A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection

2021 ◽  
Author(s):  
Canan Batur Şahin ◽  
Laith Abualigah
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Static Analysis ◽  
Selection Model ◽  
Vulnerability Detection

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 Indicator Selection for Topic Popularity Definition Based on AHP and Deep Learning Models

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Yuling Hong ◽  
Qishan Zhang
Keyword(s):  
Deep Learning ◽  
Social Network ◽  
Evaluation Method ◽  
Short Term Memory ◽  
Selection Model ◽  
Learning Technology ◽  
Prevention Measures ◽  
Learning Methods ◽  
Indicator Selection ◽  
The Social

Purpose. The purpose of this article is to predict the topic popularity on the social network accurately. Indicator selection model for a new definition of topic popularity with degree of grey incidence (DGI) is undertook based on an improved analytic hierarchy process (AHP). Design/Methodology/Approach. Through screening the importance of indicators by the deep learning methods such as recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent unit (GRU), a selection model of topic popularity indicators based on AHP is set up. Findings. The results show that when topic popularity is being built quantitatively based on the DGI method and different weights of topic indicators are obtained from the help of AHP, the average accuracy of topic popularity prediction can reach 97.66%. The training speed is higher and the prediction precision is higher. Practical Implications. The method proposed in the paper can be used to calculate the popularity of each hot topic and generate the ranking list of topics’ popularities. Moreover, its future popularity can be predicted by deep learning methods. At the same time, a new application field of deep learning technology has been further discovered and verified. Originality/Value. This can lay a theoretical foundation for the formulation of topic popularity tendency prevention measures on the social network and provide an evaluation method which is consistent with the actual situation.

TokenCheck: Towards Deep Learning Based Security Vulnerability Detection In ERC-20 Tokens

2021 ◽  
Author(s):  
Subhasish Goswami ◽  
Rabijit Singh ◽  
Nayanjeet Saikia ◽  
Kaushik Kumar Bora ◽  
Utpal Sharma
Keyword(s):  
Deep Learning ◽  
Vulnerability Detection ◽  
Security Vulnerability

scholarly journals Evaluation of SQL Injection Vulnerability Detection Tools

2019 ◽  
Vol 9 (1) ◽  
pp. 1747-1751
Keyword(s):  
Static Analysis ◽  
Web Applications ◽  
Source Code ◽  
False Negative ◽  
Vulnerability Detection ◽  
Sql Injection ◽  
User Input ◽  
Root Cause ◽  
Analysis Tools ◽  
Free Open Source

SQL injection vulnerabilities have been predominant on database-driven web applications since almost one decade. Exploiting such vulnerabilities enables attackers to gain unauthorized access to the back-end databases by altering the original SQL statements through manipulating user input. Testing web applications for identifying SQL injection vulnerabilities before deployment is essential to get rid of them. However, checking such vulnerabilities by hand is very tedious, difficult, and time-consuming. Web vulnerability static analysis tools are software tools for automatically identifying the root cause of SQL injection vulnerabilities in web applications source code. In this paper, we test and evaluate three free/open source static analysis tools using eight web applications with numerous known vulnerabilities, primarily for false negative rates. The evaluation results were compared and analysed, and they indicate a need to improve the tools.

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