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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Devareddi, Ravi Babu
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Computer Science & IT Engineering

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Phishing website detection using novel integration of BERT and XLNet with deep learning sequential models Rao, Kongara Srinivasa; Valluru, Dinesh; Patnala, Satishkumar; Devareddi, Ravi Babu; Rama Krishna, Tummalapalli Siva; Sravani, Andavarapu
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1273-1283

Abstract

Phishing websites pose a significant threat to online security, necessitating robust detection mechanisms to safeguard users' sensitive information. This study explores the efficacy of various deep learning architectures for phishing website detection. Initially, traditional sequential models, including recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), achieve accuracies of 95%, 96%, and 96.5%, respectively, on a curated dataset. Building upon these results, hybrid architectures that combine the strengths of traditional sequential models with state-of-the-art language representation models, bidirectional encoder representations from transformers (BERT) and XLNet, are investigated. Combinations such as RNN with BERT, BERT with LSTM, BERT with GRU, RNN with XLNet, XLNet with LSTM, and XLNet with GRU are evaluated. Through experimentation, accuracies of 94.5%, 96.5%, 96.1%, 95.7%, 97.4%, and 97%, respectively, are achieved, demonstrating the effectiveness of hybrid deep learning architectures in enhancing phishing detection performance. These findings contribute to advancing the state-of-the-art in cybersecurity practices and underscore the importance of leveraging diverse model types to combat online threats effectively.
A novel model to detect and categorize objects from images by using a hybrid machine learning model Sethi, Nilambar; Rama Raju, Vetukuri Venkata Siva; Lokavarapu, Venkata Srinivas; Devareddi, Ravi Babu; Reddy, Shiva Shankar; Nrusimhadri, Silpa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp667-679

Abstract

As humans, we can easily recognize and distinguish different features of objects in images due to our brain’s ability to unconsciously learn from a set of images. The objectives of this effort are to develop a model that is capable of identifying and categorizing objects that are present within images. We imported the dataset from Keras and loaded it using data loaders to achieve this. We then utilized various deep learning algorithms, such as visual geometry group (VGG)-16 and a simple net-random forest hybrid model, to classify the objects. After classification, the accuracy obtained by VGG16 and the hybrid model was 84.7% and 89.6%, respectively. Therefore, the proposed model successfully detects objects in images using a simple net as a feature extractor and a random forest for object classification, achieving better accuracy than VGG16.
Co-Authors Lokavarapu, Venkata Srinivas Nrusimhadri, Silpa Patnala, Satishkumar Rama Krishna, Tummalapalli Siva Rama Raju, Vetukuri Venkata Siva Rao, Kongara Srinivasa Reddy, Shiva Shankar Sethi, Nilambar Sravani, Andavarapu Valluru, Dinesh