Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    A two-tier network based intrusion detection system architecture using machine learning approach
    (Institute of Electrical and Electronics Engineers Inc., 2016) Divyatmika; Sreekesh, M.
    Intrusion detection systems are systems that can detect any kind of malicious attacks, corrupted data or any kind of intrusion that can pose threat to our systems. In our paper, we would like to present a novel approach to build a network based intrusion detection system using machine learning approach. We have proposed a two-tier architecture to detect intrusions on network level. Network behaviour can be classified as misuse detection and anomaly detection. As our analysis depends on the network behaviour, we have considered data packets of TCP/IP as our input data. After, pre-processing the data by parameter filtering, we build a autonomous model on training set using hierarchical agglomerative clustering. Further, data gets classified as regular traffic pattern or intrusions using KNN classification. This reduces cost-overheads. Misuse detection is conducted using MLP algorithm. Anomaly detection is conducted using Reinforcement algorithm where network agents learn from the environment and take decisions accordingly. The TP rate of our architecture is 0.99 and false positive rate is 0.01. Thus, our architecture provides a high level of security by providing high TP and low false positive rate. And, it also analyzes the usual network patterns and learns incrementally (to build autonomous system) to separate normal data and threats. © 2016 IEEE.
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    An Efficient Rainfall Prediction Model Using Deep Learning Method
    (Institute of Electrical and Electronics Engineers Inc., 2023) Verma, V.K.; Janagama, H.S.; Patil, N.
    Rainfall is a crucial aspect of the Earth's natural cycle and it is necessary for various activities such as agriculture, water supply and hydroelectric power generation. However excessive rainfall can lead to floods, landslides and other destructive consequences, while insufficient rainfall can cause droughts and water shortages. Therefore accurate estimation of rainfall is essential to manage and mitigate the impacts of rainfall. In this study, the dataset is collected from the NASA Power database [22] to predict the annual rainfall in Mangalore(Karnataka), India. The data is collected from January 1, 2003 to February 04, 2023 using NASA POWER API. The study used four models MLP[15], LSTM, BiLSTM, CNN to predict the daily average precipitation that contributes to the annual rainfall. The input parameters considered for the prediction are maximum monthly temperature, minimum monthly temperature, humidity, atmospheric pressure and wind speed[9]. The model's performance is measured using mean squared error (MSE) and mean absolute error (MAE) of the predicted values on training and testing ratio 80:20. CNN(Convolutional Neural Network) model outperforms and gives the MSE and MAE for the CNN(Convolutional Neural Network) model are 0.0041 and 0.0456 respectively. © 2023 IEEE.
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    Comparative Assessment of Different Machine Learning Models to Estimate Daily Soil Moisture
    (Springer Science and Business Media Deutschland GmbH, 2023) Nagashree, G.E.; Nema, M.K.
    Soil moisture is vital as it is the primary governing factor of agriculture production and natural vegetation growth. It plays an essential role in understanding the hydrological cycle and its effect on weather and climate, and its precise prediction helps to manage the water resources optimally. Prediction of soil moisture is dependent on surface meteorological variables and soil attributes. Existing soil moisture models/prediction methods are inaccurate, and developing an optimum mathematical model for it is difficult. This study evaluates the performance of four machine learning models (deep neural network (DNN) regression, support vector machine (SVM), multiple layer perceptron (MLP), and multi-linear regression (MLR) to estimate the soil moisture conditions. The models were tested for soil moisture at two depths (25 and 50 cm depth) using the meteorological data of two stations located in a Lesser Himalayan catchment. The model outputs were compared with the observed data, and intercomparison was also made. The model performance was evaluated based on MAPE, RMSE, Nash–Sutcliffe efficiency coefficient (EN–S), and R2. The study results indicated that the DNN model outperforms the other prediction models with the highest efficacy for both stations. Therefore, the DNN model can be endorsed to estimate soil moisture when primary meteorological data are available, and it can be promising for water-efficient agriculture applications and draught management. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.