Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Advances in Machine Learning and Big Data Analytics
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Machine Learning and Big Data Analytics

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: 20 June 2025 | Viewed by 5124

Special Issue Editors

Dr. Md Liakat Ali

E-Mail Website
Guest Editor
Department of Computer Science & Physics, Rider University, 2083 Lawrenceville Rd, Lawrenceville, NJ 08648, USA
Interests: machine learning; artificial intelligence; behavioral biometrics; cybersecurity; data science; big data analytics
Dr. Muath Obaidat

E-Mail Website
Guest Editor
Computer Science Department, City University of New York, New York, NY 10019, USA
Interests: cybersecurity and networking; network security; IoT security and privacy; blockchain; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Suzanna Schmeelk

E-Mail Website
Guest Editor
Department of Computer Science, Mathematics and Science, St. John's University, Queens, NY, USA
Interests: cybersecurity; machine learning; mathematics education; data breaches

Special Issue Information

Dear Colleagues,

Machine learning and big data analysis have become a powerful driving force for predictive analysis and guiding data-driven decision-making. Combining the processing power of big data technology with the intelligent algorithms of machine learning provides us with unprecedented opportunities to collate actionable information from massive data sets.

In this Special Issue, we invite submissions that explore cutting-edge research and recent advances in machine learning and big data analytics. Theoretical and experimental research as well as comprehensive reviews are welcome.

Dr. Md Liakat Ali
Dr. Muath Obaidat
Dr. Suzanna Schmeelk
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • big data analytics
  • artificial intelligence
  • cybersecurity

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 2519 KiB  
Article
Deep Learning vs. Machine Learning for Intrusion Detection in Computer Networks: A Comparative Study
by Md Liakat Ali, Kutub Thakur, Suzanna Schmeelk, Joan Debello and Denise Dragos
Appl. Sci. 2025, 15(4), 1903; https://doi.org/10.3390/app15041903 - 12 Feb 2025
Viewed by 4787
Abstract
In response to the increasing volume of network traffic and the growing sophistication of cyber threats, this study examines the use of deep learning-based intrusion detection systems (IDSs) in large-scale network environments. Traditional IDS face challenges such as high false positive rates, complex [...] Read more.
In response to the increasing volume of network traffic and the growing sophistication of cyber threats, this study examines the use of deep learning-based intrusion detection systems (IDSs) in large-scale network environments. Traditional IDS face challenges such as high false positive rates, complex feature engineering, and class imbalances in datasets, all of which impede accurate threat detection. To overcome these limitations, we implement various deep learning models, including multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM), alongside traditional machine learning algorithms such as logistic regression, naive Bayes, random forest, K-nearest neighbors, and decision trees. A significant contribution of this study is the application of the synthetic minority over-sampling technique (SMOTE) to address class imbalance, enhancing the representativeness of the learning process. Additionally, we conduct a comprehensive performance comparison of the models, incorporating correlation-based feature selection and hyperparameter tuning to maximize detection accuracy. Our results indicate that deep learning models, particularly CNN and LSTM, outperform traditional machine learning approaches in cyber threat detection, achieving accuracy rates of 98%. However, random forest achieves the highest accuracy at 99.9%, demonstrating its effectiveness in structured intrusion detection tasks. Moreover, we evaluate computational efficiency and practical deployment considerations, discussing trade-offs between accuracy and resource consumption. These findings highlight the potential of deep learning-based IDS for large-scale network security applications while addressing key challenges such as interpretability and computational overhead. The study provides actionable insights for selecting the most suitable IDS models based on specific network environments and security requirements. Full article
(This article belongs to the Special Issue Advances in Machine Learning and Big Data Analytics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop