Issued quarterly (4 issues per year)

ISSN (Online): 2588-123X
ISSN (Print):
Researchgate Linkedin EmailFacebook Twitter
  • Home
  • Editorial Board
  • Guidelines
  • Policies
  • Submissions
  • Search
  • Archives
  • Announcements
  • Statistics

About TDTU

Ton Duc Thang University (TDTU) is a public university with the main campus located in vibrant Ho Chi Minh City, Vietnam’s economic and educational hub. Founded in 1997, TDTU has developed into one of the largest and fastest growing universities in Vietnam with more than 22,000 students, enrolled in undergraduate and graduate programs ranging from science, engineering to business management, law, and humanities. To foster the country’s human resources and best serve the nation in the knowledge based economy of the 21st century, TDTU is combining vocational training with high-level research. The establishment of JAEC is one of TDTU’s efforts in this direction. More

Publication Information


Publisher
Ton Duc Thang University
Honorary Editor-in-Chief
Tran Trong Dao
Executive Editor
Thai Hoang Chien
Chairman of the Editorial Board
Václav Snášel
Vice Chairman of the Editorial Board
Ivan Zelinka
Managing Editor
Nguyen-Thanh Nhon
Editorial Board
Timon Rabczuk
Hari Mohan Srivastava
Seung-Bok Choi
Carlo Cattani
Phan Thien Nhan
Nguyen Minh Tho
Adel M. Alimi
Petr Musílek
Jaroslav Pokorný
Juan Velasquez
Michal Wozniak
Hana Řezanková
Hendrik Richter
Mohammed Chadli
Nikolay V Kuznetsov
Shobhit K. Patel
Miroslav Vozňák
Roman Senkerik
Juan Carlos Burguillo Rial
Akhil Garg
Nguyen Pham Trung Hieu
Nguyen Quoc Hung
Aleš Zamuda
Ngo Son Tung
User

Guide for Authors

  • View 'Guide for Authors' online

Submit Your Paper

In order to submit your paper, please login and navigate to the author page.

If you do not have an account, please consider registering one.

Track Your Paper

Track accepted paper
Once your article has been accepted you will receive an email from Author Services. This email contains a link to check the status of your articles.

Click here to track your accepted papers
Journal Content

Browse
  • By Issue
  • By Author
  • By Title

Abstracting/Indexing 

  • Home
  • Vol 8, No 4 (2024)
  • Nguyen

Influential Features Analysis And AI-Driven Accuracy Enhancement: A Study Case For DDoS Detection

Le Ba Nguyen, Quoc-Binh Nguyen, Ngoc Hong Tran

Abstract


Cybersecurity is known today as one of the greatest challenges of the modern era. Among the various types of cyber attacks that threaten our security, the Distributed Denial of Service (DDoS) attack is among some of the most common, effective, and well-recognized attack strategies. Since this form of attack is meant to disrupt the availability factor covertly, it can be detrimental to the targeted machines and difficult to be discovered. Because of that, there have been a number of approaches, as well as solutions that have been devised in order to detect it as accurately and efficiently as possible. Impressively, data mining methods have been employed to identify patterns of DDoS attacks from the computer network traffic. Nevertheless, the recent works’ results have not yet mentioned which factors of the computer network traffic play the most vital role in indicating the potential for true positive attacks. Additionally, with the Machine Learning approach, there are still ample opportunities to enhance the attack prediction accuracy of the detection model. As such, in this paper, we attempt to explore factors that would influence the classification result, and leverage a variety of Machine Learning algorithms, i.e. Random Forest, Naive Bayes, Logistic Regression, and Multilayer Perceptron, for the purpose of improving the accuracy of data classification. The experiments were deployed using CICIDS2017 dataset and compared with the other related works on the same dataset. The experimental outcomes of our methodologies and analyses demonstrate some potential and effectiveness enhancement compared to previous works. Moreover, we analysized and concluded the insight of how side factors affect the attack identification result. The collected information from our analysis identifies dominant factors, and opens a new view for their hidden correlationship directly affecting the attack labeling.


Keywords


Distributed Denial of Service; Random Forest; Naive Bayes; Logistic Regression; Multilayer Perceptron

Full Text:

PDF

Time cited: 0

Download citation



DOI: http://dx.doi.org/10.55579/jaec.202484.466

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Journal of Advanced Engineering and Computation

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


Owner: Ton Duc Thang University. All rights reserved.
License No: 507/GP-BTTTT, issued: 18th November 2016
Contact address: 19, Nguyen Huu Tho Street, Tan Phong Ward, District 7, Ho Chi Minh City
Tel: +84-28 3775 5037  Fax: +84-28 3775 5055
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.