomalies with remarkable accuracy. By utilizing deep models like Deep Neural Network (DNN) and Deep Short-Term Memory (LSTM) in a two-stage procedure, we significantly enhance the capabilities of our proposed methodology. We also employ a Deep Sparse Autoencoder (DSAE) to resolve the feature engineering problem and prepare data for processing. The effectiveness of this strategy is evaluated using datasets collected from the IoT ecosystem, specifically IoT-23 and LITNET-2020. The evaluation results for the proposed method are presented and discussed, comparing its statistical significance with the most cutting-edge techniques for detecting network anomalies.


Examining the Indirect Impact of Information and System Quality on the Overall Educators' Use of E- Learning Tools: A PLS-SEM Analysis
Geeta Sandeep Nadella and Sanjaikanth E Vadakkethil Somanathan Pillai
IEEE
This study investigates the indirect impact of Information and System Quality, specifically in the context of e-learning tools, and its influence on the overall impact of educators' utilization of these tools in U.S. high schools. Employing Gable's IS-Impact Measurement Model, this study conceptualizes Quality factors as first-order constructs and Impact factors as second- order constructs, providing a robust theoretical foundation for analysis. To meticulously validate these indirect effects, this research employs Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4. A comprehensive dataset comprising responses from 237 participants, encompassing educators, administrators, and IT personnel from diverse U.S. high schools, is collected through rigorously designed online anonymous surveys. Thorough assessments of measurement and structural models are conducted to ensure the highest standards of reliability and validity. The results of this study unveil compelling insights into the indirect relationship between Information and System Quality and the overall impact on educators' use of e-Iearning tools. Specifically, Information Quality and System Quality demonstrate significant and positive indirect impacts on the ‘Overall Impact’ experienced by educators in U.S. high schools. The analysis unveils statistical significance in specific indirect effects and associated pathways, reinforcing the pivotal role of IS quality in shaping educational outcomes. These findings offer actionable guidance for educational policy and practice, striving to align IS tools more effectively with the core objectives of educational institutions. Furthermore, this research sets the stage for future investigations to assess the indirect effects and impacts using the IS-Impact model across diverse domains and stakeholder groups.

RECENT SCHOLAR PUBLICATIONS

Utilizing logistic regression in machine learning for categorizing social media advertisement
H Gonaygunta, GS Nadella, K Meduri
Indonesian Journal of Electrical Engineering and Computer Science 37 (3 2025



Generative AI-Enhanced Cybersecurity Framework for Enterprise Data Privacy Management
GS Nadella, SR Addula, AR Yadulla, GS Sajja, M Meesala, MH Maturi, ...
Computers 14 (2), 55 2025



Societal Impact and Governance: Shaping the Future of AI Ethics
GS Nadella, SS Meduri, MH Maturi, P Whig
Ethical Dimensions of AI Development, 261-282 2025



Privacy and Security: Safeguarding Personal Data in the AI Era
GS Nadella, H Gonaygunta, M Harish, P Whig
Ethical Dimensions of AI Development, 157-174 2025



IoT Network Security Anomaly Detection and Classification using Deep Learning
K Meduri, S Brown, GS Nadella, H Gonaygunta, S Satish, MH Maturi
Journal of Information Systems Engineering and Management 10 (6S), 181-191 2024



Factors Influencing Trust in Cloud Adoption for Financial Services
S Satish, GS Nadella, K Meduri, MH Maturi, F Fatima, H Gonaygunta
2024 International Conference on Information Technology and Computing 2024



Cybersecurity Data Analytics System Success: An Exploratory Study on U.S Government Agencies
E De La Cruz, O Oni, GS Nadella, H Gonaygunta, SS Meduri, ...
2024 International Seminar on Application for Technology of Information and 2024



ISNCC 2024 Authors Index
A Khan, A Falk, AH Ali, A Moubayed, A Ouda, A Alshaibani, A Dutta, ...
2024 International Symposium on Networks, Computers and Communications (ISNCC) 2024



