Badugu Samatha

Koneru Lakshmaiah Education Foundation

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Nagarjuna Karyemsetty

Koneru Lakshmaiah Education Foundation

Thalakola Syamsundararao

KPR Institute of Engineering and Technology

K. Raja Kumar

Andhra University

Dasari Koteswara Rao

Guntur Medical College

K. R. P. Singh

Pera (United Kingdom)

D. Yuvaraj

Cihan University-Erbil

Mahanty Rashmita

Vignan's Foundation for Science, Technology & Research

Doddi Suresh Kumar

Koneru Lakshmaiah Education Foundation

Syed Azahad

Methodist University

Moni Sushma Deep Kavila

Cooperative Institutions

Koneru Lakshmaiah Education Foundation

Saveetha University

International Institute of Information Technology

GITAM University

Indian Institute of Management Visakhapatnam

Vignan's Foundation for Science, Technology & Research

Dr. M.G.R. Educational and Research Institute

Andhra University

Guntur Medical College

SRM Institute of Science and Technology

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Cybersecurity Fortification in Edge Computing through the Synergy of Deep Learning

Nagarjuna Karyemsetty Patibandla Bharath Narasimha Muppalla Poorna Tejaswi Vaddi Naga Sivaji Challa Leela Venkata Kamal

This research study explores the vital realm of cybersecurity fortification in edge computing environments, focusing on the synergy of deep learning, specifically CNNs, for intrusion detection and threat mitigation. With edge computing's proliferation, the study addresses the challenges posed by resource constraints and real-time processing requirements, emphasizing the need for adaptive security measures. The research thoroughly investigates the application of CNNs for intrusion detection, encompassing system design and empirical evaluations, while also exploring their potential in automating threat mitigation responses. The recognized results demonstrate significant advancements in edge cybersecurity through CNN integration, paving the way for proactive and adaptive security measures in dynamic edge computing environments. Practical considerations are discussed, underscoring the significant advancements achieved in enhancing edge cybersecurity through CNN integration, promising proactive and adaptive security measures suitable for dynamic edge computing scenarios. Future extensions of this work involve advanced adversarial defense mechanisms, real-time threat intelligence integration, explainable deep learning models, and the exploration of federated learning for distributed intrusion detection, all contributing to a more secure and resilient future in edge computing. This research contributes a robust foundation for safer and more resilient edge deployments across diverse domains.

10.1109/i-smac58438.2023.10290400

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Citations (3)

A Low cost prototype for Vehicle to Infrastructure communication

International Journal Of Engineering And Computer Science (2016)

Badugu Samatha

10.18535/ijecs/v5i12.54

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IoT-Enabled Potato Diseases Prediction using Deep Learning

Badugu Samatha G Ravi Kumar Palthiya Anantha Rao Dasari Koteswara Rao Thalakola Syamsundararao

Inspection methods that are both quick and non-destructive, such as those enabled by deep learning and image processing, could be extremely beneficial for monitoring the quality of potatoes and other agricultural products. This research work proposes a Deep Neural Network (DNN) model to visually classify the diseased potatoes. A set of potato images are given as input to the DNN model during the training and testing phase. Further, a Modified Support Vector Machine (MSVM) and a Convolutional Neural Network (CNN) models are used to perform segmentation and feature extraction on the potato images. The proposed model was tested using new modern versions of standard datasets maintained by Canada and the United States agricultural departments. The results of this experiment demonstrated that deep learning model has outperformed both traditional approaches and the Machine Learning models. Accuracy rates of over 99% have been reported for some types of images. While some other types of images have reported 97% accuracy.

Feature Engineering

10.1109/icidca56705.2023.10099611

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Performance Analysis of Vehicular Network Scenarios Using SUMO and NS2 Simulators

Lecture notes on data engineering and communications technologies (2021)

K. Raja Kumar Nagarjuna Karyemsetty Badugu Samatha

10.1007/978-981-16-0081-4_33

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Citations (6)