Conference Papers

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    Comparative study of Principal Component Analysis based Intrusion Detection approach using machine learning algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2015) Chabathula, K.J.; Jaidhar, C.D.; M.a, M.A.
    This paper induces the prominence of variegated machine learning techniques adapted so far for the identifying different network attacks and suggests a preferable Intrusion Detection System (IDS) with the available system resources while optimizing the speed and accuracy. With booming number of intruders and hackers in todays vast and sophisticated computerized world, it is unceasingly challenging to identify unknown attacks in promising time with no false positive and no false negative. Principal Component Analysis (PCA) curtails the amount of data to be compared by reducing their dimensions prior to classification that results in reduction of detection time. In this paper, PCA is adopted to reduce higher dimension dataset to lower dimension dataset. It is accomplished by converting network packet header fields into a vector then PCA applied over high dimensional dataset to reduce the dimension. The reduced dimension dataset is tested with Support Vector Machines (SVM), K-Nearest Neighbors (KNN), J48 Tree algorithm, Random Forest Tree classification algorithm, Adaboost algorihm, Nearest Neighbors generalized Exemplars algorithm, Navebayes probabilistic classifier and Voting Features Interval classification algorithm. Obtained results demonstrates detection accuracy, computational efficiency with minimal false alarms, less system resources utilization. Experimental results are compared with respect to detection rate and detection time and found that TREE classification algorithms achieved superior results over other algorithms. The whole experiment is conducted by using KDD99 data set. © 2015 IEEE.
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    Predicting Survival of People with Heart Failure Using Oversampling, Feature Selections and Dimensionality Reduction
    (Institute of Electrical and Electronics Engineers Inc., 2022) Niharika, G.; Lekha, A.I.; Leela Akshaya, T.; Anand Kumar, A.M.
    Cardiovascular diseases are deadly and kill millions of people around the world every year. Heart failure is one of the unfortunate consequence where the heart is unable to pump enough blood for the body. A medical checkup of these patients with attributes including creatinine phosphokinase, ejection fraction, serum creatinine and serum sodium can be used for analysis. In this paper, we have analysed this clinical data and built machine learning models that can predict the survival rate of heart failure of a person. We have used various dimensionality reduction techniques to analyse the data with the aim of reducing the dimensions of the dataset. Finally, we reduced the overfitting of data using Synthetic Minority Oversampling Technique(SMOTE) and Adaptive Synthetic(ADASYN). © 2022 IEEE.
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    Hybrid Genetic Algorithm and Machine Learning Approach for Software Reliability Assessment in Safety-Critical Systems
    (Institute of Electrical and Electronics Engineers Inc., 2024) Goyal, G.; Sharma, K.; Anshuman; Mittal, V.; Singla, B.; Das, M.; Mohan, B.R.
    Software reliability is a paramount determinant of software quality. In this research paper, we delve into utilizing Genetic Algorithms (GAs) for feature selection and classification. We undertake a comprehensive evaluation and comparative analysis of Machine Learning models, specifically Random Forest and Logistic Regression, both with and without Genetic Algorithmdriven feature selection. Our findings substantiate the significant impact of Genetic Algorithms in improving the accuracy of software reliability analysis. © 2024 IEEE.