Government Cybersecurity Data Analytics System Success: An Exploratory Study of Technology and Organization
E De La Cruz, JC Hyatt, GS Nadella, H Gonaygunta, SS Meduri, ...
2024 International Symposium on Networks, Computers and Communications 2024



Leveraging Federated Learning for Privacy-Preserving Analysis of Multi-Institutional Electronic Health Records in Rare Disease Research
K Meduri, GS Nadella, AR Yadulla, VK Kasula, MH Maturi, S Brown, ...
Journal of Economy and Technology 2024



AI-Based Predictive Security System for Early Threat Detection and Anomaly Identification in Cyber Networks (AI-PSS-ETD-AICN)
SS Balantrapu, K Meduri, GS Nadella, H Gonaygunta, MH Maturi, ...
IN Patent App. 202,411,083,928 2024



AI-Powered Image Processing System for Real-Time Threat Detection and Security Enhancement (AI-IPTD-SE)
SS Balantrapu, K Meduri, GS Nadella, H Gonaygunta, MH Maturi, ...
IN Patent App. 202,411,083,727 2024



Mental Health Monitoring And Intervention Using Unsupervised Deep Learning On EEG Data
AR Yadulla, GS Sajja, SR Addula, MH Maturi, GS Nadella, E De La Cruz, ...
2024



SecureDigiTrans: Method and System for secured digital transformation leveraging advanced encryption and blockchain technologies
P Whig, K Meduri, GS Nadella, AR Yadulla, H Gonaygunta, MH Maturi, ...
IN Patent App. 202,411,067,121 2024



Exploring Network Privacy Measures in Mobile Networks
SEVS Pillai, GS Nadella
Sustainable Development through Machine Learning, AI and IoT: Second 2024



Evaluating behavioral intention and financial stability in cryptocurrency exchange app: Analyzing system quality, perceived trust, and digital currency
AR Yadulla, GS Nadella, MH Maturi, H Gonaygunta
Journal of Digital Market and Digital Currency 1 (2), 103-124 2024



Volatility Comparison of Dogecoin and Solana Using Historical Price Data Analysis for Enhanced Investment Strategies
AR Yadulla, MH Maturi, GS Nadella, S Satish
Journal of Current Research in Blockchain 1 (2), 91-111 2024



Sales Trends and Price Determinants in the Virtual Property Market: Insights from Blockchain-Based Platforms
AR Yadulla, MH Maturi, K Meduri, GS Nadella
International Journal Research on Metaverse 1 (2), 113-126 2024



Examining E-learning tools impact using IS-impact model: A comparative PLS-SEM and IPMA case study
GS Nadella, K Meduri, S Satish, MH Maturi, H Gonaygunta
Journal of Open Innovation: Technology, Market, and Complexity 10 (3), 1-10 2024



Resilience and Risk Management in Cybersecurity: A Grounded Theory Study of Emotional, Psychological, and Organizational Dynamics
F Fatima, JC Hyatt, SU Rehman, E De La Cruz, GS Nadella, K Meduri
Journal of Economy and Technology 2, 247-257 2024

MOST CITED SCHOLAR PUBLICATIONS

Enhancing cybersecurity: The development of a flexible deep learning model for enhanced anomaly detection
H Gonaygunta, GS Nadella, PP Pawar, D Kumar
2024 Systems and Information Engineering Design Symposium (SIEDS), 79-84 2024
Citations: 51



Developing a Fog Computing-based AI Framework for Real-time Traffic Management and Optimization
K Meduri, GS Nadella, H Gonaygunta, SS Meduri
International Journal of Sustainable Development in Computing Science 5 (4 2023
Citations: 47



A systematic literature review of advancements, challenges and future directions of AI and ML in healthcare
GS Nadella, S Satish, K Meduri, SS Meduri
International Journal of Machine Learning for Sustainable Development 5 (3 2023
Citations: 44



Quantum machine learning: exploring quantum algorithms for enhancing deep learning models
H Gonaygunta, MH Maturi, GS Nadella, K Meduri, S Satish
International Journal of Advanced Engineering Research and Science 11 (05) 2024
Citations: 42



