Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Trusted path between two entities in Cloud(Institute of Electrical and Electronics Engineers Inc., 2016) Divakarla, U.; Chandrasekaran, K.Trust plays an important role in the security of resources of the emerging technology called Cloud. To develop a strong trust there has to be a strong trust path between two entities. In cloud, trust plays a major role as the basic services between the user and service provider is due to the trust. Our paper proposes scheme to build a strong trust path between two important entities in cloud namely user and resources of the service provider. The performance analysis proves that the proposed model platform independent and also much stronger in case of migration and more efficient in terms of computation time. © 2016 IEEE.Item A novel approach for evaluating trust of resources in cloud environment(Institute of Electrical and Electronics Engineers Inc., 2017) Divakarla, U.; Chandrasekaran, K.Trust is a significant facet in decision making of any distributed network. Cloud computing is a new computing model that provides computing resources to consumers. Due to outsourcing. there is always an uncertainty about the reliability and quality of the services. Though service providers assure of quality and secure services, these assurances do not satisfy the trustworthiness of the service providers for the consumers. In this paper, we have proposed a model that develops a strong trust relationship between consumer and resources of the service provider in cloud environment. This trust relation strengthens the security of the resources as well as the authentication of the consumer. The implementation proved that trust model developed is more efficient in terms of compute time and process time. © 2016 IEEE.Item Workload classification in multi-vm cloud environment using deep neural network model(Association for Computing Machinery, 2021) Bhagtya, P.; Raghavan, S.; Chandrasekaran, K.; Divakarla, U.In this competitive world, everyone needs to be prepared for future risks and emergency conditions. In a multi-cloud environment users can easily shift from one cloud to another cloud because of the available data and application transfer technologies. Therefore a strong forecast system is mandatory for such conditions and to stop user migration to other clouds. Virtual Machine (VM) plays an important role in effective resource management and cost reduction in cloud infrastructure. Workload prediction in multi-VM is very useful to handle uncertain situations. In this paper, we propose a promising workload prediction technique that can handle the workload from multiple virtual machines. It has a pre-processing and feature selection engine that handles direct data from these virtual machines and the model is strong enough in classifying data based on historical workloads. This classification enables extra knowledge for the cloud vendor to optimize resource usage. This strategy can be used for producing an alarm whenever there is continuously high utilization of resources in the future. Here, our prediction methodology is experimented with a popular real-world Grid Workload Archive (GWA) dataset and it achieves more than 85% prediction accuracy for CPU, Memory and Disk Utilization. © 2021 Owner/Author.Item A Machine Learning Approach for Daily Temperature Prediction Using Big Data(Springer Science and Business Media Deutschland GmbH, 2022) Divakarla, U.; Chandrasekaran, K.; Hemant Kumar Reddy, K.H.K.; Reddy, R.V.; Rao, M.Due to global warming, weather forecasting becomes complex problem which is affected by a lot of factors like temperature, wind speed, humidity, year, month, day, etc. weather prediction depends on historical data and computational power to analyze. Weather prediction helps us in many ways like in astronomy, agriculture, predicting tsunamis, drought, etc. this helps us to be prepared in advance for any kinds disasters. With rapid development in computational power of high end machines and availability of enormous data weather prediction becomes more and more popular. But handling such huge data becomes an issue for real time prediction. In this paper, we introduced the machine learning-based prediction approach in Hadoop clusters. The extensive use of map-reduce function helps us distribute the big data into different clusters as it is designed to scale up from single servers to thousands of machines, each offering local computation and storage. An ensemble distributed machine learning algorithms are employed to predict the daily temperature. The experimental results of proposed model outperform than the techniques available in literature. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item A Novel Approach towards Windows Malware Detection System Using Deep Neural Networks(Elsevier B.V., 2022) Divakarla, U.; Reddy, K.H.K.; Chandrasekaran, K.Now-a-day's malicious software is increasing in numbers and at present becomes more harmful for any digital equipment like mobile, tablet, and computers. Traditional techniques like static and dynamic analysis, signature-based detection methods are become absolute and not effective at all. The advanced techniques like code encryption and code packing techniques can be used to hide detection; polymorphic malware is a new class of malware that changes their code structure from time to time to avoid detection, so there is a need for an intelligent system which can efficiently analyze the features of a new, unknown executable file and classify it correctly. There have been learning-based malware detection systems proposed in the literature, but most of those proposed approaches present a high accuracy over a small dataset, whereas the performance is very poor over industry-standard datasets. Operating system like windows is always in prime malware target because of the sheer high number of users. This paper proposes a simple, deep learning-based detection approachthat classifies a specified executable into benign or harmful. It has been trained using EMBER, an industry-level Windows malware dataset and tests with an accuracy of 87.76%. © 2023 The Authors. Published by Elsevier B.V.Item Comprehensive Prediction Model for Player Selection in FIFA Manager Mode(Springer Science and Business Media Deutschland GmbH, 2023) Divakarla, U.; Chandrasekaran, K.; Hemant Kumar Reddy, K.; Rao, M.Game is one of the most entertaining shows