AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection-Reference-Cited by-同舟云学术

AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection

Published:2023 Issue: Volume:11 Page:135507-135516
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Thalji Nisrean¹^ORCID,Raza Ali²^ORCID,Islam Mohammad Shariful³^ORCID,Samee Nagwan Abdel⁴^ORCID,Jamjoom Mona M.⁵^ORCID

Affiliation:

1. Department of Robotics and Artificial Intelligence, Jadara University, Irbid, Jordan

2. Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan

3. Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Chattogram, Bangladesh

4. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

5. Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Funder

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia, through Researchers Supporting

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10332180.pdf?arnumber=10332180

Reference36 articles.

1. A systematic review of detection and prevention techniques of SQL injection attacks

2. Impact of the Keep-Alive Parameter on SQL Injection Attack Detection in Network Flow Data

3. A novel ensemble method for enhancing Internet of Things device security against botnet attacks

4. Deep Ensemble-based Efficient Framework for Network Attack Detection

5. An overview of malware injection attacks: Techniques, impacts, and countermeasures;Madhvan;Borneo Int. J. eISSN,2023

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