Hardware Trojan Detection Using XGBoost Algorithm for IoT with Data Augmentation Using CTGAN and SMOTE

Srinivas, Chintakindi; Balachander, Srijith; Samant, Yogesh Chandra Singh; Hariharan, Balaji; Devi, M. Nirmala

doi:10.1007/978-3-030-79276-3_10

Cited by 3 publications

(1 citation statement)

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“…Machine Learning has proved its importance in the 21st century in various sectors such as medical, defence, automobiles, agriculture etc. [1,2]. The major factors that drive these systems are the use of effective algorithms to capture the physical data and convert it to data used by AI.…”

Section: Introductionmentioning

confidence: 99%

Feature‐based augmentation and classification for tabular data

Sathianarayanan

Samant

Guruprasad

et al. 2022

CAAI Trans on Intel Tech

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Generating synthetic samples for a tabular data is a strenuous task. Most of the time, the columns (features) in the dataset may not follow an ideal distribution function. The objective of the proposed algorithm, Histogram Augmentation Technique (HAT), is to generate a dataset whose distribution is similar to that of the original dataset. This augmentation is achieved based on individual columns, where separate algorithms are designed for continuous and discrete columns. Humans also use features of an object for interpretation. When humans make a judgement, they notice prominent features and characterise the perceived object. However, conventional Machine Learning classifiers are designed and trained on the basis of samples. Taking the features as the basis for classification, Feature Importance Classifier (FIC) has been attempted in this work. FIC treats every feature independent of each other, and ranks the features based on its dependence with the classified label. It has been found that the FIC has the highest accuracy and has improved the accuracy by 5.54% on average, when it's compared to other classifiers. The suggested algorithms have been experimented on five datasets and compared with two augmentation algorithms and four state‐of‐the‐art ML classification algorithms.

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Section: Introductionmentioning

confidence: 99%