for today’s all generation peoples, particularly Football in most part of countries of the world. Football as a sport is only growing more and more popular every day. It is currently the world’s most-watched sport and has the highest viewership audience. As a result, a whole industry has arisen around this sport with one important part of it being FIFA. The amount of budget allocated and the number of persons involved in a Football game directly or indirectly can affect the financial budget of a person to a federation's finance. In such cases, player selection for a finalist from the federation is the most crucial task. Every year different approaches were investigated for player selections, but none of them was regarded as the best approach for team selection. Thus, there is a need for a standard approach for finding out the perfect players for their teams with the exact qualities that they demand. In response, we have developed a machine learning model that predicts players who could replace a current existing player in a team. Along with that, we have also incorporated Data Analytics that helps us decide which factors would be more important than others. The proposed prediction model is implemented and the results of our machine learning (SAGA-ML) tool are applied to Electronics Arts’ FIFA Soccer game. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Automation with Blockchain: Creating a Marketplace for IoT Based Irrigation System(Springer Science and Business Media Deutschland GmbH, 2023) Divakarla, U.; Chandrasekaran, K.The next revolutionary technology to emerge since the creation of bitcoin in 2008 is blockchain technology. The Internet of Things (IoT), security, and other industries have all adopted blockchain technology. The article discusses the use of smart contracts, an Ethereum blockchain feature, to automate transactions in the Ethereum blockchain-based marketplace platform for Internet of Things devices. The following is demonstrated as a proof of concept by building a prototype of the suggested platform utilising Ethereum, Ganache, Web3, and Metamask. This exemplifies how transactions can be automated using smart contracts on the blockchain. When the land’s moisture content is less than some critical value, the prototype concentrates on automating the watering of the land. Real-time monitoring of the moisture level of the consumer’s land is possible. On the basis of the centralised paradigm, attempts are being made to construct such a system. The goal is to automate every activity. In the blockchain, the smart contract functions like a living thing. Because of this, automating is made possible. The blockchain also gives each gadget a unique account, considering them as participants in the transaction rather than just the sold goods, increasing the potential for automation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item IOT Devices Using Supervised Machine Learning Models for Anomaly Based Intrusion Detection(Institute of Electrical and Electronics Engineers Inc., 2023) Divakarla, U.; Chandrasekaran, K.Identifying dangers and irregularities in any infrastructure is a growing problem in the Internet of Things (IoT) industry. IoT infrastructure is utilised more frequently across a wide spectrum of organisations, which increases the risks and attack methods. Attacks and anomalies that could lead an IoT system to malfunction include denial of service attacks, data type probing, malicious control, malicious operation, scans, surveillance, and improper configuration. This article studies the ability of several machine learning models to predict attacks and abnormalities on IoT devices. The f1 score, area under the receiver operating characteristic curve, accuracy, precision, recall, and precision are among the metrics used to assess performance. ANNs, decision trees, and random forests all shown performance with a 99.4% accuracy rate in the system's tests. © 2023 IEEE.Item Semantic Segmentation for Autonomous Driving(Springer Science and Business Media Deutschland GmbH, 2023) Divakarla, U.; Bhat, R.; Madagaonkar, S.B.; Pranav, D.V.; Shyam, C.; Chandrashekar, K.Recently, autonomous vehicles (namely self-driving cars) are becoming increasingly common in developed urban areas. It is of utmost importance for real-time systems such as robots and automatic vehicles (AVs) to understand visual data, make inferences and predict events in the near future. The ability to perceive RGB values (and other visual data such as thermal, LiDAR), and segment each pixel into objects is called semantic segmentation. It is the first step toward any sort of automated machinery. Some existing models use deep learning methods for 3D object detection in RGB images but are not completely efficient when they are fused with thermal imagery as well. In this paper, we summarize many of these architectures starting from those that are applicable to general segmentation and then those that are specifically designed for autonomous vehicles. We also cover open challenges and questions for further research. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Predicting Phishing Emails and Websites to Fight Cybersecurity Threats Using Machine Learning Algorithms(Institute of Electrical and Electronics Engineers Inc., 2023) Divakarla, U.; Chandrasekaran, K.Phishing attempts, which try to fool people into giving attackers valuable information like login credentials, credit card numbers, and personal data, have grown more frequent and sophisticated over time. According to the 2021 Verizon Data Breach Investigations Report, phishing attempts were the cause of 36% of data breaches in 2020, up from 25% in 2019, and 96% of these assaults were sent by email. A mix of user education, technical controls, and automated detection systems can be used to prevent phishing attempts, which are crucial for preserving cybersecurity. Since phishing efforts are continually changing and growing more complex, machine learning and deep learning techniques are very useful for detecting them. Extreme gradient boosting (XGBoost) and Random Forest algorithms were utilised in this work to construct automated models for spotting phishing emails and links. These algorithms had accuracy rates of 98.57% and 96.7%, respectively. These models enable organisations and people to proactively recognise and stop phishing assaults, lowering the risk of monetary losses and data breaches. © 2023 IEEE.