Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text

• URL: http://arxiv.org/abs/2110.15718v1
• Date: Sun, 10 Oct 2021 17:19:37 GMT
• Title: Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text
• Authors: Mai A. Shaaban (1), Yasser F. Hassan (2), and Shawkat K. Guirguis (3) ((1) Department of Mathematics and Computer Science, Faculty of Science, Alexandria University, Alexandria, Egypt, (2) Faculty of Computers and Data Science, Alexandria University, Alexandria, Egypt, (3) Institute of Graduate Studies and Research, Alexandria University, Alexandria, Egypt)
• Abstract summary: This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.
• Score: 219.15486286590016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increase in people's use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medical information regarding the COVID-19 pandemic are widely shared on social media leading to people's fear and confusion. Thus, filtering spam content is vital to reduce risks and threats. Previous studies relied on machine learning and deep learning approaches for spam classification, but these approaches have two limitations. Machine learning models require manual feature engineering, whereas deep neural networks require a high computational cost. This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along with base classifiers such as random forests and extremely randomized trees for classifying texts into spam or legitimate ones. Moreover, the model employs ensemble learning procedures like boosting and bagging. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.

Related papers

• Undermining Image and Text Classification Algorithms Using Adversarial Attacks [0.0]
  Our study addresses the gap by training various machine learning models and using GANs and SMOTE to generate additional data points aimed at attacking text classification models. Our experiments reveal a significant vulnerability in classification models. Specifically, we observe a 20 % decrease in accuracy for the top-performing text classification models post-attack, along with a 30 % decrease in facial recognition accuracy.
  arXiv  Detail & Related papers  (2024-11-03T18:44:28Z)
• Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem [37.650342256199096]
  We introduce MaleficNet 2.0, a technique to embed self-extracting, self-executing malware in neural networks. MaleficNet 2.0 injection technique is stealthy, does not degrade the performance of the model, and is robust against removal techniques. We implement a proof-of-concept self-extracting neural network malware using MaleficNet 2.0, demonstrating the practicality of the attack against a widely adopted machine learning framework.
  arXiv  Detail & Related papers  (2024-03-06T10:27:08Z)
• Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework [1.3353802999735709]
  Current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker. In this research, we proposed a framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing.
  arXiv  Detail & Related papers  (2024-02-27T06:47:52Z)
• What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection [53.063161380423715]
  Existing detection models have shown remarkable success in discriminating known deepfake audio, but struggle when encountering new attack types. We propose a continual learning approach called Radian Weight Modification (RWM) for audio deepfake detection.
  arXiv  Detail & Related papers  (2023-12-15T09:52:17Z)
• Verifying the Robustness of Automatic Credibility Assessment [79.08422736721764]
  Text classification methods have been widely investigated as a way to detect content of low credibility. In some cases insignificant changes in input text can mislead the models. We introduce BODEGA: a benchmark for testing both victim models and attack methods on misinformation detection tasks.
  arXiv  Detail & Related papers  (2023-03-14T16:11:47Z)
• Deep networks for system identification: a Survey [56.34005280792013]
  System identification learns mathematical descriptions of dynamic systems from input-output data. Main aim of the identified model is to predict new data from previous observations. We discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks.
  arXiv  Detail & Related papers  (2023-01-30T12:38:31Z)
• Neurosymbolic hybrid approach to driver collision warning [64.02492460600905]
  There are two main algorithmic approaches to autonomous driving systems. Deep learning alone has achieved state-of-the-art results in many areas. But sometimes it can be very difficult to debug if the deep learning model doesn't work.
  arXiv  Detail & Related papers  (2022-03-28T20:29:50Z)
• Modeling Coherency in Generated Emails by Leveraging Deep Neural Learners [6.891238879512674]
  Advanced machine learning and natural language techniques enable attackers to launch sophisticated and targeted social engineering-based attacks. Email masquerading using targeted emails to fool the victim is an advanced attack method. We demonstrate the generation of short and targeted text messages using the deep model.
  arXiv  Detail & Related papers  (2020-07-14T23:47:08Z)
• Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems [83.98774574197613]
  We take one of the simplest inference methods, a truncated max-product Belief propagation, and add what is necessary to make it a proper component of a deep learning model. This BP-Layer can be used as the final or an intermediate block in convolutional neural networks (CNNs) The model is applicable to a range of dense prediction problems, is well-trainable and provides parameter-efficient and robust solutions in stereo, optical flow and semantic segmentation.
  arXiv  Detail & Related papers  (2020-03-13T13:11:35Z)
• Cyber Attack Detection thanks to Machine Learning Algorithms [0.0]
  This paper explores Machine Learning as a viable solution by examining its capabilities to classify malicious traffic in a network. Our approach analyzes five different machine learning algorithms against NetFlow dataset containing common botnets. The Random Forest succeeds in detecting more than 95% of the botnets in 8 out of 13 scenarios and more than 55% in the most difficult datasets.
  arXiv  Detail & Related papers  (2020-01-17T13:52:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.