APSIPA Transactions on Signal and Information Processing > Vol 12 > Issue 2

A Real-Time DDoS Attack Detection and Classification System Using Hierarchical Temporal Memory

Yu-Kuen Lai, Department of Electrical Engineering, Chung-Yuan Christian University, Taiwan, lai@cnsrl.cycu.edu.tw , Manh-Hung Nguyen, Department of Electrical Engineering, Chung-Yuan Christian University, Taiwan
 
Suggested Citation
Yu-Kuen Lai and Manh-Hung Nguyen (2023), "A Real-Time DDoS Attack Detection and Classification System Using Hierarchical Temporal Memory", APSIPA Transactions on Signal and Information Processing: Vol. 12: No. 2, e8. http://dx.doi.org/10.1561/116.00000147

Publication Date: 03 Apr 2023
© 2023 Y. K. Lai and M. H. Nguyen
 
Subjects
 
Keywords
Multiclassificationhierarchical temporal memoryDDoS attackincremental learningentropy
 

Share

Open Access

This is published under the terms of CC BY-NC.

Downloaded: 429 times

In this article:
Introduction 
Background 
Related Studies 
System Architecture 
Experiment and Evaluation 
Conclusions and Future Works 
References 

Abstract

This paper presents a system implementation to detect and classify different DDoS attacks. The system adopts features of inter-arrival time, entropy, and packet length distribution for a hybrid machine learning model, which is based on the hierarchical temporal memory (HTM) with a k-nearest neighbors (KNN) classifier that can mine network traffic anomalies. Furthermore, it can incrementally learn new traffic behavior and recognize new types of attacks. Finally, system evaluation is conducted based on the CICDDoS 2019 dataset. Thus, the proposed system can successfully identify different attacks with high detection rate, accuracy, and precision.

DOI:10.1561/116.00000147

Companion

APSIPA Transactions on Signal and Information Processing Special Issue - Learning, Security, AIoT for Emerging Communication/Networking Systems
See the other articles that are part of this special issue.