An efficient deep learning-based scheme for web spam detection in IoT environment

2020 ◽  
Vol 108 ◽  
pp. 467-487 ◽  
Author(s):  
Aaisha Makkar ◽  
Neeraj Kumar
Keyword(s):  
Deep Learning ◽  
Spam Detection ◽  
Web Spam

Enhancement of email spam detection using improved deep learning algorithms for cyber security

2021 ◽  
pp. 1-34
Author(s):  
Kadam Vikas Samarthrao ◽  
Vandana M. Rohokale
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Visual Features ◽  
Spam Detection ◽  
Learning Approaches ◽  
Learning Technique ◽  
Text Features ◽  
Optimal Feature Selection ◽  
Optimal Feature ◽  
Email Spam

Email has sustained to be an essential part of our lives and as a means for better communication on the internet. The challenge pertains to the spam emails residing a large amount of space and bandwidth. The defect of state-of-the-art spam filtering methods like misclassification of genuine emails as spam (false positives) is the rising challenge to the internet world. Depending on the classification techniques, literature provides various algorithms for the classification of email spam. This paper tactics to develop a novel spam detection model for improved cybersecurity. The proposed model involves several phases like dataset acquisition, feature extraction, optimal feature selection, and detection. Initially, the benchmark dataset of email is collected that involves both text and image datasets. Next, the feature extraction is performed using two sets of features like text features and visual features. In the text features, Term Frequency-Inverse Document Frequency (TF-IDF) is extracted. For the visual features, color correlogram and Gray-Level Co-occurrence Matrix (GLCM) are determined. Since the length of the extracted feature vector seems to the long, the optimal feature selection process is done. The optimal feature selection is performed by a new meta-heuristic algorithm called Fitness Oriented Levy Improvement-based Dragonfly Algorithm (FLI-DA). Once the optimal features are selected, the detection is performed by the hybrid learning technique that is composed of two deep learning approaches named Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). For improving the performance of existing deep learning approaches, the number of hidden neurons of RNN and CNN is optimized by the same FLI-DA. Finally, the optimized hybrid learning technique having CNN and RNN classifies the data into spam and ham. The experimental outcomes show the ability of the proposed method to perform the spam email classification based on improved deep learning.

scholarly journals Comparative Study of Web Spam Detection using Data Mining

2013 ◽  
Vol 68 (18) ◽  
pp. 26-29
Author(s):  
Chirag Nathwani ◽  
Viralkumar Prajapati ◽  
Deven Agravat
Keyword(s):  
Data Mining ◽  
Comparative Study ◽  
Spam Detection ◽  
Web Spam ◽  
Using Data

scholarly journals Web Spam Detection by Learning from Small Labeled Samples

2012 ◽  
Vol 50 (21) ◽  
pp. 1-5 ◽  
Author(s):  
Jaber Karimpour ◽  
Ali A. Noroozi ◽  
Somayeh Alizadeh
Keyword(s):  
Spam Detection ◽  
Web Spam
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