Department Of Computer Science And Technology

Permanent URI for this communityhttps://kr.cup.edu.in/handle/32116/79

Browse

Subject: search.filters.subject.Deep Learning

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    A systematic literature review on phishing website detection techniques
    (King Saud bin Abdulaziz University, 2023-01-11T00:00:00) Safi, Asadullah; Singh, Satwinder
    Phishing is a fraud attempt in which an attacker acts as a trusted person or entity to obtain sensitive information from an internet user. In this Systematic Literature Survey (SLR), different phishing detection approaches, namely Lists Based, Visual Similarity, Heuristic, Machine Learning, and Deep Learning based techniques, are studied and compared. For this purpose, several algorithms, data sets, and techniques for phishing website detection are revealed with the proposed research questions. A systematic Literature survey was conducted on 80 scientific papers published in the last five years in research journals, conferences, leading workshops, the thesis of researchers, book chapters, and from high-rank websites. The work carried out in this study is an update in the previous systematic literature surveys with more focus on the latest trends in phishing detection techniques. This study enhances readers' understanding of different types of phishing website detection techniques, the data sets used, and the comparative performance of algorithms used. Machine Learning techniques have been applied the most, i.e., 57 as per studies, according to the SLR. In addition, the survey revealed that while gathering the data sets, researchers primarily accessed two sources: 53 studies accessed the PhishTank website (53 for the phishing data set) and 29 studies used Alexa's website for downloading legitimate data sets. Also, as per the literature survey, most studies used Machine Learning techniques; 31 used Random Forest Classifier. Finally, as per different studies, Convolution Neural Network (CNN) achieved the highest Accuracy, 99.98%, for detecting phishing websites. � 2023 The Author(s)
  • No Thumbnail Available
    Item
    Deep Ensemble Approach for COVID-19 Fake News Detection from Social Media
    (Institute of Electrical and Electronics Engineers Inc., 2021-10-20T00:00:00) Priya, Anu; Kumar, Abhinav
    Social media networks such as Facebook and Twitter are overwhelmed with COVID-19-related posts during the outbreak. People have also posted several fake news among the massive COVID-19-related social media posts. Fake news has the potential to create public fear, weaken government credibility, and pose a serious threat to social order. This paper provides a deep ensemble-based method for detecting COVID-19 fake news. An ensemble classifier is made up of three different classifiers: Support Vector Machine, Dense Neural Network, and Convolutional Neural Network. The extensive experiments with the proposed ensemble model and eight different conventional machine learning classifiers are carried out using the character and word n-gram TF-IDF features. The results of the experiments show that character n-gram features outperform word n-gram features. The proposed deep ensemble classifier performed better, with a weighted Fl -score of 0.97 in contrast to numerous conventional machine learning classifiers and deep learning classifiers. � 2021 IEEE