Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Microstructure and mechanical properties new magnesium-zinc-gadolinium alloys(Minerals, Metals and Materials Society 184 Thorn Hill Road Warrendale PA 15086, 2016) Seetharaman, S.; Tekumalla, S.; Lalwani, B.; Patel, H.; Bau, N.Q.; Gupta, M.Magnesium based materials are effective for structural/component weight reduction in automotive applications. However, their real time applications are limited because of their inadequate mechanical properties, especially the absolute strength and creep resistance. In this regard, the formation of thermally stable ternary compounds is believed to positively influence the properties of Mg-Zn-RE alloys. In this study, new Mg alloys containing Zn and Gd (Mg-2.0Zn-0.5Gd and Mg-3.4Zn-0.8Gd, in at.%) were developed using disintegrated melt deposition technique followed by hot extrusion. The developed alloys were investigated for their microstructural and mechanical properties in hot-extruded conditions. The mechanical properties examined under indentation, tension and compression loads indicated improved mechanical performance due to Zn and Gd addition. The observed mechanical properties are presented using structure-property relationship. © © 2016 by The Minerals, Metals & Materials Society. All rights reserved.Item Remote Surveillance Robot System-A Robust Framework Using Cloud(Institute of Electrical and Electronics Engineers Inc., 2016) Sundaram, A.; Gupta, M.; Rathod, V.; Chandrasekaran, K.Today's technology provides a rather convenient way for developer community to develop an integrated network environment for the diversified applications of various robotic systems. Internet based robotic systems have been gaining importance of late and we explore one such application in the field of remote surveillance. Even though direct control has potential difficulties due to unpredictable delay, recent technological advances have reduced this to a large extent. This paper describes a direct model of robot control. The system uses standard protocol and a machine-human interface. Using a Web browser (thin client), a remote user can control the mobile robot to navigate in an enclosure with visual feedback via the Internet. The use of an intuitive user interface permits Internet users to access and control the mobile robot and perform useful tasks remotely, from a different location. The direct mode is implemented with the help of event driven methods which provide complete control over the robot. This paper proposes a model architecture for direct control and discusses an implementation and performance of a networked robot system with the help of Cloud computing. © 2015 IEEE.Item Role of Rare Earth Oxide Reinforcements in Enhancing the Mechanical, Damping and Ignition Resistance of Magnesium(Springer, 2019) Kujur, M.S.; Manakari, V.; Parande, G.; Doddamani, M.; Mallick, A.; Gupta, M.Magnesium based nanocomposites, on account of their excellent dimensional stability coupled with mechanical integrity, have provided the much-needed impetus for utilization in both aerospace-related and automobile-related applications. However, the perceived easy ignition and flammability of magnesium alloys create a detrimental safety feature that hinders the aerospace application opportunities. Incorporation of rare earth metal oxides into magnesium matrix can induce ‘reactive element effect’ (REE), due to their strong rare earth–oxygen interactions. Along with enhancing the protective characteristics of oxides on many metals and alloys, the addition of such rare earth oxides also helps in realizing a refined microstructure and good strength–ductility combination in the composites. This manuscript presents the mechanical properties, damping and ignition resistance characteristics of the new and improved composite materials engineered by reinforcing magnesium with rare earth oxide nanoparticle. Rationale for the observed properties is discussed while concurrently establishing the relationship between microstructure of the engineered composites and resultant mechanical properties. © 2019, The Minerals, Metals & Materials Society.Item Securing Database Backups Using Blockchain and Peer-to-Peer Network(Institute of Electrical and Electronics Engineers Inc., 2021) Maheshwarkar, A.; Kumar, A.; Gupta, M.Databases are one of the critical services provided by a cloud environment. Database downtime can have a devastating impact on any running service. Database backups are generally made robust with high availability by replicating them in a new server. However, storing a database backup on a new server is not cost-effective. At the same time, storing the backup in the same server does not ensure high availability. In this paper, we propose creating a secure database backup architecture using a transaction blockchain and peer-to-peer network to record the history of the transactions. Our architecture makes database backups highly available with a 4 percent decrease in throughput. © 2021 IEEE.Item Analysis of written interactions in open-source communities using RCNN(Institute of Electrical and Electronics Engineers Inc., 2021) Maheshwarkar, A.; Kumar, A.; Gupta, M.Open-source software has proved to be a key pillar in modern-day software development. The growing size of the open-source communities has significantly increased the throughput of these projects. However, larger communities tend to lead to difficulties in communication and openness for newer members. In this paper, we try to analyze the interactions on Github for some of the popular open-source projects. We have created a database of 2500 filtered comments classified into five classes of emotion. We have also proposed a novel RCNN based architecture to detect the sentiment of the comments and perform multiclass text classification. Furthermore, we have discussed possible model integrations with existing open-source platforms and the challenges associated with the implementation. © 2021 IEEE.Item Trusted Federated Learning Framework for Attack Detection in Edge Industrial Internet of Things(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, M.P.; Anand, A.; Prateek Janaswamy, L.A.; Sundarrajan, S.; Gupta, M.The edge Industrial Internet of Things (IIoT) is highly vulnerable to attacks due to the vast number of connected devices and the lack of security features. Attacks in edge IIoT can lead to significant damage, including data theft, malfunctioning, and privacy breaches. Federated Learning (FL) is a promising approach to detecting attacks by utilizing edge devices’ collective intelligence. FL allows devices to collaboratively learn from multiple devices’ data without centralized sharing, which preserves data privacy and reduces communication costs. However, FL has vulnerabilities that can compromise model accuracy, privacy, and security. Trusted FL is essential for collaboration among multiple edge IIoT devices while preserving data privacy and security. Trust plays a critical role in the success of FL, as edge IIoT devices must trust that the models are accurately learning and that their data is protected. To address this, we propose an FL framework that uses Federated Averaging (FedAvg) and Convolutional Neural Network (CNN) to detect attacks in edge IIoT. We also propose a mechanism to calculate trust for appropriate edge IIoT device selection by measuring each device’s (a.k.a client’s) performance during model training. The proposed edge IIoT device selection method, client selection, can fairly select clients for model training and improve trust in the entire system. Although the proposed FL approach does not outperform the centralized ResNet-18 CNN model on experimental analysis, improving its performance can be a promising solution for detecting attacks in edge IIoT. © 2023 IEEE.Item Early Detection and Classification of Zero-Day Attacks in Network Traffic Using Convolutional Neural Network(Springer Science and Business Media Deutschland GmbH, 2024) Singh, M.P.; Singh, V.P.; Gupta, M.In a Zero-Day cyberattack, attackers exploit a software vulnerability for which the software vendor is unaware or has not released a patch. This can make it difficult for organizations to protect their systems until a patch or mitigation is developed. To stay ahead of these evolving cyber threats, it’s critical to keep up to date with the latest threat information and to remain vigilant. Traditional methods for detecting and classifying zero-day attacks often require session-wide features, which can be challenging to implement. This paper presents a novel approach for detecting and classifying Zero-Day attacks in network traffic. Specifically, we present a framework composed of a 1D Convolutional Neural Network (1D-CNN), which involves minimal preprocessing and directly leverages raw network data as byte sequences to learn features, eliminating the need for complex feature extraction. To test the effectiveness of our proposed approach, publicly available network traffic datasets encompassing various malware families are used. Results show that the proposed approach is significantly effective in detecting and classifying Zero-Day attacks, empowering organizations to combat evolving cyber threats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.