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Govind Ballabh Pant University of Agriculture and Technology, Pantnagar

After independence, development of the rural sector was considered the primary concern of the Government of India. In 1949, with the appointment of the Radhakrishnan University Education Commission, imparting of agricultural education through the setting up of rural universities became the focal point. Later, in 1954 an Indo-American team led by Dr. K.R. Damle, the Vice-President of ICAR, was constituted that arrived at the idea of establishing a Rural University on the land-grant pattern of USA. As a consequence a contract between the Government of India, the Technical Cooperation Mission and some land-grant universities of USA, was signed to promote agricultural education in the country. The US universities included the universities of Tennessee, the Ohio State University, the Kansas State University, The University of Illinois, the Pennsylvania State University and the University of Missouri. The task of assisting Uttar Pradesh in establishing an agricultural university was assigned to the University of Illinois which signed a contract in 1959 to establish an agricultural University in the State. Dean, H.W. Hannah, of the University of Illinois prepared a blueprint for a Rural University to be set up at the Tarai State Farm in the district Nainital, UP. In the initial stage the University of Illinois also offered the services of its scientists and teachers. Thus, in 1960, the first agricultural university of India, UP Agricultural University, came into being by an Act of legislation, UP Act XI-V of 1958. The Act was later amended under UP Universities Re-enactment and Amendment Act 1972 and the University was rechristened as Govind Ballabh Pant University of Agriculture and Technology keeping in view the contributions of Pt. Govind Ballabh Pant, the then Chief Minister of UP. The University was dedicated to the Nation by the first Prime Minister of India Pt Jawaharlal Nehru on 17 November 1960. The G.B. Pant University is a symbol of successful partnership between India and the United States. The establishment of this university brought about a revolution in agricultural education, research and extension. It paved the way for setting up of 31 other agricultural universities in the country.

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Subject: Information Technology × Author: Rai, Mahima × End date: 2019 × Start date: 2018 × Type: Thesis × Thesis Type: M.Tech. ×
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    ThesisItemOpen Access
    Intrusion detection using ensemble methods and deep learning
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-08) Rai, Mahima; Mandoria, H.L.
    In recent years Machine Learning and Deep Learning have played a crucial role in the field of cyber security. This research therefore intends to design IDS with a good mix of various ML and DL classifiers. It gives us an idea about which classifier gives the highest accuracy. The aim of this research is to classify whether the packet is a normal data packet or attacked data packet. Therefore the model of IDS should be implemented based on effective and significant ML and DL algorithms. To ensure network security, an effective intrusion detection system is required. Several ensemble methods like XG-Boost and LGBM have been developed in the past 4-5 years. These have not been exploited in the previous researches on anomaly detection. Our study will make use of these novel Gradient Boosting Decision Tree algorithms. XG-Boost and LGBM have proved to be the most productive techniques for several supervised and unsupervised learning tasks. In this research, we study several machine learning and deep learning classifiers and compare their performance. We have used the NSL KDD dataset to predict the probability of occurrence of 21 different classes of attacks on a network and three different categories of models - Linear Models including Logistic Regression and Stochastic Gradient Descent (SGD) classifier; Gradient Boosting Decision Tree ensembles including LightGBM (LGBM) and XG-Boost; and a Deep Neural Network (DNN) classifier and also trained a stacked model consisting of all these models as base learners. Finally, compared the performances of all the models for Network Intrusion Detection using the NSL-KDD dataset and have drawn useful conclusions. The simulation result shows that Light GBM, XGBoost, and stacked classifiers outperform with high accuracy as compared to Logistic regression, Stochastic Gradient Descent Classifier and Deep Neural network. All of these are predictive analysis techniques.