Conference Papers

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

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Subject: search.filters.subject.Machine Learning (ML)

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    Measuring Robustness of Side Channel Analysis in the Detection of Hardware Trojans in Encryption Modules
    (Institute of Electrical and Electronics Engineers Inc., 2022) Masand, S.; Fernandes, K.R.; Bhat, M.S.
    The hardware, software, and the data present in any electronic system predominantly determine the system's security. Just like software, hardware is equally prone to attacks leading to malfunction. Altering the circuit design via different techniques to create a secret channel that maliciously affects the functionality of the system is called Hardware Trojan (HT) insertion and can cause significant harm. Therefore, it is necessary to efficiently detect the presence of Hardware Trojans in any system. This paper presents the use of a well known Hardware Trojan detection technique called Side-Channel Analysis (SCA) to detect Trojans in encryption modules like AES and RSA. The availability of a golden circuit to compare against the Circuit Under Test (CUT) is assumed to detect Trojans through side-channel analysis. For the same, Xilinx Vivado is used to program the Intellectual Properties (IPs) on the Nexys 4 DDR FPGA. It is shown that the above- mentioned technique is not accurate in certain cases especially when the size of the Trojan is not large enough. So, an alternative technique is proposed that uses machine learning algorithms - that provide an accuracy of at least 93.06% while using the side channel data-sets, thereby significantly increasing the Trojan detection accuracy. © 2022 IEEE.
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    Prediction of High-Resolution Soil Moisture Using Multi-source Data and Machine Learning
    (Springer Science and Business Media Deutschland GmbH, 2024) Sudhakara, B.; Bhattacharjee, S.
    Soil moisture (SM) stands as a critical meteorological element influencing the dynamic interplay between the land and the atmosphere. Its comprehension, modeling, and examination hold key significance in unraveling this interaction. Information about the surface SM is necessary for predicting crop yield, future disasters, etc. Ground-based SM measurement is accurate but time-consuming and costly. An alternate approach for measuring SM using satellite images is becoming more popular in recent years. Surface SM retrieval with a fine-resolution still poses challenges. The proposed work considers multi-satellite data for predicting high-resolution SM of Oklahoma, USA using multiple Machine Learning (ML) algorithms, such as K-nearest neighbour (KNN), Decision tree (DT), Random forest (RF), and Extra trees regressor (ETR). A high-resolution SM map for the study region is also reported, considering the Soil Moisture Active Passive (SMAP) SM data as the base, Landsat 8 bands, and normalized difference vegetation index (NDVI) data as the reference datasets. The ETR model performed the best with a mean absolute error (MAE) of 0.940 mm, a root mean square error (RMSE) of 1.303 mm and a coefficient of determination (R2 ) of 0.965. The external validation is carried out with ground-based SM data from the International Soil Moisture Network (ISMN). Both the actual SMAP SM and predicted SM values demonstrate a comparable correlation with the ISMN data. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Inorganic Chemical Reaction Predictor Using Random Forest and Support Vector Machine
    (Institute of Electrical and Electronics Engineers Inc., 2025) Ramesh, G.; Sahil, M.; Palan, S.A.; Bhandary, D.; Shetty, S.S.; Poojary, K.K.; Sowjanya, N.
    The Chemical Reaction Predictor project shall use machine learning approaches to make predictions on chemical reaction effects. When a large enough group of known reactions is available, each identified set of reactants and products can be used to construct a model into which can be fed any set of reactants. It includes data acquisition and data pre-processing, feature selection of reactant properties and reaction conditions, and construction of several predictive models. The first and main goal is to dogmatically apply machine learning models such as Random Forests and Support Vector Machines to attain an accuracy of 60% or higher. Furthermore, we measure the accuracy, and other measures such as precision, recall, and F1 score to determine the efficiency of these models. Finally, while the optimal model is found and implemented, it is brought within a simple graphical user interface that enables the users to input reactants and obtain predicted products. © 2025 IEEE.