Conference Papers

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    Fake News Detection Using Machine Learning Algorithms
    (Association for Computing Machinery, 2022) Imbwaga, J.L.; Chittaragi, N.; Koolagudi, S.G.
    There has been an exponential growth in users sharing news and information in real-time on various social media platforms worldwide. However, few of the users share fake and misleading news for various reasons. The reasons for sharing fake news may not be limited to financial, personal, and/or political gain. Since users cannot determine or censor the type of content that appears on their respective platforms, fake news can pose significant and detrimental effects on an individual and society at large. In this regard, we have proposed the work with the primary objective of development of a fake news detection system by applying supervised machine learning algorithms on an annotated (labeled) dataset. The dataset was selected from Kaggle, consisting of fake news with 23503 entries and true news with 21418 entries. An overall better accuracies are observed with tree-based decision tree classifiers and a gradient boosting ensemble algorithm. © 2022 ACM.
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    Comparative Analysis of Intrusion Detection System using ML and DL Techniques
    (Springer Science and Business Media Deutschland GmbH, 2023) Sunil, C.K.; Reddy, S.; Kanber, S.G.; Vuddanti, V.R.; Patil, N.
    Intrusion detection system (IDS) protects the network from suspicious and harmful activities. It scans the network for harmful activity and any potential breaching. Even in the presence of the so many network intrusion APIs there are still problems in detecting the intrusion. These problems can be handled through the normalization of whole dataset, and ranking of feature on benchmark dataset before training the classification models. In this paper, used NSL-KDD dataset for the analysation of various features and test the efficiency of the various algorithms. For each value of k, then, trained each model separately and evaluated the feature selection approach with the algorithms. This work, make use of feature selection techniques like Information gain, SelectKBest, Pearson coefficient and Random forest. And also iterate over the number of features to pick the best values in order to train the dataset.The selected features then tested on different machine and deep learning approach. This work make use of stacked ensemble learning technique for classification. This stacked ensemble learner contains model which makes un-correlated error there by making the model more robust. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.