scholarly journals Hybrid Email Spam Detection Model Using Artificial Intelligence

2020 ◽  
Vol 10 (2) ◽  
pp. 316-322 ◽  
Author(s):  
Samira. Douzi ◽  
◽  
Feda A. AlShahwan ◽  
Mouad. Lemoudden ◽  
Bouabid. El Ouahidi
Keyword(s):  
Artificial Intelligence ◽  
Spam Detection ◽  
Detection Model ◽  
Email 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.

Enhanced Bio-Inspired Algorithms for Detecting and Filtering Spam

2018 ◽  
pp. 179-215
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Electronic Communication ◽  
Distance Measure ◽  
Machine Learning Algorithms ◽  
Spam Detection ◽  
Social Bees ◽  
Representation Technique ◽  
Sensitive Parameters ◽  
Measure Entropy ◽  
Email Spam ◽  
F Measure

The internet era promotes electronic commerce and facilitates access to many services. In today's digital society, the explosion in communication has revolutionized the field of electronic communication. Unfortunately, this technology has become incontestably the original source of malicious activities, especially the plague called undesirables email (SPAM) that has grown tremendously in the last few years. This chapter unveils fresh bio-inspired techniques (artificial social cockroaches [ASC], artificial haemostasis system [AHS], and artificial heart lungs system [AHLS]) and their application for SPAM detection. For the experimentation, the authors used the benchmark SMS Spam corpus V.0.1 and the validation measures (recall, precision, f-measure, entropy, accuracy, and error). They optimize the sensitive parameters of each algorithm (text representation technique, distance measure, weightings, and threshold). The results are positive compared to the result of artificial social bees and machine-learning algorithms (decision tree C4.5 and K-means).

scholarly journals Network Intrusion Detection Model Based on Artificial Intelligence

2020 ◽  
Vol 1617 ◽  
pp. 012082
Author(s):  
Qingchuan Meng ◽  
Youzi Zhang ◽  
Fengzhi Wu ◽  
Xiaoming Chen
Keyword(s):  
Artificial Intelligence ◽  
Intrusion Detection ◽  
Network Intrusion Detection ◽  
Detection Model ◽  
Model Based ◽  
Network Intrusion
Sign in / Sign up

Export Citation Format

Share Document