Security and Privacy of Big Data: Issues, Challenges, and Future Perspectives

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 August 2023) | Viewed by 2962

Special Issue Editor


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Guest Editor
Applied College in Abqaiq, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Interests: cyber security; artificial intelligence; deep learning; big data

Special Issue Information

Dear Colleagues,

The gathering, processing, and analysis of the many types of organizational data have become more convenient and widespread as a result of the ongoing development of emerging technologies such as safe organizational systems, social networks, online commerce, and 5G systems. Because of this, personal information is frequently made more prone to being misused; consequently, it is becoming increasingly vital to investigate secure processes and optimized solutions for changing technologies.

The internet generates an incredible quantity of data, and the capacity to electronically store, transfer, and process that data is continuing to skyrocket along with the development of new technologies related to the internet. Big Data is a relatively new technology that has emerged in recent years as a response to this issue. Data security and privacy issues become more difficult to solve in the numerous processes that are involved due to the huge volume of data, including data collecting, storage, processing, and analysis. The general public, organizations, regulators, and data service providers all have a vested interest in, and a need for, cutting-edge technology and applications for maintaining the confidentiality and security of their customers' data.

This Special Issue, which strives to encourage high-quality submissions from the community, will focus on the preservation of data security and privacy in the context of Big Data processing as one of its areas of focus. We encourage contributions from a wide array of industries and types of data.

Dr. Theyazn H.H. Aldhyani
Guest Editor

Manuscript Submission Information

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Keywords

  • differential privacy
  • artificial intelligence on cybersecurity
  • federated analytics
  • privacy-preserving databases
  • privacy-preserving analytics
  • secure outsourcing
  • trustworthy machine learning
  • zero-trust architecture for data security
  • intrusion detection system

Published Papers (1 paper)

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Research

11 pages, 853 KiB  
Article
Enhancing the Performance of SQL Injection Attack Detection through Probabilistic Neural Networks
by Fawaz Khaled Alarfaj and Nayeem Ahmad Khan
Appl. Sci. 2023, 13(7), 4365; https://doi.org/10.3390/app13074365 - 29 Mar 2023
Cited by 7 | Viewed by 2484
Abstract
SQL injection attack is considered one of the most dangerous vulnerabilities exploited to leak sensitive information, gain unauthorized access, and cause financial loss to individuals and organizations. Conventional defense approaches use static and heuristic methods to detect previously known SQL injection attacks. Existing [...] Read more.
SQL injection attack is considered one of the most dangerous vulnerabilities exploited to leak sensitive information, gain unauthorized access, and cause financial loss to individuals and organizations. Conventional defense approaches use static and heuristic methods to detect previously known SQL injection attacks. Existing research uses machine learning techniques that have the capability of detecting previously unknown and novel attack types. Taking advantage of deep learning to improve detection accuracy, we propose using a probabilistic neural network (PNN) to detect SQL injection attacks. To achieve the best value in selecting a smoothing parament, we employed the BAT algorithm, a metaheuristic algorithm for optimization. In this study, a dataset consisting of 6000 SQL injections and 3500 normal queries was used. Features were extracted based on tokenizing and a regular expression and were selected using Chi-Square testing. The features used in this study were collected from the network traffic and SQL queries. The experiment results show that our proposed PNN achieved an accuracy of 99.19% with a precision of 0.995%, a recall of 0.981%, and an F-Measure of 0.928% when employing a 10-fold cross-validation compared to other classifiers in different scenarios. Full article
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