Enhancing Cybersecurity with Artificial Intelligence: Predictive Techniques and Challenges in the Age of IoT
K Meduri, GS Nadella, H Gonaygunta
International Journal of Science and Engineering Applications 13 (4), 30-33 2024
Citations: 32



Adversarial attacks on deep neural network: developing robust models against evasion technique
GS Nadella, H Gonaygunta, K Meduri, S Satish
Transactions on Latest Trends in Artificial Intelligence 4 (4), 2519-1168.2023 2023
Citations: 31



AI-Driven Business Analytics Framework for Data Integration Across Hybrid Cloud Systems
V Raghunath, M Kunkulagunta, GS Nadella
Transactions on Latest Trends in Artificial Intelligence 4 (4) 2023
Citations: 31



Fault Diagnosis and Prognosis using IoT in Industry 5.0
MH Maturi, H Gonaygunta, GS Nadella, K Meduri
International Numeric Journal of Machine Learning and Robots 7 (7), 1-21 2023
Citations: 31



The impact of virtual reality on social interaction and relationship via statistical analysis
H Gonaygunta, SS Meduri, S Podicheti, GS Nadella
International Journal of Machine Learning for Sustainable Development 5 (2 2023
Citations: 30



Enhancing Data Integration Using AI and ML Techniques for Real-Time Analytics
V Raghunath, M Kunkulagunta, GS Nadella
International Journal of Machine Learning for Sustainable Development 5 (3) 2023
Citations: 30



Integrating AI and Cloud Computing for Scalable Business Analytics in Enterprise Systems
V Raghunath, M Kunkulagunta, GS Nadella
International Journal of Sustainable Development in Computing Science 5 (3) 2023
Citations: 30



Adaptive intelligence: GPT-powered language models for dynamic responses to emerging healthcare challenges
K Meduri, H Gonaygunta, GS Nadella, PP Pawar, D & Kumar
International Journal of Advanced Research in Computer and Communication 2024
Citations: 28



Understanding the role of social influence on consumer trust in adopting AI tools
GS Nadella, SS Meduri, H Gonaygunta, S Podicheti
International Journal of Sustainable Development in Computing Science 5 (2 2023
Citations: 28



Exploring the impact of AI-driven solutions on cybersecurity adoption in small and medium enterprises
GS Nadella, H Gonaygunta, D Kumar, PP Pawar
World Journal of Advanced Research and Reviews 22 (1), 1190-1197 2024
Citations: 24



Advancing Edge Computing with Federated Deep Learning: Strategies and Challenges
GS Nadella, K Meduri, H Gonaygunta, SR Addula, S Satish, M Harish, ...
International Journal for Research in Applied Science and Engineering 2024
Citations: 23



Artificial Intelligence in Business Analytics: Cloud-Based Strategies for Data Processing and Integration
V Raghunath, M Kunkulagunta, GS Nadella
International Journal of Sustainable Development in Computing Science 2 (4) 2020
Citations: 23



Examining E-learning tools impact using IS-impact model: A comparative PLS-SEM and IPMA case study
GS Nadella, K Meduri, S Satish, MH Maturi, H Gonaygunta
Journal of Open Innovation: Technology, Market, and Complexity 10 (3), 1-10 2024
Citations: 21



Study on empowering cyber security by using Adaptive Machine Learning Methods
H Gonaygunta, GS Nadella, PP Pawar, D Kumar
2024 Systems and Information Engineering Design Symposium (SIEDS), 166-171 2024
Citations: 21



Machine Learning Models for Optimizing SAP-Based Data Processing in Cloud Environments
V Raghunath, M Kunkulagunta, GS Nadella
International Journal of Sustainable Development in Computing Science 3 (3) 2021
Citations: 21



Machine Learning in SAP Workflows: A Study of Predictive Analytics and Automation
V Raghunath, M Kunkulagunta, GS Nadella
Transactions on Latest Trends in Artificial Intelligence 2 (2) 2021
Citations: 21